Cold Exposure and Oral Delivery of GLP-1R Agonists by an Engineered Probiotic Yeast Strain Have Antiobesity Effects in Mice.

Advanced microbiome therapeutics (AMTs) holds promise in utilizing engineered microbes such as bacteria or yeasts for innovative therapeutic applications, including the in situ delivery of therapeutic peptides. Glucagon-like peptide-1 receptor agonists, such as Exendin-4, have emerged as potential treatments for type 2 diabetes and obesity. However, current administration methods face challenges with patient adherence and low oral bioavailability. To address these limitations, researchers are exploring improved oral delivery methods for Exendin-4, including utilizing AMTs. This study engineered the probiotic yeast Saccharomyces boulardii to produce Exendin-4 (Sb-Exe4) in the gastrointestinal tract of male C57BL/6 mice to combat diet-induced obesity. The biological efficiency of Exendin-4 secreted by S. boulardii was analyzed ex vivo on isolated pancreatic islets, demonstrating induced insulin secretion. The in vivo characterization of Sb-Exe4 revealed that when combined with cold exposure (8 °C), the Sb-Exe4 yeast strain successfully suppressed appetite by 25% and promoted a 4-fold higher weight loss. This proof of concept highlights the potential of AMTs to genetically modify S. boulardii for delivering active therapeutic peptides in a precise and targeted manner. Although challenges in efficacy and regulatory approval persist, AMTs may provide a transformative platform for personalized medicine. Further research in AMTs, particularly focusing on probiotic yeasts such as S. boulardii, holds great potential for novel therapeutic possibilities and enhancing treatment outcomes in diverse metabolic disorders.

Engineered E. coli Nissle 1917 for delivery of bioactive IL-2 for cancer immunotherapy.

In this study we performed a step-wise optimization of biologically active IL-2 for delivery using E. coli Nissle 1917. Engineering of the strain was coupled with an in vitro cell assay to measure the biological activity of microbially produced IL-2 (mi-IL2). Next, we assessed the immune modulatory potential of mi-IL2 using a 3D tumor spheroid model demonstrating a strong effect on immune cell activation. Finally, we evaluated the anticancer properties of the engineered strain in a murine CT26 tumor model. The engineered strain was injected intravenously and selectively colonized tumors. The treatment was well-tolerated, and tumors of treated mice showed a modest reduction in tumor growth rate, as well as significantly elevated levels of IL-2 in the tumor. This work demonstrates a workflow for researchers interested in engineering E. coli Nissle for a new class of microbial therapy against cancer.

Candida expansion in the gut of lung cancer patients associates with an ecological signature that supports growth under dysbiotic conditions.

Candida species overgrowth in the human gut is considered a prerequisite for invasive candidiasis, but our understanding of gut bacteria promoting or restricting this overgrowth is still limited. By integrating cross-sectional mycobiome and shotgun metagenomics data from the stool of 75 male and female cancer patients at risk but without systemic candidiasis, bacterial communities in high Candida samples display higher metabolic flexibility yet lower contributional diversity than those in low Candida samples. We develop machine learning models that use only bacterial taxa or functional relative abundances to predict the levels of Candida genus and species in an external validation cohort with an AUC of 78.6-81.1%. We propose a mechanism for intestinal Candida overgrowth based on an increase in lactate-producing bacteria, which coincides with a decrease in bacteria that regulate short chain fatty acid and oxygen levels. Under these conditions, the ability of Candida to harness lactate as a nutrient source may enable Candida to outcompete other fungi in the gut.

Predictable modulation of cancer treatment outcomes by the gut microbiota.

The gut microbiota has the potential to influence the efficacy of cancer therapy. Here, we investigated the contribution of the intestinal microbiome on treatment outcomes in a heterogeneous cohort that included multiple cancer types to identify microbes with a global impact on immune response. Human gut metagenomic analysis revealed that responder patients had significantly higher microbial diversity and different microbiota compositions compared to non-responders. A machine-learning model was developed and validated in an independent cohort to predict treatment outcomes based on gut microbiota composition and functional repertoires of responders and non-responders. Specific species, Bacteroides ovatus and Bacteroides xylanisolvens, were positively correlated with treatment outcomes. Oral gavage of these responder bacteria significantly increased the efficacy of erlotinib and induced the expression of CXCL9 and IFN-γ in a murine lung cancer model. These data suggest a predictable impact of specific constituents of the microbiota on tumor growth and cancer treatment outcomes with implications for both prognosis and therapy.