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Cited by 119Gut microbiota and healthSpinal Cord Injury ResearchGastrointestinal motility and disorders

The DOI matches its Crossref record; 72 of the 73 references it cites were found in a registry or an index.

Fecal transplant prevents gut dysbiosis and anxiety-like behaviour after spinal cord injury in rats

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Schmidt, Emma K. A., Torres-Espin, Abel, Raposo, Pamela J. F., Madsen, Karen L., Kigerl, Kristina A., Popovich, Phillip G., Fenrich, Keith K., Fouad, Karim·2020·PLoS ONE
DOI 10.1371/journal.pone.0226128PMC PMC6961833
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Abstract

Secondary manifestations of spinal cord injury beyond motor and sensory dysfunction can negatively affect a person's quality of life. Spinal cord injury is associated with an increased incidence of depression and anxiety; however, the mechanisms of this relationship are currently not well understood. Human and animal studies suggest that changes in the composition of the intestinal microbiota (dysbiosis) are associated with mood disorders. The objective of the current study is to establish a model of anxiety following a cervical contusion spinal cord injury in rats and to determine whether the microbiota play a role in the observed behavioural changes. We found that spinal cord injury caused dysbiosis and increased symptoms of anxiety-like behaviour. Treatment with a fecal transplant prevented both spinal cord injury-induced dysbiosis as well as the development of anxiety-like behaviour. These results indicate that an incomplete unilateral cervical spinal cord injury can cause affective disorders and intestinal dysbiosis, and that both can be prevented by treatment with fecal transplant therapy.

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Fig 1 Cervical spinal cord injury in rats induces anxiety-like behaviour and alterations in gut microbiota. The elevated plus maze, shown in (A), was tested 3 weeks following spinal cord injury (SCI). (B) There was no difference in the total distance travelled between SCI and sham operated groups. Sham = 4869 px ± 1579, SCI = 4536 px ± 1713. (C) After SCI, rats spent significantly less time in the open arms compared to sham animals (Mann-Whitney test, p = 0.0043; Sham = 13.37% ± 8.26, SCI = 0.50% ± 1.23) (D) Fecal matter from SCI rats were collected at various time points, filtered and diluted with PBS using sterile techniques, and plated onto CHROMagar orientation plates to assess bacterial growth over 48 hours. Immediately following injury (time 0) there was clear alterations in microbial growth, with maximal changes seen 3 days after injury. **p<0.01.
Fig 1 Cervical spinal cord injury in rats induces anxiety-like behaviour and alterations in gut microbiota. The elevated plus maze, shown in (A), was tested 3 weeks following spinal cord injury (SCI). (B) There was no difference in the total distance travelled between SCI and sham operated groups. Sham = 4869 px ± 1579, SCI = 4536 px ± 1713. (C) After SCI, rats spent significantly less time in the open arms compared to sham animals (Mann-Whitney test, p = 0.0043; Sham = 13.37% ± 8.26, SCI = 0.50% ± 1.23) (D) Fecal matter from SCI rats were collected at various time points, filtered and diluted with PBS using sterile techniques, and plated onto CHROMagar orientation plates to assess bacterial growth over 48 hours. Immediately following injury (time 0) there was clear alterations in microbial growth, with maximal changes seen 3 days after injury. **p<0.01.CC BY · PubMed Central
Fig 2 Experimental Timeline. Upon arrival, rats were allowed one week to acclimate to their environment before testing. Prior to injury, baseline measures were obtained in the cylinder test, open field, light-dark box and sucrose preference tests. With the exception of the healthy group, all rats were gavaged with a fecal slurry (FMT: fecal microbiota transplant) or control solution at the time of injury and for 2 days following injury or sham surgery. Stool samples were collected for 16s rRNA sequencing prior to, 3 days and 4 weeks following surgeries. After surgeries, rats were tested weekly on a battery of behavioural tests followed by perfusions (5 weeks following surgeries) and tissue analysis.
