Immune checkpoint blockade (ICB) has revolutionized cancer treatment, yet quality of life and continuation of therapy can be constrained by immune-related adverse events (irAEs). Limited understanding of irAE mechanisms hampers development of approaches to mitigate their damage. To address this, we examined whether mice gained sensitivity to anti-CTLA-4 (αCTLA-4)–mediated toxicity upon disruption of gut homeostatic immunity. We found αCTLA-4 drove increased inflammation and colonic tissue damage in mice with genetic predisposition to intestinal inflammation, acute gastrointestinal infection, transplantation with a dysbiotic fecal microbiome, or dextran sodium sulfate administration. We identified an immune signature of αCTLA-4–mediated irAEs, including colonic neutrophil accumulation and systemic interleukin-6 (IL-6) release. IL-6 blockade combined with antibiotic treatment reduced intestinal damage and improved αCTLA-4 therapeutic efficacy in inflammation-prone mice. Intestinal immune signatures were validated in biopsies from patients with ICB colitis. Our work provides new preclinical models of αCTLA-4 intestinal irAEs, mechanistic insights into irAE development, and potential approaches to enhance ICB efficacy while mitigating irAEs.
Summary
Expectations of machine learning (ML) are high for discovering new patterns in high-throughput biological data, but most such practices are accustomed to relying on existing knowledge conditions to design experiments. Investigations of the power and limitation of ML in revealing complex patterns from data without the guide of existing knowledge have been lacking. In this study, we conducted systematic experiments on such
ab initio
knowledge discovery with ML methods on single-cell RNA-sequencing data of early embryonic development. Results showed that a strategy combining unsupervised and supervised ML can reveal major cell lineages with minimum involvement of prior knowledge or manual intervention, and the
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mining enabled a new discovery of human early embryonic cell differentiation. The study illustrated the feasibility, significance, and limitation of
ab initio
ML knowledge discovery on complex biological problems.
Background: Metagenomic sequencing is a complex sampling procedure from unknown mixtures of many genomes. Having metagenome data with known genome compositions is essential for both benchmarking bioinformatics software and for investigating influences of various factors on the data. Compared to data from real microbiome samples or from defined microbial mock community, simulated data with proper computational models are better for the purpose as they provide more flexibility for controlling multiple factors. Methods: We developed a non-uniform metagenomic sequencing simulation system (nuMetaSim) that is capable of mimicking various factors in real metagenomic sequencing to reflect multiple properties of real data with customizable parameter settings. Results: We generated 9 comprehensive metagenomic datasets with different composition complexity from of 203 bacterial genomes and 2 archaeal genomes related with human intestine system. Conclusion: The data can serve as benchmarks for comparing performance of different methods at different situations, and the software package allows users to generate simulation data that can better reflect the specific properties in their scenarios.
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