The human gut microbiota (GM) has emerged as a key factor in susceptibility to, as well as a potential biomarker of, several diseases and conditions. Similarly, researchers now appreciate that the GM of laboratory animals could affect the reproducibility and translatability of many disease models, including a complete loss of phenotype. While associations between characteristics of the GM and differential disease model phenotypes are of concern, they can also be viewed as sources of discovery related to disease pathogenesis. As such, there is considerable interest in factors that inadvertently influence the composition of the GM and methods of manipulating the GM prospectively to investigate such associations and standardize or optimize disease models. The webinar will present data on variables capable of influencing the GM of laboratory rodents citing several examples and animal models, considerations related to manipulation of the GM in mice and rats, and recent data supporting the use of “dirty” mice in biomedical research.
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The Microbiome of Research Animals : Implications for Reproducibility, Translatability, and Discovery
1. The Microbiome of Research Animals
Implications for Reproducibility,
Translatability, and Discovery
Aaron Ericsson, DVM, PhD
21 March 2018
2. The Microbiome
• Resident microbial communities
• Bacteria/archaea
• Viruses/phages
• Fungi
• Eukaryotes?
• Changes in composition associated
with myriad conditions
• Animal models allow:
• Defined time-course
• Controlled genetics and environment
https://play.google.com/store/apps/details?id=io.appery.project438057
3. The Rodent Microbiome
Krych et al. (2013) PLoS One 8(5): e62578
See also Ley et al. (2005) PNAS 102 (31): 11070
Number of GM taxa shared between
humans and mice at the level of
phylum (A) and genus (B)
4. Factors affecting the Gut Microbiota
Ericsson and Franklin (2017) Lab Animal, 46(4): 114-122.
7. Before you even start the study…
Animal Supplier Your InstitutionShipping
Rederivation?
Quarantine?
Barrier or conventional?
Facility? Isolator?
Temperature control?
Food source?
Husbandry?
9. But does it matter?
B6 IL-10-/-
(GMCRL)
Pups: B6 IL-10-/-
(GMJAX)
B6 IL-10-/-
(GMTAC)
Hart et al. (2017) Frontiers in Microbiol 8: 792.
10. IL-10-/- on C57BL/6J Background
GMCRL GMJAX GMTAC
GMCRL GMJAX GMTAC
Coloniclesionscore
OTUrelativeabundance
Cecallesionscore
GMCRL GMJAX GMTAC
Hart et al. (2017) Frontiers in Microbiol 8: 792.
11. IL-10-/- on C3H/HeJ Background
GMCRL GMJAX GMTAC
GMCRL GMJAX GMTAC
ColoniclesionscoreCecallesionscore
OTUrelativeabundance
GMCRL GMJAX GMTAC
Hart et al. (2017) Frontiers in Microbiol 8: 792.
12. Controlling for Maternal Factors
CD1CD1
CD1GMCRL CD1GMJAX CD1GMTAC
Outbred CD1 colonies with four stable profiles now in 18th generation
Inbred FVB colonies with four stable profiles now in 11th generation
15. Do they travel well? Yes!
Hart et al. (manuscript in preparation)
16. Contribution of GM vs. Genetics?
J AX -A P C
m in
C C -A P C
m in
0
5 0
1 0 0
1 5 0
2 0 0
S m a ll In te s tin a l T u m o r N u m b e r
A P C
m i n
C o lo n y
TumorNumber
P < 0 .0 0 0 1
J AX -A P C
m in
C C -A P C
m in
0
2
4
6
8
C o lo n ic T u m o r N u m b e r
A P C
m i n
C o lo n y
TumorNumber
P < 0 .0 001
p < 0.0001 p < 0.0001
Data courtesy of Jim Amos-Landgraf and Jake Moskowitz
C57BL/6J-APCmin/+ CC-APCmin/+
C57BL/6J-APCmin/+ CC-APCmin/+
17. Contribution of GM vs. Genetics
J A X -A P C
m in
(G M J A X )
J A X -A P C
m in
(G M H S D )
C C -A P C
m in
(G M J A X )
C C -A P C
m in
(G M H S D )
0
5 0
1 0 0
1 5 0
2 0 0
G e n e tic B a c k g ro u n d a n d G M e ffe c ts
o n S I T u m o r C o u n ts
A P C
m in
C olony
TumorNumber
*
*
J A X -A P C
m in
(G M J A X )
J A X -A P C
m in
(G M H S D )
C C -A P C
m in
(G M J A X )
C C -A P C
m in
(G M H S D )
0
2
4
6
G e n e tic B a c k g ro u n d a n d G M e ffe c ts
o n C o lo n ic T u m o r C o u n ts
A P C
m in
C olony
TumorNumber
*
Data courtesy of Jim Amos-Landgraf and Jake Moskowitz
C57BL/6J-APCmin/+ CC-APCmin/+ C57BL/6J-APCmin/+ CC-APCmin/+
GMJAX GMHSD GMJAX GMHSD GMJAX GMHSD GMJAX GMHSD
Small intestines Colon
25. Differences during development
Cecal contents Feces
Lactobacillus spp.
Streptococcus spp.
Family Enterobacteriaceae
Family Pasteurellaceae
Aggregatibacter segnis
28. Argument for “dirty” mice
Reese et al. 2016 Cell Host Microbe 19(5): 713-719.
Baldridge et al. (2015) Science 347:266
Chou et al. (2015) PNAS 112:2175
See also Muraille, E. (2016) Frontiers in Microbiol 6:1525.
30. Summary
• Many variables can alter the microbiota of research
animals
• Animal source and rederivation
• Husbandry-related factors
• Changes in the microbiota can cause change or loss of
phenotype
• There are several methods, including targeted
rederivation, of manipulating the microbiota
• “Dirty” mice may represent more translatable models but
the presence of possible pathogens presents a challenge
• MMRRC, RRRC, and MUMC can help researchers
33. Quarantine has minimal effectC57BL/6NHsdC57BL/6J
Korte et al. (manuscript in preparation)
34. Bedding and caging affect cecal microbiota
Ericsson et al. (manuscript under review)
35. Bedding and H2O treatment matter
• Environmental factors (e.g.,
caging, bedding, water treatment)
can significantly affect GM
• Factors can have synergistic effects
• Factors can have “upstream”
effects that are muted in fecal
communities
Bidot et al. (manuscript in preparation)
37. Development of Production Colonies
Rederivation in ASF-colonized surrogates Production colony
Data courtesy of Jeff Lohmiller (Taconic)
38. Sample to Insight
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Q&A session
For up-to-date licensing information and product-specific disclaimers for QIAGEN products, see the respective
QIAGEN kit handbook or user manual. QIAGEN kit handbooks and user manuals are available at www.qiagen.com
or can be requested from QIAGEN Technical Services or your local distributor.
Questions?
Thank you to our speaker for the informative presentation!