Fig 2 Experimental Timeline. Upon arrival, rats were allowed one week to acclimate to their environment before testing. Prior to injury, baseline measures were obtained in the cylinder test, open field, light-dark box and sucrose preference tests. With the exception of the healthy group, all rats were gavaged with a fecal slurry (FMT: fecal microbiota transplant) or control solution at the time of injury and for 2 days following injury or sham surgery. Stool samples were collected for 16s rRNA sequencing prior to, 3 days and 4 weeks following surgeries. After surgeries, rats were tested weekly on a battery of behavioural tests followed by perfusions (5 weeks following surgeries) and tissue analysis.CC BY · PubMed Central
Fig 3 Treatment with a fecal microbiota transplant following spinal cord injury reduced anxiety-like behaviour. Animals were tested in the elevated plus maze 3 weeks following surgeries. (A) Representative images from the motion tracking software used to analyze the elevated plus maze. (B) Healthy animals travelled significantly further than the SCI group (one-way ANOVA; p = 0.0014) and sham group (p = 0.0469). Units of distance measured are expressed in pixels (Sham = 6964 px ± 1456, SCI = 6177 px ± 1456, SCI-FMT = 7146 px ± 1199, Healthy = 8500 px ± 1070). (C) Tukey’s multiple comparisons revealed that SCI rats spent significantly less time in the open arms compared to FMT treated and Healthy groups (one-way ANOVA; SCI vs. SCI-FMT p = 0.0119; SCI vs. Healthy p = 0.0027; Sham = 6.68% ± 4.97, SCI = 1.10% ± 1.04, SCI-FMT = 9.84% ± 8.84, Healthy = 12.11% ± 7.15). (D) Similarly, SCI rats displayed a reduced percentage of open arm entries compared to healthy rats (one-way ANOVA; p = 0.0267; SCI vs. Healthy p = 0.027. Sham = 19.42% ± 9.91, SCI = 9.5% ± 9.58, SCI-FMT = 19.71% ± 11.79, Healthy = 22.94% ± 8.67). Although not significant, SCI rats also displayed a reduced percentage of open arm entries compared to sham (p = 0.1352) and SCI-FMT rats (p = 0.0909). (E) 4 weeks after injury, rats were tested in the light-dark box with increased time and reduced latency to enter the light component indicating decreased anxiety-like behaviour. FMT treated animals spent significantly more time in the light component compared to the SCI group as well as the Healthy group (Two-way repeated measures ANOVA; SCI vs. SCI-FMT p = 0.01, SCI-FMT vs. Healthy p = 0.03. Sham = 37.03s ± 38.97, SCI = 20.3s ± 13.88, SCI-FMT = 74.77s ± 44.25, Healthy = 34.17s ± 31.63; baseline and 1 week post-injury values are shown in S1 Fig.). (F) FMT rats displayed a reduced latency to enter the light compartment compared to the SCI group, although this failed to reach significance (two-way repeated measure ANOVA; p = 0.0843; Sham = 4.09min ± 4.05, SCI = 5.79min ± 4.30, SCI-FMT = 1.04min ±, Healthy = 3.95min ± 4.31). (G) There were no differences between groups in the percentage of sucrose water consumed at any time point tested. Error bars indicate standard error mean. *p<0.05, **p<0.01, ***p<0.001.
