🔍 AI agent screened nearly 2,000 genes and found new pathways against gut disease

• Stanford researchers have built Eubiota, an open-source AI agent system that autonomously drives scientific investigations of the gut microbiome and delivered lab-validated results across four case studies.
• The system screened 1,945 bacterial genes by analyzing over 10,000 scientific papers in a matter of hours and identified a DNA repair mechanism that helps gut bacteria survive inflammatory stress.
• In a blinded evaluation, domain experts preferred Eubiota's responses over GPT-5.1 in 70.6 percent of cases.

Four agents working together

Eubiota was developed by researchers at Stanford University and Chan Zuckerberg Biohub in San Francisco. The system consists of four specialized AI agents that share a common memory: a planner that sets the strategy, an executor that runs tools and database searches, a verifier that checks whether the evidence holds up, and a generator that compiles the results with source references.

Unlike many other AI systems for research, Eubiota is built on an open model with eight billion parameters (Qwen3-8B) optimized with reinforcement learning. This strategy allows the system to learn from the entire reasoning chain rather than individual steps. Across six different benchmarks, Eubiota achieved 87.7 percent average accuracy, compared with 77.3 percent for GPT-5.1 and 67.2 percent for the base model Qwen3-8B without the agent framework.

Genes that protect bacteria during inflammation

In the first case study, Eubiota was tasked with identifying which genes help gut bacteria survive the chemical stress that occurs during intestinal inflammation. The body produces nitric oxide as part of its immune defense, and this gas damages bacterial DNA.

Eubiota screened 1,945 candidate genes across 118 whole-genome-sequenced gut bacterial strains. The system analyzed 10,867 scientific papers across five independent runs and identified genes including uvrA and ruvA, both linked to DNA repair. Five of the ten top-ranked genes showed clear growth defects under nitric oxide exposure in the laboratory (P < 0.05). The same genes were also overrepresented in gut samples from patients with inflammatory bowel disease (IBD) in two independent datasets.

A bacterial cocktail that reduces colitis in mice

Eubiota was then tasked with designing a synthetic bacterial group to treat intestinal inflammation. The system selected four strains: Blautia hansenii, Bifidobacterium breve, Faecalibacterium prausnitzii, and Roseburia intestinalis. The selection was based on these species collectively contributing butyrate production, mucosal barrier protection, and immunomodulatory properties.

The researchers tested this group in a mouse model of colitis. The mice that received the bacterial treatment had lower weight loss, lower disease activity index scores, and lower levels of the inflammation marker lipocalin-2 compared with the control group. Colon length, a measure of inflammatory damage, was also better preserved.

Antibiotics that spare beneficial bacteria

In a third case study, Eubiota designed an antibiotic cocktail consisting of piperacillin, tazobactam, and amikacin at specific concentrations tailored to eliminate three pathogenic bacteria (Pseudomonas aeruginosa, Citrobacter portucalensis, and Klebsiella pneumoniae) while preserving beneficial gut bacteria such as Bacteroides and Bifidobacterium. Laboratory tests under anaerobic conditions showed that the cocktail inhibited the disease-causing bacteria while commensal species were partially preserved.

Anti-inflammatory compounds from a dietary study

Finally, Eubiota was asked to analyze 397 metabolites that were elevated in participants eating a vegan diet in the Twins Nutrition Study, a controlled trial with identical twins at Stanford. The system ranked the metabolites by likely anti-inflammatory effect and identified five top candidates. All five reduced NF-κB signaling in immune cells in laboratory tests (P < 0.05). Among them, 3-methoxycatechol showed a dose response at concentrations between 10 and 50 micromoles.

38 times faster than manual review

A comparison showed that Eubiota on average analyzed 920 unique scientific papers per run in approximately 1.2 hours using two GPUs. The equivalent manual review was estimated at 46 hours, representing a 38-fold increase in speed. The system's code and training data are available under an open license, and an interactive web platform is accessible at eubiota.ai.

Found via BAIO.

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