¿Cómo cultivar microorganismos no cultivables?

Many microorganisms are "unculturable," or at least not able to grow in known media. Now, a new tool enables researchers to predict what nutrients organisms need to thrive in the lab, eliminating most of the guesswork involved in setting up new cultures. Read more...

Studying microbes has provided unparalleled insights into the molecular mechanisms governing cell functions, but expanding on such studies with new microbes depends on finding the right media for culturing the bacteria. Now, Matthew Oberhardt, a postdoctoral fellow at the Center for Bioinformatics and Computational Biology at the University of Maryland, and his colleagues present a new way to approach these “unculturables” in the journal Nature Communications.

“We had a very simple question in mind. Could we try to predict the minimal media needed to actually grow organisms?” he said. To find the answer, Oberhardt and his fellow researchers turned to the Leibniz Institute German Collection of Microorganisms and Cell Cultures, which hosts a repository of around 1300 media recipes for 23,000 microbes. “We thought that we might be able to extract a lot of information and create a new paradigm where we could do some predictive modeling.”
The team combed through recipe files and extracted details such as ingredient lists and salt concentrations to create a Known Media Database (KOMODO) that includes more than 18,000 strain-media combinations as well as more than 3000 media variants and compound concentrations.
Oberhardt and his colleagues then leveraged the database to predict which organisms would grow well in which media. By looking at media that support multiple microbes and applying the transitive property and a phylogeny-based filter, they were able to predict which microbes would grow in new in vitro experiments with approximately 83% accuracy.
“No one has really looked at this problem this way before, and we’ve been able to set a new framework,” Oberhardt said. “We’re not done yet. We’re going to improve the database by including more biogenetic data and ecological data. But we think this is a really good starting point, and we can use more sophisticated machine learning methods to help us create good metabolic models in the future.”

Reference
Oberhardt MA, Zarecki R, Gronow S, Lang E, Klenk HP, Gophna U, Ruppin E. Harnessing the landscape of microbial culture media to predict new organism-media pairings. Nat Commun. 2015 Oct 13;6:8493. doi: 10.1038/ncomms9493.  

Novel antibody–antibiotic conjugate eliminates intracellular S. aureus

Staphylococcus aureus is considered to be an extracellular pathogen. However, survival of S. aureus within host cells may provide a reservoir relatively protected from antibiotics, thus enabling long-term colonization of the host and explaining clinical failures and relapses after antibiotic therapy. Here we confirm that intracellular reservoirs of S. aureus in mice comprise a virulent subset of bacteria that can establish infection even in the presence of vancomycin, and we introduce a novel therapeutic that effectively kills intracellular S. aureus. This antibody–antibiotic conjugate consists of an anti-S. aureus antibody conjugated to a highly efficacious antibiotic that is activated only after it is released in the proteolytic environment of the phagolysosome. The antibody–antibiotic conjugate is superior to vancomycin for treatment of bacteraemia and provides direct evidence that intracellular S. aureus represents an important component of invasive infections.



a, Model of AAC (not drawn to scale). b, Mechanism of AAC action. c, Binding of Alexa-488 anti-β-GlcNAC WTA monoclonal antibody (mAb) or anti-α-GlcNAC WTA monoclonal antibody, or isotype control antibody, anti-cytomegalovirus glycoprotein-D (gD) to USA300 isolated from infected kidneys (n = 3). MFI, mean fluorescence intensity. d, Binding of anti-GlcNAC WTA antibodies (red) or isotype control (grey) to protein-A-deficient USA300 lacking tarM or tarS (n = 3). WT, wild type. e, Crystal structure of anti-β-GlcNAc WTA Fab bound to a synthetic minimal β-WTA unit. Antibody light chain (pink) and heavy chain (blue) are shown. f, MIC determination for rifampicin and rifalogue on USA300 (n = 5). g, Survival of stationary phase USA300 incubated with 1 × 10−6 M rifampicin or rifalogue (n = 4). h, USA300 bacteria were incubated without antibiotic (black) or with 3 μg ml−1 ciprofloxacin (Cipro; green, red and grey). 1 μg ml−1 of rifalogue (red) or rifampicin (grey) was added as indicated (n = 3). i, Intact AAC does not kill planktonic bacteria but does after pre-treatment with cathepsin-B (n = 3). g–i, Error bars show s.d. for triplicate samples (n = biological repeats).

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