Fig 3 Treatment with a fecal microbiota transplant following spinal cord injury reduced anxiety-like behaviour. Animals were tested in the elevated plus maze 3 weeks following surgeries. (A) Representative images from the motion tracking software used to analyze the elevated plus maze. (B) Healthy animals travelled significantly further than the SCI group (one-way ANOVA; p = 0.0014) and sham group (p = 0.0469). Units of distance measured are expressed in pixels (Sham = 6964 px ± 1456, SCI = 6177 px ± 1456, SCI-FMT = 7146 px ± 1199, Healthy = 8500 px ± 1070). (C) Tukey’s multiple comparisons revealed that SCI rats spent significantly less time in the open arms compared to FMT treated and Healthy groups (one-way ANOVA; SCI vs. SCI-FMT p = 0.0119; SCI vs. Healthy p = 0.0027; Sham = 6.68% ± 4.97, SCI = 1.10% ± 1.04, SCI-FMT = 9.84% ± 8.84, Healthy = 12.11% ± 7.15). (D) Similarly, SCI rats displayed a reduced percentage of open arm entries compared to healthy rats (one-way ANOVA; p = 0.0267; SCI vs. Healthy p = 0.027. Sham = 19.42% ± 9.91, SCI = 9.5% ± 9.58, SCI-FMT = 19.71% ± 11.79, Healthy = 22.94% ± 8.67). Although not significant, SCI rats also displayed a reduced percentage of open arm entries compared to sham (p = 0.1352) and SCI-FMT rats (p = 0.0909). (E) 4 weeks after injury, rats were tested in the light-dark box with increased time and reduced latency to enter the light component indicating decreased anxiety-like behaviour. FMT treated animals spent significantly more time in the light component compared to the SCI group as well as the Healthy group (Two-way repeated measures ANOVA; SCI vs. SCI-FMT p = 0.01, SCI-FMT vs. Healthy p = 0.03. Sham = 37.03s ± 38.97, SCI = 20.3s ± 13.88, SCI-FMT = 74.77s ± 44.25, Healthy = 34.17s ± 31.63; baseline and 1 week post-injury values are shown in S1 Fig.). (F) FMT rats displayed a reduced latency to enter the light compartment compared to the SCI group, although this failed to reach significance (two-way repeated measure ANOVA; p = 0.0843; Sham = 4.09min ± 4.05, SCI = 5.79min ± 4.30, SCI-FMT = 1.04min ±, Healthy = 3.95min ± 4.31). (G) There were no differences between groups in the percentage of sucrose water consumed at any time point tested. Error bars indicate standard error mean. *p<0.05, **p<0.01, ***p<0.001.CC BY · PubMed Central
Fig 4 Fecal microbiota transplant did not affect open field behaviour or lesion pathology. Rats were tested in the open field before surgeries, then again 1 week and 3 weeks following later. (A) Representative image from the motion tracking software used to analyze the movement of rats in the open field. (B) 1 week following injury, both SCI and SCI-FMT groups travelled less distance compared to baseline than sham and healthy animals Repeated measure two-way ANOVA: Sham vs SCI p = 0.026, Sham vs. SCI-FMT p = 0.007, SCI vs. Healthy p = 0.036, SCI-FMT vs. Healthy p = 0.011. Sham = 85.33% ± 28.83, SCI = 50.78% ± 30.98, SCI-FMT = 48.58% ± 43.41, Healthy = 84.57% ± 20.6). By 3 weeks following injury there was no difference in the distance travelled in the open field relative to baseline between groups. (C) We next assessed the percent distance travelled in the inner 60% of the arena relative to baseline. At both 1 and 3 weeks following injury there was no difference between groups in the percent inner distance travelled. (D) Representative images of the maximum lesion site from SCI-FMT and SCI groups stained with cresyl violet. (E) There was no significant difference between SCI and SCI-FMT groups in the lesion progression (Mann-Whitney test; p = 0.3688). The average maximum lesion severity for SCI group was 32.67% and 32.95% for SCI-FMT groups. The average lesion extension was 4.06 mm for the SCI group and 3.886 mm for the SCI-FMT group. (F) Sham and Healthy rats did not show any forelimb asymmetry at any time point (Repeated measure two-way ANOVA; Pre-Injury: Sham = 51.97% ± 8.38, Healthy = 47.82% ± 7.94; 1WPI: Sham = 53.05% ± 9.02, Healthy = 49.92% ± 4.90, 3WPI: Sham = 51.33% ± 14.47, Healthy = 44.35% ± 7.86). Prior to SCI, rats did not display any contralateral bias in their paw placements in the cylinder test (SCI = 53.26% ± 5.66, SCI-FMT = 48.75% ± 7.20). 