{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T19:14:55Z","timestamp":1787166895390,"version":"build-2736575974"},"reference-count":44,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T00:00:00Z","timestamp":1671580800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Stroke"],"abstract":"\n Background<\/jats:title>\n An imbalanced gut microbial community, or dysbiosis, has been shown to occur following stroke. It is possible that this dysbiosis negatively impacts stroke recovery and rehabilitation. Species level resolution measurements of the gut microbiome following stroke are needed to develop and test precision interventions such as probiotic or fecal microbiota transplant therapies that target the gut microbiome. Previous studies have used 16S rRNA amplicon sequencing in young male mice to obtain broad profiling of the gut microbiome at the genus level following stroke, but further investigations will be needed with whole genome shotgun sequencing in aged rats of both sexes to obtain species level resolution in a model which will better translate to the demographics of human stroke patients.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n Thirty-nine aged male and female rats underwent middle cerebral artery occlusion. Fecal samples were collected before stroke and 3 days post stroke to measure gut microbiome. Machine learning was used to identify the top ranked bacteria which were changed following stroke. MRI imaging was used to obtain infarct and edema size and cerebral blood flow (CBF). ELISA was used to obtain inflammatory markers.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Dysbiosis was demonstrated by an increase in pathogenic bacteria such as\n Butyricimonas virosa<\/jats:italic>\n (15.52 fold change,\n p<\/jats:italic>\n &lt; 0.0001),\n Bacteroides vulgatus<\/jats:italic>\n (7.36 fold change,\n p<\/jats:italic>\n &lt; 0.0001), and\n Escherichia coli<\/jats:italic>\n (47.67 fold change,\n p<\/jats:italic>\n &lt; 0.0001). These bacteria were positively associated with infarct and edema size and with the inflammatory markers Ccl19, Ccl24, IL17a, IL3, and complement C5; they were negatively correlated with CBF. Conversely, beneficial bacteria such as\n Ruminococcus flavefaciens<\/jats:italic>\n (0.14 fold change,\n p<\/jats:italic>\n &lt; 0.0001),\n Akkermansia muciniphila<\/jats:italic>\n (0.78 fold change,\n p<\/jats:italic>\n &lt; 0.0001), and\n Lactobacillus murinus<\/jats:italic>\n (0.40 fold change,\n p<\/jats:italic>\n &lt; 0.0001) were decreased following stroke and associated with all the previous parameters in the opposite direction of the pathogenic species. There were not significant microbiome differences between the sexes.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n \n The species level resolution measurements found here can be used as a foundation to develop and test precision interventions targeting the gut microbiome following stroke. Probiotics that include\n Ruminococcus flavefaciens, Akkermansia muciniphila, and Lactobacillus murinus<\/jats:italic>\n should be developed to target the deficit following stroke to measure the impact on stroke severity.\n <\/jats:p>\n <\/jats:sec>","DOI":"10.3389\/fstro.2022.1026066","type":"journal-article","created":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T04:17:15Z","timestamp":1671596235000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Gut microbial dysbiosis correlates with stroke severity markers in aged rats"],"prefix":"10.3389","volume":"1","author":[{"given":"Tyler C.","family":"Hammond","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah","family":"Messmer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacqueline A.","family":"Frank","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Doug","family":"Lukins","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rita","family":"Colwell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ai-Ling","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keith R.","family":"Pennypacker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,12,21]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1186\/s13293-021-00378-z","article-title":"Sex differences in the intestinal microbiome: interactions with risk factors for atherosclerosis and cardiovascular disease","volume":"12","author":"Ahmed","year":"2021","journal-title":"Biol. 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