Host genetics and gut microbiota cooperatively contribute to azoxymethane-induced acute toxicity in Collaborative Cross mice

Zhong, Chenhan; He, Li; Lee, Sun-Young; Chang, Hang; Zhang, Yuqing; Threadgill, David W.; Yuan, Ying; Zhou, Fuling; Celniker, S; Xia, Yankai; Snijders, Antoine M.; Mao, Jian‐Hua

doi:10.1007/s00204-021-02972-x

Cited by 12 publications

(12 citation statements)

References 43 publications

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“…Growing evidence suggests a complex interaction between the host genes and the microbiome [26, 29,56,57]. Further, studies have revealed the combined effects of host genetics and microbiome composition on several phenotypes including sleep, anxiety, and liver damage [34,36,37]. The effect of diet, environment, early exposure to certain microbes and the use of antibiotics, makes it challenging to study these interactions in humans.…”

Section: Discussionmentioning

confidence: 99%

“…We wanted to identify correlations between pre-infection bacterial composition and the outcome of STm infection. Previous CC studies have successfully employed machine-learning algorithms to predict the outcome of phenotypes including memory, anxiety and AOM induced toxicity [35][36][37]. We employed a similar random forest classi er to predict the CC genotype with respect to microbial composition.…”

Section: Discussionmentioning

confidence: 99%

“…The CC population has also been used to associate microbial metabolites with GI cancers, lipid metabolism and in ammation [33]. Recently, the intestinal microbiome composition of CC mice has been associated with a sleep phenotype, memory, anxiety-like behavior, and Azoxymethaneinduced toxicity [34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

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Using the Collaborative Cross to identify the role of host genetics in defining the murine gut microbiome

Nagarajan

Scoggin

Gupta

et al. 2022

Preprint

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Background: The human gut microbiota is a complex community comprised of trillions of bacteria and is critical for the digestion and absorption of nutrients. Bacterial communities of the intestinal microbiota influence the development of several conditions and diseases. We studied the effect of host genetics on gut microbial composition using Collaborative Cross (CC) mice, a panel of genetically diverse mouse strains. CC mice are genetically diverse across strains, but genetically identical within a given strain allowing for repetition and deeper analysis than is possible with other collections of genetically diverse mice. Results: 16S rRNA from the feces of 167 mice from 28 different CC strains was sequenced and analyzed using the Qiime2 pipeline. We observed large variance in the bacterial composition across CC strains starting at the phylum level. Using bacterial composition data, we identified 26 significant Quantitative Trait Locus (QTL) linked to 23 genera on 15 different mouse chromosomes. Genes within these intervals were analyzed for significant association with pathways and the previously known human GWAS database using Enrichr and DAVID analysis. Multiple host genes involved in obesity, glucose homeostasis, immunity, neurological diseases, and many other protein-coding genes located in these regions may play roles in determining the composition of the gut microbiota. After the collection of feces used for sequencing, a subset of CC mice was infected with Salmonella Typhimurium. Using the infection outcome data, we correlated the increase in abundance of Genus Lachnospiraceae and decrease in Genus Parasutterella to positive health outcomes after infection. Machine learning classifiers accurately predicted the CC strain and the infection outcome using pre-infection bacterial composition data from the feces. Conclusion: Our study supports the hypothesis that multiple host genes influence the gut microbiome composition and homeostasis, and that certain organisms may influence health outcomes after S. Typhimurium infection.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Using the Collaborative Cross to identify the role of host genetics in defining the murine gut microbiome

Nagarajan

Scoggin

Gupta

et al. 2022

Preprint

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“…All mice were maintained on standard chow (PicoLab Rodent diet 5053). Since 10 mg/kg of AOM is lethal to some CC strains, 50 426 11-week-old mice were injected intraperitoneally once weekly with 5 mg/kg of AOM (Sigma, St. Louis, MO) for six weeks for tumor induction and were monitored for one year ( Figure 1(a) ). Finally, a total of 355 surviving mice from 30 CC strains were used in this study (Supplemental Table S1).…”

