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      The surprising link between gut bacteria and devastating eye diseases

      Nature
      Springer Science and Business Media LLC

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          Is Open Access

          Identification of an intraocular microbiota

          The current dogma in ophthalmology and vision research presumes the intraocular environment to be sterile. However, recent evidence of intestinal bacterial translocation into the bloodstream and many other internal organs including the eyes, found in healthy and diseased animal models, suggests that the intraocular cavity may also be inhabited by a microbial community. Here, we tested intraocular samples from over 1000 human eyes. Using quantitative PCR, negative staining transmission electron microscopy, direct culture, and high-throughput sequencing technologies, we demonstrated the presence of intraocular bacteria. The possibility that the microbiome from these low-biomass communities could be a contamination from other tissues and reagents was carefully evaluated and excluded. We also provide preliminary evidence that a disease-specific microbial signature characterized the intraocular environment of patients with age-related macular degeneration and glaucoma, suggesting that either spontaneous or pathogenic bacterial translocation may be associated with these common sight-threatening conditions. Furthermore, we revealed the presence of an intraocular microbiome in normal eyes from non-human mammals and demonstrated that this varied across species (rat, rabbit, pig, and macaque) and was established after birth. These findings represent the first-ever evidence of intraocular microbiota in humans.
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            CRB1-associated retinal degeneration is dependent on bacterial translocation from the gut

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              Author and article information

              Journal
              Nature
              Nature
              Springer Science and Business Media LLC
              0028-0836
              1476-4687
              February 26 2024
              Article
              10.1038/d41586-024-00562-2
              ae4fb358-095d-412d-a06e-bced84e2b9a8
              © 2024

              https://www.springernature.com/gp/researchers/text-and-data-mining

              https://www.springernature.com/gp/researchers/text-and-data-mining

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