Summary of Study ST002345

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001505. The data can be accessed directly via it's Project DOI: 10.21228/M85717 This work is supported by NIH grant, U2C- DK119886.

See: https://www.metabolomicsworkbench.org/about/howtocite.php

This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.

Study ID	ST002345
Study Title	Stress-Induced Mucosal Layer Disruption Drives Gut Dysbiosis and Depressive-like Behaviors
Study Summary	Depression is a common mental health condition with a large social and economic impact. While depression etiology is multifactorial, chronic stress is a well-accepted contributor to disease onset. In addition, depression is associated with altered gut microbial signatures that can be replicated in animal models. While targeted restoration of the microbiome has been shown to reduce depressive-like behaviors in mice, the complexity and diversity of the human microbiome has complicated therapeutic intervention in patients. To circumvent these limitations, there is a critical need for identifying pathways responsible for microbiome dysbiosis. Here, for the first time, we identify the changes in host physiology that induce microbiome dysbiosis. Specifically, we show that a component of mucosal layer, the transmembrane protein mucin 13, can regulate microbiome composition. Using a model of chronic stress to induce behavioral and microbial changes in mice, we show a significant reduction in mucin 13 expression across the intestines that occurs independently of the microbiome. Furthermore, deleting Muc13 leads to gut dysbiosis, and baseline behavioral changes normally observed after stress exposure. Together, these results validate the hypothesis that mucosal layer disruption is an initiating event in stress-induced dysbiosis and offer mucin 13 as a potential new therapeutic target for microbiome dysbiosis in stress-induced depression. For the first time, our data provide an upstream and conserved target for treating microbiome dysbiosis, a result with sweeping implications for diseases presenting with microbial alterations.
Institute	University of Virginia
Last Name	Rivet-Noor
First Name	Courtney
Address	409 Lane Road, Charlottsville, Virginia, 22903, USA
Email	crr4tz@virginia.edu
Phone	434-243-1903
Submit Date	2022-11-10
Num Groups	2
Total Subjects	23
Num Males	23
Raw Data Available	Yes
Raw Data File Type(s)	raw(Thermo)
Analysis Type Detail	LC-MS
Release Date	2022-11-28
Release Version	1

Select appropriate tab below to view additional metadata details:

Project:

Project ID:	PR001505
Project DOI:	doi: 10.21228/M85717
Project Title:	Stress-Induced Mucosal Layer Disruption Drives Gut Dysbiosis and Depressive-like Behaviors
Project Summary:	Depression is a common mental health condition with a large social and economic impact. While depression etiology is multifactorial, chronic stress is a well-accepted contributor to disease onset. In addition, depression is associated with altered gut microbial signatures that can be replicated in animal models. While targeted restoration of the microbiome has been shown to reduce depressive-like behaviors in mice, the complexity and diversity of the human microbiome has complicated therapeutic intervention in patients. To circumvent these limitations, there is a critical need for identifying pathways responsible for microbiome dysbiosis. Here, for the first time, we identify the changes in host physiology that induce microbiome dysbiosis. Specifically, we show that a component of mucosal layer, the transmembrane protein mucin 13, can regulate microbiome composition. Using a model of chronic stress to induce behavioral and microbial changes in mice, we show a significant reduction in mucin 13 expression across the intestines that occurs independently of the microbiome. Furthermore, deleting Muc13 leads to gut dysbiosis, and baseline behavioral changes normally observed after stress exposure. Together, these results validate the hypothesis that mucosal layer disruption is an initiating event in stress-induced dysbiosis and offer mucin 13 as a potential new therapeutic target for microbiome dysbiosis in stress-induced depression. For the first time, our data provide an upstream and conserved target for treating microbiome dysbiosis, a result with sweeping implications for diseases presenting with microbial alterations.
Institute:	University of Virginia
Department:	Neuroscience
Laboratory:	Gaultier Lab
Last Name:	Rivet-Noor
First Name:	Courtney
Address:	409 Lane Road, Charlottsville, Virginia, 22903, USA
Email:	crr4tz@virginia.edu
Phone:	434-243-1903
Funding Source:	NIH

Subject:

Subject ID:	SU002434
Subject Type:	Mammal
Subject Species:	Mus musculus
Taxonomy ID:	10090
Age Or Age Range:	12-24 weeks
Gender:	Male
Animal Animal Supplier:	Jackson
Animal Light Cycle:	12L/12D

Factors:

Subject type: Mammal; Subject species: Mus musculus (Factor headings shown in green)

mb_sample_id	local_sample_id	Group
SA235359	Cal4_B	CTL
SA235360	Cal4_A	CTL
SA235361	Cal5_A	CTL
SA235362	Cal5_B	CTL
SA235363	blank1	CTL
SA235364	Cal3_A	CTL
SA235365	Cal3_B	CTL
SA235366	Cal2_B	CTL
SA235367	blank3	CTL
SA235368	blank2	CTL
SA235369	Cal1_A	CTL
SA235370	blank4	CTL
SA235371	Cal1_B	CTL
SA235372	Cal2_A	CTL
SA235373	Naive_238	Naïve
SA235374	Naive_239	Naïve
SA235375	Naive_248	Naïve
SA235376	Naive_249	Naïve
SA235377	Naive_247	Naïve
SA235378	Naive_231	Naïve
SA235379	Naive_237	Naïve
SA235380	Naive_227	Naïve
SA235381	Naive_226	Naïve
SA235382	Naive_229	Naïve
SA235383	Naive_230	Naïve
SA235384	Stress_244	Stress
SA235385	Stress_245	Stress
SA235386	Stress_246	Stress
SA235387	Stress_243	Stress
SA235388	Stress_233	Stress
SA235389	Stress_240	Stress
SA235390	Stress_234	Stress
SA235391	Stress_232	Stress
SA235392	Stress_235	Stress
SA235393	Stress_236	Stress
SA235394	Stress_241	Stress
SA235395	Stress_242	Stress