1 week post-injury, SCI and SCI-FMT rats made significantly fewer ipsilesional (right) paw placements than Healthy and Sham animals. At one week post injury, the SCI-FMT group had a more significant contralateral bias in their paw placements than the SCI group (SCI vs. SCI-FMT p = 0.0008). 3 weeks after injury, the differences between SCI and SCI-FMT groups were abolished, with all SCI animals making fewer ipsilesional paw placements than uninjured groups (Sham vs. SCI p = 0.0001 (1WPI & 3WPI), sham vs. SCI-FMT p < 0.0001 (1WPI & 3WPI), SCI vs. Healthy p = 0.0015 (1WPI) p = 0.0013 (3WPI), SCI-FMT vs. Healthy p < 0.0001 (1WPI & 3WPI). 1WPI: SCI = 29.12% ± 25.33, SCI-FMT = 8.59% ± 12.93; 3WPI: SCI = 23.29% ± 11.8, SCI-FMT = 16.33% ± 15.66). Error bars indicate standard error mean.*p<0.05, **p<0.01, **p<0.001.
Fig 4 Fecal microbiota transplant did not affect open field behaviour or lesion pathology. Rats were tested in the open field before surgeries, then again 1 week and 3 weeks following later. (A) Representative image from the motion tracking software used to analyze the movement of rats in the open field. (B) 1 week following injury, both SCI and SCI-FMT groups travelled less distance compared to baseline than sham and healthy animals Repeated measure two-way ANOVA: Sham vs SCI p = 0.026, Sham vs. SCI-FMT p = 0.007, SCI vs. Healthy p = 0.036, SCI-FMT vs. Healthy p = 0.011. Sham = 85.33% ± 28.83, SCI = 50.78% ± 30.98, SCI-FMT = 48.58% ± 43.41, Healthy = 84.57% ± 20.6). By 3 weeks following injury there was no difference in the distance travelled in the open field relative to baseline between groups. (C) We next assessed the percent distance travelled in the inner 60% of the arena relative to baseline. At both 1 and 3 weeks following injury there was no difference between groups in the percent inner distance travelled. (D) Representative images of the maximum lesion site from SCI-FMT and SCI groups stained with cresyl violet. (E) There was no significant difference between SCI and SCI-FMT groups in the lesion progression (Mann-Whitney test; p = 0.3688). The average maximum lesion severity for SCI group was 32.67% and 32.95% for SCI-FMT groups. The average lesion extension was 4.06 mm for the SCI group and 3.886 mm for the SCI-FMT group. (F) Sham and Healthy rats did not show any forelimb asymmetry at any time point (Repeated measure two-way ANOVA; Pre-Injury: Sham = 51.97% ± 8.38, Healthy = 47.82% ± 7.94; 1WPI: Sham = 53.05% ± 9.02, Healthy = 49.92% ± 4.90, 3WPI: Sham = 51.33% ± 14.47, Healthy = 44.35% ± 7.86). Prior to SCI, rats did not display any contralateral bias in their paw placements in the cylinder test (SCI = 53.26% ± 5.66, SCI-FMT = 48.75% ± 7.20). 1 week post-injury, SCI and SCI-FMT rats made significantly fewer ipsilesional (right) paw placements than Healthy and Sham animals. At one week post injury, the SCI-FMT group had a more significant contralateral bias in their paw placements than the SCI group (SCI vs. SCI-FMT p = 0.0008). 3 weeks after injury, the differences between SCI and SCI-FMT groups were abolished, with all SCI animals making fewer ipsilesional paw placements than uninjured groups (Sham vs. SCI p = 0.0001 (1WPI & 3WPI), sham vs. SCI-FMT p < 0.0001 (1WPI & 3WPI), SCI vs. Healthy p = 0.0015 (1WPI) p = 0.0013 (3WPI), SCI-FMT vs. Healthy p < 0.0001 (1WPI & 3WPI). 1WPI: SCI = 29.12% ± 25.33, SCI-FMT = 8.59% ± 12.93; 3WPI: SCI = 23.29% ± 11.8, SCI-FMT = 16.33% ± 15.66). Error bars indicate standard error mean.*p<0.05, **p<0.01, **p<0.001.CC BY · PubMed Central
