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- 2019
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Tidskriftsartikel (refereegranskat)
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| 2. |
- Gu, Peng, et al.
(författare)
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A metabolite from commensal Candida albicans enhances the bactericidal activity of macrophages and protects against sepsis
- 2023
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Ingår i: Cellular & Molecular Immunology. - London : Nature Publishing Group. - 1672-7681 .- 2042-0226. ; 20:10, s. 1156-1170
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Tidskriftsartikel (refereegranskat)abstract
- The gut microbiome is recognized as a key modulator of sepsis development. However, the contribution of the gut mycobiome to sepsis development is still not fully understood. Here, we demonstrated that the level of Candida albicans was markedly decreased in patients with bacterial sepsis, and the supernatant of Candida albicans culture significantly decreased the bacterial load and improved sepsis symptoms in both cecum ligation and puncture (CLP)-challenged mice and Escherichia coli-challenged pigs. Integrative metabolomics and the genetic engineering of fungi revealed that Candida albicans-derived phenylpyruvate (PPA) enhanced the bactericidal activity of macrophages and reduced organ damage during sepsis. Mechanistically, PPA directly binds to sirtuin 2 (SIRT2) and increases reactive oxygen species (ROS) production for eventual bacterial clearance. Importantly, PPA enhanced the bacterial clearance capacity of macrophages in sepsis patients and was inversely correlated with the severity of sepsis in patients. Our findings highlight the crucial contribution of commensal fungi to bacterial disease modulation and expand our understanding of the host-mycobiome interaction during sepsis development. © 2023, The Author(s), under exclusive licence to CSI and USTC.
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| 3. |
- Bäckhed, Fredrik, 1973, et al.
(författare)
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Dynamics and Stabilization of the Human Gut Microbiome during the First Year of Life
- 2015
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Ingår i: Cell Host & Microbe. - Cambridge : Elsevier BV. - 1931-3128 .- 1934-6069. ; 17:5, s. 690-703
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Tidskriftsartikel (refereegranskat)abstract
- The gut microbiota is central to human health, but its establishment in early life has not been quantitatively and functionally examined. Applying metagenomic analysis on fecal samples from a large cohort of Swedish infants and their mothers, we characterized the gut microbiome during the first year of life and assessed the impact of mode of delivery and feeding on its establishment. In contrast to vaginally delivered infants, the gut microbiota of infants delivered by C-section showed significantly less resemblance to their mothers. Nutrition had a major impact on early microbiota composition and function, with cessation of breast-feeding, rather than introduction of solid food, being required for maturation into an adult-like microbiota. Microbiota composition and ecological network had distinctive features at each sampled stage, in accordance with functional maturation of the microbiome. Our findings establish a framework for understanding the interplay between the gut microbiome and the human body in early life.
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| 4. |
- Han, Ridong, et al.
(författare)
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Document-level Relation Extraction with Relation Correlations
- 2024
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Ingår i: Neural Networks. - Oxford : Elsevier. - 0893-6080 .- 1879-2782. ; 171, s. 14-24
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Tidskriftsartikel (refereegranskat)abstract
- Document-level relation extraction faces two often overlooked challenges: long-tail problem and multi-label problem. Previous work focuses mainly on obtaining better contextual representations for entity pairs, hardly address the above challenges. In this paper, we analyze the co-occurrence correlation of relations, and introduce it into the document-level relation extraction task for the first time. We argue that the correlations can not only transfer knowledge between data-rich relations and data-scarce ones to assist in the training of long-tailed relations, but also reflect semantic distance guiding the classifier to identify semantically close relations for multi-label entity pairs. Specifically, we use relation embedding as a medium, and propose two co-occurrence prediction sub-tasks from both coarse- and fine-grained perspectives to capture relation correlations. Finally, the learned correlation-aware embeddings are used to guide the extraction of relational facts. Substantial experiments on two popular datasets (i.e., DocRED and DWIE) are conducted, and our method achieves superior results compared to baselines. Insightful analysis also demonstrates the potential of relation correlations to address the above challenges. The data and code are released at https://github.com/RidongHan/DocRE-Co-Occur. © 2023 Elsevier Ltd
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