Showing results 1 to 37 of 37

Showing results 1 to 37 of 37

Collection:

Collection ID:	CO002427
Collection Summary:	Whole blood was extracted from animals at the time of euthanization from the heart chamber. Blood was collected into blood collection tubes (Fisher Scientific; #02-675-185) and spun for 10 min at 11,000g. Serum was collected and frozen in liquid nitrogen. 25uL of plasma was extracted with 500uL of acetonitrile by vortexing and centrifugation at 10min at 14,000rpm. 450uL of supernatant was transferred to new tube and dried via SpeedVac. Dried samples were reconstituted with 25uL of 50% methanol and transferred to autosampler vials. Injection volume =10uL in PRM mode for detection and quantification of 10 different analytes. Metabolite mixture was analyzed on Thermo Orbitrap IDX MS system coupled to a Vanquish UPLC system. Samples were transported via the autosampler (10uL injection volume) onto a Waters BEH C18 column. Runtime was 15min in PRM mode. Buffer A: 0.1% formic acid in water. Buffer B: 0.1% formic acid in methanol. LC Gradient: 0min: 0% B, 8min: 50% B, 9 min: 98% B, 13min: 98% B. Recalibration of system up to 15 min at 0% B for next injection.
Sample Type:	Blood (serum)
Collection Method:	Cardiac Puncture
Collection Location:	Heart
Storage Conditions:	Described in summary

Treatment:

Treatment ID:	TR002446
Treatment Summary:	Mice were subjected to 3weeks of unpredictable chronic mild restraint stress or kept in a naive setting

Sample Preparation:

Sampleprep ID:	SP002440
Sampleprep Summary:	Whole blood was extracted from animals at the time of euthanization from the heart chamber. Blood was collected into blood collection tubes (Fisher Scientific; #02-675-185) and spun for 10 min at 11,000g. Serum was collected and frozen in liquid nitrogen. 25uL of plasma was extracted with 500uL of acetonitrile by vortexing and centrifugation at 10min at 14,000rpm. 450uL of supernatant was transferred to new tube and dried via SpeedVac. Dried samples were reconstituted with 25uL of 50% methanol and transferred to autosampler vials. Injection volume =10uL in PRM mode for detection and quantification of 10 different analytes. Metabolite mixture was analyzed on Thermo Orbitrap IDX MS system coupled to a Vanquish UPLC system. Samples were transported via the autosampler (10uL injection volume) onto a Waters BEH C18 column. Runtime was 15min in PRM mode. Buffer A: 0.1% formic acid in water. Buffer B: 0.1% formic acid in methanol. LC Gradient: 0min: 0% B, 8min: 50% B, 9 min: 98% B, 13min: 98% B. Recalibration of system up to 15 min at 0% B for next injection.
Processing Storage Conditions:	Described in summary
Extract Storage:	Described in summary

Combined analysis:

Analysis ID	AN003829
Analysis type	MS
Chromatography type	Reversed phase
Chromatography system	Thermo Vanquish
Column	Waters Acquity BEH C18 (100 x 2mm,1.7um)
MS Type	ESI
MS instrument type	Orbitrap
MS instrument name	Thermo Orbitrap ID-X Tribrid
Ion Mode	UNSPECIFIED
Units	ug/mL

Chromatography:

Chromatography ID:	CH002834
Instrument Name:	Thermo Vanquish
Column Name:	Waters Acquity BEH C18 (100 x 2mm,1.7um)
Flow Gradient:	0min: 0% B, 8min: 50% B, 9 min: 98% B, 13min: 98% B. Recalibration of system up to 15 min at 0% B for next injection.
Solvent A:	100% water; 0.1% formic acid
Solvent B:	100% methanol 0.1% formic acid
Analytical Time:	15 min
Chromatography Type:	Reversed phase

MS:

MS ID:	MS003571
Analysis ID:	AN003829
Instrument Name:	Thermo Orbitrap ID-X Tribrid
Instrument Type:	Orbitrap
MS Type:	ESI
MS Comments:	Raw data files were brought into Skyline software. Targeted peak detection was done based on the parent mass. Mass analyzer set to Orbitrap and resolution power set to 120,000 resolution. Then all raw files and unknown samples were imported to Skyline. Calibration curves were generated by Linear regression fit. Targeted precursor MZ and MZ of analytes was used to track and quantification of the metabolite. Peak areas for analytes in samples were used for quantification based in the generated calibration curves.
Ion Mode:	UNSPECIFIED