Fig 5 Analysis of stool samples by 16s rRNA sequencing. (A) Shannon index of alpha diversity was calculated from the operational taxonomic unit (OTU) table (see methods) and a LMM was fitted revealing no statistical differences between groups. A significant time effect was found with an increase in alpha diversity at 3 days and normalization by 4 weeks. (B) The number of differentially represented OTUs at the genus-species level (measured by the limma method) between 3 days and pre-injury was higher in SCI group, followed by Sham, and SCI-FMT groups. Healthy animals did not show significant differences at the specified cutoff (adj. p<0.01), indicating the stability of the microbiota in healthy rats over the course of the experiment. By 4 weeks the number of differences were highly reduced demonstrating the normalization of the microbiota composition by the end of the follow up. (C) Pairwise comparisons were performed between groups at each time point by contrasting the coefficients of the limma model. When comparing between groups, major differences were observed at 3 days post-injury, especially between SCI and Healthy, and SCI and SCI-FMT groups. Notice that the number of differentially represented genus-species comparing Healthy vs. SCI-FMT was the smallest of all, confirming the proximity of these two groups. (D) Unsupervised ordination was performed over all the samples by NMDS and Bray-Curtis dissimilarity of the genus-species OTUs level (stress of 0.084 after 20 iterations). Considering group and time, the 2D-plot of the NMDS two first components shows a cluster cloud of the ‘microbiota composition’ and the deviation of the SCI animals from that cluster at 3 days post-injury. The big points represent the 2D centroids of each group and timepoint, and the lines join the time trajectory for each group. A PERMANOVA was used to perform hypothesis testing in the Bray-Curtis dissimilarity matrix between groups, timepoints and their interaction (group effect R2 = 0.079 p<0.001; time effect R2 = 0.091 p<0.001; interaction R2 = 0.112 p<0.001). Notice that only around 30% of the variance is explained by group, time and their interaction, indicative that other factors might contribute to the big individual differences. Nonetheless, the dispersion between groups, especially SCI compared to Healthy and SCI-FMT can be appreciated when that same analysis is plotted by timepoints (E). Dotted lines in (E) represents the 2D distance of each animal with the respective centroid at each timepoint in the NMDS space.
Fig 5 Analysis of stool samples by 16s rRNA sequencing. (A) Shannon index of alpha diversity was calculated from the operational taxonomic unit (OTU) table (see methods) and a LMM was fitted revealing no statistical differences between groups. A significant time effect was found with an increase in alpha diversity at 3 days and normalization by 4 weeks. (B) The number of differentially represented OTUs at the genus-species level (measured by the limma method) between 3 days and pre-injury was higher in SCI group, followed by Sham, and SCI-FMT groups. Healthy animals did not show significant differences at the specified cutoff (adj. p<0.01), indicating the stability of the microbiota in healthy rats over the course of the experiment. By 4 weeks the number of differences were highly reduced demonstrating the normalization of the microbiota composition by the end of the follow up. (C) Pairwise comparisons were performed between groups at each time point by contrasting the coefficients of the limma model. When comparing between groups, major differences were observed at 3 days post-injury, especially between SCI and Healthy, and SCI and SCI-FMT groups. Notice that the number of differentially represented genus-species comparing Healthy vs. SCI-FMT was the smallest of all, confirming the proximity of these two groups. (D) Unsupervised ordination was performed over all the samples by NMDS and Bray-Curtis dissimilarity of the genus-species OTUs level (stress of 0.084 after 20 iterations). Considering group and time, the 