2017
DOI: 10.1128/msystems.00036-17
|View full text |Cite
|
Sign up to set email alerts
|

Development of Inflammatory Bowel Disease Is Linked to a Longitudinal Restructuring of the Gut Metagenome in Mice

Abstract: IBD patients harbor distinct microbial communities with functional capabilities different from those seen with healthy people. But is this cause or effect? Answering this question requires data on changes in gut microbial communities leading to disease onset. By performing weekly metagenomic sequencing and mixed-effects modeling on an established mouse model of IBD, we identified several functional pathways encoded by the gut microbiome that covary with host immune status. These pathways are novel early biomar… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

2
41
0

Year Published

2019
2019
2023
2023

Publication Types

Select...
5
3

Relationship

0
8

Authors

Journals

citations
Cited by 47 publications
(45 citation statements)
references
References 131 publications
2
41
0
Order By: Relevance
“…1a ) compared to treatment with vehicle control. To take advantage of the “within-subjects” analysis of longitudinal changes, we used a generalized linear mixed-effects model to evaluate longitudinal changes 22 , and found that Firmicutes were significantly increased (slope=0.038, p =0.005) and Bacteroidetes were decreased (slope=-0.049, p =0.057). Eleven bacterial genera were differentially abundant comparing high-dose treatment to vehicle control (4 decreased and 7 increased, DESeq p adj <0.01, day 4).…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…1a ) compared to treatment with vehicle control. To take advantage of the “within-subjects” analysis of longitudinal changes, we used a generalized linear mixed-effects model to evaluate longitudinal changes 22 , and found that Firmicutes were significantly increased (slope=0.038, p =0.005) and Bacteroidetes were decreased (slope=-0.049, p =0.057). Eleven bacterial genera were differentially abundant comparing high-dose treatment to vehicle control (4 decreased and 7 increased, DESeq p adj <0.01, day 4).…”
Section: Resultsmentioning
confidence: 99%
“…DESeq2 62 was used to determine differentially abundant taxa on raw count data. Significance testing of longitudinal trends was determined using generalized mixed effects models using the cplm package 22,63 (v. 0.7-7) on clr normalized values. For each sample, fastq files are available in NCBI’s Sequence Read Archive (SRA), accession number SRP5125967.…”
Section: Methodsmentioning
confidence: 99%
“…Generalized linear mixed models (GLMMs) (97) were used to determine significantly different temporal trends in abundance for microbial taxa. A Tweedie compound Poisson distribution was chosen for this model given that it best captures the nature of amplicon sequence datasets (e.g., overdispersion, zero-inflated datasets, and continuous values) (98). The ‘cpglmm’ function of the ‘cplm’ R package (99) was used for time-series analyses following the general procedure outlined in (98).…”
Section: Methodsmentioning
confidence: 99%
“…A Tweedie compound Poisson distribution was chosen for this model given that it best captures the nature of amplicon sequence datasets (e.g., overdispersion, zero-inflated datasets, and continuous values) (98). The ‘cpglmm’ function of the ‘cplm’ R package (99) was used for time-series analyses following the general procedure outlined in (98). Summarized sequence count tables of family-level taxonomic units were created and full GLMMs were fit relating counts to treatment, days post transplantation, the interaction of main effects, and random effects of each taxon.…”
Section: Methodsmentioning
confidence: 99%
“…We included in each regression model the same demographic and gut-related terms to account for their variance as well. The regression method used was a compound Poisson generalized linear model (CPGLM; (73)), which uses a distribution that has a point mass over zero, allowing it to better handle the sparseness of functional and taxonomic community data (74). After all pairwise regressions, we adjusted the p-values using the False Discovery Rate (FDR) with a cutoff of q = 0.05.…”
Section: Methodsmentioning
confidence: 99%