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van der Sloot, A.

Publications and source records attributed to van der Sloot, A..

2 recordsLinked to original sources

Engineered spermidine-secreting Saccharomyces boulardii ameliorate colitis and colon cancer in mice

Experimental studies suggest that the probiotic yeast Saccharomyces boulardii can mitigate the symptoms of inflammatory bowel disease. However, these results are equivocal and S boulardii probiotic therapy has not gained widespread acceptance in clinical practice. To assess whether the therapeutic properties of S boulardii might be improved upon, we engineered S boulardii to overproduce and secrete spermidine, a pro-regenerative natural metabolite. We employed CRISPR gene deletion and transposon-mediated gene integration to manipulate expression of key enzymes in the polyamine synthetic and transport pathways. We tested the engineered yeast by oral gavage of mice treated with azoxymethane and dextran sulfate sodium to induce chronic colitis and colon cancer. We demonstrate that oral delivery of spermidine-secreting S boulardii in mice populates the gastrointestinal tract with viable spermidine-secreting S boulardii cells and raises free spermidine levels in the gastrointestinal tract. Strikingly, spermidine-secreting S boulardii strains were significantly more effective than wild-type S boulardii in reducing dextran sulfate sodium-induced colitis as well as colitis-associated colon cancer in mice. These results suggest that in situ spermidine secretion by engineered synthetic biotic yeast strains may be an effective and low-cost therapy to mitigate inflammatory bowel disease and colon cancer.

cancer biology↗

Protein Language Models: Is Scaling Necessary?

Public protein sequence databases contain samples from the fitness landscape explored by nature. Protein language models (pLMs) pre-trained on these sequences aim to capture this landscape for tasks like property prediction and protein design. Following the same trend as in natural language processing, pLMs have continuously been scaled up. However, the premise that scale leads to better performance assumes that source databases provide an accurate representation of the underlying fitness landscape, which is likely false. By developing an efficient codebase, designing a modern architecture, and addressing data quality concerns such as sample bias, we introduce AMPLIFY, a best-in-class pLM that is orders of magnitude less expensive to train and deploy than previous models. Furthermore, to support the scientific community and democratize the training of pLMs, we have open-sourced AMPLIFYs pre-training codebase, data, and model checkpoints.

molecular biology↗