2D-plot of the NMDS two first components shows a cluster cloud of the ‘microbiota composition’ and the deviation of the SCI animals from that cluster at 3 days post-injury. The big points represent the 2D centroids of each group and timepoint, and the lines join the time trajectory for each group. A PERMANOVA was used to perform hypothesis testing in the Bray-Curtis dissimilarity matrix between groups, timepoints and their interaction (group effect R2 = 0.079 p<0.001; time effect R2 = 0.091 p<0.001; interaction R2 = 0.112 p<0.001). Notice that only around 30% of the variance is explained by group, time and their interaction, indicative that other factors might contribute to the big individual differences. Nonetheless, the dispersion between groups, especially SCI compared to Healthy and SCI-FMT can be appreciated when that same analysis is plotted by timepoints (E). Dotted lines in (E) represents the 2D distance of each animal with the respective centroid at each timepoint in the NMDS space.CC BY · PubMed Central
Fig 6 Functional analysis of the ‘microbiota composition’ by PICRUST at 3 days. Of the 328 functional pathways found by the metagenomic analysis of the 16s RNA by using PICRUST, 86 were found differentially represented between groups (determined by limma method and cutoff of adj.p<0.05). (A) Hierarchical clustering with these 86 functional pathways was performed, demonstrating the proximity between SCI and Sham, and Healthy and SCI-FMT. This analysis revealed two clusters of functional pathways that followed different direction between the SCI/Sham binomial and the Healthy/SCI-FMT pair, with over-representation and under-representation of these pathways respectively and vice versa. (B) A PCA was performed to determine the major components of functional pathways that explains the observed variance between animals in a unsupervised manner. The 2D plot of the animals’ scores for the two first components (explaining ~56% of the variance) shows a clear differentiation between the aforementioned group pairs, PC1 being he component that distinguish between SCI/Sham and Healthy/SCI-FMT. Leave-one-out cross-validation demonstrated high stability of the PCA results (Pearson’s PC1 r = 0.99 ± 0.0003; PC2 r = 0.99 ± 0.0029). (C) List of the most important (|loading| <0.6) functional pathways that contribute to PC1. (D) Hypothesis testing of the PC1 and PC2 scores by Kruskal-Wallis showed statistical differences between groups in the scores (PC1 p<0.0001, PC2 p<0.01). Conover test for multiple comparisons revealed that PC1 scores were statistically different between SCI and Healthy and SCI-FMT (p<0.001) but not between SCI and Sham (p = 0.56). PC2 scores were statistically different comparing Sham animals and SCI-FMT rats (p<0.01). (E) A second PCA between the first and second components of the ‘functional microbiota PCA and the behavioural analysis was conducted to study the interrelation of the microbiota and the animal performance. This second PCA shows how the ‘functional microbiota PC1 correlated with the performance on the cylinder test and the LDB 4 weeks post injury, while ‘functional microbiota PC2 was associated with EPM and LDB at 1 week. The direction of the loadings scores ‘functional microbiota PC1 was invers to EPM distance, EPM % time in the open arms and EPM time in the open arms absolute value. That can be interpreted as higher score in ‘functional microbiota PC1 (pointing to SCI/Sham in B) being associated with less distance and open arm time in the EPM. Similar interpretation can be done for the ‘functional microbiota PC2 and the LDB at four weeks. Overall this second PCA shows an association between the functional pathway composition of the metagenomic microbiota and the behavioural outcomes. Leave-one-out cross-validation also showed stability of the second PCA (Pearson’s PC1 r = 0.96 ± 0.025; PC2 r = 0.95 ± 0.029). In D *** p<0.001. In E * |loading|<0.4.
Fig 6 Functional analysis of the ‘microbiota composition’ by PICRUST at 3 days. Of the 328 functional pathways found by the metagenomic analysis of the 16s RNA by using PICRUST, 86 were found differentially represented between groups (determined by limma method and cutoff of adj.p<0.05). (A) Hierarchical clustering with these 86 functional pathways was performed, demonstrating the proximity between SCI and Sham, and Healthy and SCI-FMT. This analysis revealed two clusters of functional pathways that followed different direction between the SCI/Sham binomial and the Healthy/SCI-FMT pair, with over-representation and under-representation of these pathways respectively and vice versa. (B) A PCA was performed to determine the major components of functional pathways that explains the observed variance between animals in a unsupervised manner. The 2D plot of the animals’ scores for the two first components (explaining ~56% of the variance) shows a clear differentiation between the aforementioned group pairs, PC1 being he component that distinguish between SCI/Sham and Healthy/SCI-FMT. Leave-one-out cross-validation demonstrated high stability of the PCA results (Pearson’s PC1 r = 0.99 ± 0.0003; PC2 r = 0.99 ± 0.0029). (C) List of the most important (|loading| <0.6) functional pathways that contribute to PC1. (D) Hypothesis testing of the PC1 and PC2 scores by Kruskal-Wallis showed statistical differences between groups in the scores (PC1 p<0.0001, PC2 p<0.01). Conover test for multiple comparisons revealed that PC1 scores were statistically different between SCI and Healthy and SCI-FMT (p<0.001) but not between SCI and Sham (p = 0.56). PC2 scores were statistically different comparing Sham animals and SCI-FMT rats (p<0.01). (E) A second PCA between the first and second components of the ‘functional microbiota PCA and the behavioural analysis was conducted to study the interrelation of the microbiota and the animal performance. This second PCA shows how the ‘functional microbiota PC1 correlated with the performance on the cylinder test and the LDB 4 weeks post injury, while ‘functional microbiota PC2 was associated with EPM and LDB at 1 week. The direction of the loadings scores ‘functional microbiota PC1 was invers to EPM distance, EPM % time in the open arms and EPM time in the open arms absolute value. That can be interpreted as higher score in ‘functional microbiota PC1 (pointing to SCI/Sham in B) being associated with less distance and open arm time in the EPM. Similar interpretation can be done for the ‘functional microbiota PC2 and the LDB at four weeks. Overall this second PCA shows an association between the functional pathway composition of the metagenomic microbiota and the behavioural outcomes. Leave-one-out cross-validation also showed stability of the second PCA (Pearson’s PC1 r = 0.96 ± 0.025; PC2 r = 0.95 ± 0.029). In D *** p<0.001. In E * |loading|<0.4.CC BY · PubMed Central

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Authors
Schmidt, E. K. A., Torres-Espin, A., Raposo, P. J. F., Madsen, K. L., Kigerl, K. A., Popovich, P. G., Fenrich, K. K., & Fouad, K.
Affiliations
  • University of Alberta (CA)
  • The Ohio State University Wexner Medical Center (US)
Corresponding author
Fouad, Karim
ORCID
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2020-01-15
Volume
15
Issue
1
Pages
e0226128
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0226128
PMC
PMC6961833
OpenAlex
W2998874562
Cited by
119
References
73
Citations by year
2026: 8 · 2025: 12 · 2024: 12 · 2023: 24 · 2022: 17 · 2021: 31 · 2020: 15
FWCI
7.13
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
Funders
  • Craig H. Neilsen Foundation (542589)
  • Canadian Institutes of Health Research (119281)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Health Sciences › Medicine › Clinical Neurology › Spinal Cord Injury Research
  • Health Sciences › Medicine › Gastroenterology › Gastrointestinal motility and disorders
Keywords
fecal microbiota transplantation, spinal cord injury, gut dysbiosis, anxiety-like behavior, gut microbiota, mood disorders, depression
MeSH terms
Gastrointestinal Microbiome, Fecal Microbiota Transplantation, Animals, Anxiety, Behavior, Animal, Spinal Cord Injuries, Maze Learning, Recovery of Function, Rats, Dysbiosis

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