Section: Methodsmentioning

confidence: 99%

Genetic and microbial determinants of azoxymethane-induced colorectal tumor susceptibility in Collaborative Cross mice and their implication in human cancer

Li,

Zhong,

Yang

et al. 2024

Gut Microbes

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The insights into interactions between host genetics and gut microbiome (GM) in colorectal tumor susceptibility (CTS) remains lacking. We used Collaborative Cross mouse population model to identify genetic and microbial determinants of Azoxymethane-induced CTS. We identified 4417 CTS-associated single nucleotide polymorphisms (SNPs) containing 334 genes that were transcriptionally altered in human colorectal cancers (CRCs) and consistently clustered independent human CRC cohorts into two subgroups with different prognosis. We discovered a set of genera in early-life associated with CTS and defined a 16-genus signature that accurately predicted CTS, the majority of which were correlated with human CRCs. We identified 547 SNPs associated with abundances of these genera. Mediation analysis revealed GM as mediators partially exerting the effect of SNP UNC3869242 within Duox2 on CTS. Intestine cell-specific depletion of Duox2 altered GM composition and contribution of Duox2 depletion to CTS was significantly influenced by GM. Our findings provide potential novel targets for personalized CRC prevention and treatment.

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“…[13][14][15] Moreover, large CC strain-dependent variations in many phenotypes, such as spontaneous tumor development, have been reported. [16][17][18][19][20][21][22][23][24][25][26][27][28][29] In this study, we identified host genetic variants that predispose Erbb2-driven tumor development and metastasis using the CC mouse resource. Additionally, we systematically evaluated the clinical value for prognosis and therapeutic responses of a mouse mammary tumor susceptibility gene signature in human BC using publicly available cohorts, including clinical trial cohorts.…”

Section: Introductionmentioning

confidence: 99%

A susceptibility gene signature for ERBB2-driven mammary tumor development and metastasis in Collaborative Cross mice with human translational value

Yang,

Wang,

Blanco-Gómez

et al. 2024

Preprint

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BackgroundDeeper insights into ERBB2-driven cancers are essential to develop novel treatment avenues for ERBB2+ breast cancers (BCs). We employed Collaborative Cross (CC) mouse model, along with human translational evaluation, to unearth genetic factors underpinning Erbb2-driven mammary tumor development and metastasis.Methods732 F1 hybrid female mice between FVB/N MMTV-Erbb2and 30 CC strains were monitored for mammary tumor phenotypes. GWAS pinpointed SNPs that influence various tumor phenotypes. Clinical value of a mouse tumor susceptibility gene signature (mTSGS) was evaluated using public datasets, encompassing TCGA, METABRIC and I-SPY2 cohorts. The predictive power of mTSGS for response to chemotherapy was validatedin vivousing genetically diverse MMTV-Erbb2mice.ResultsDistinct variances in tumor onset, multiplicity, and metastatic patterns were observed across CC strains. Besides lung metastasis, liver and kidney metastases emerged in specific CC strains. GWAS identified 1525 SNPs, 800 SNPs, 568 SNPs, and 23 SNPs significantly associated with tumor onset, multiplicity, lung metastasis, and liver metastasis, respectively. Multivariate analyses flagged SNPs in 20 genes independently tied to various tumor characteristics, designated as mTSGS. These 20 genes were transcriptionally altered in human BCs. We then established mTSGS scores (mTSGSS) based on their transcriptional levels. The mTSGSS showed prognostic values, superseding clinical factors and PAM50 molecular subtype across cohorts. Moreover, mTSGSS predicted pathological complete response (pCR) to six of thirteen treatment regimens, including chemotherapy only, in I-SPY2 study. Importantly, the predictive value of the mTSGSS for pCR stood independent of the MammaPrint score. The power of mTSGSS for predicting chemotherapy response was validated in anin vivoMMTV-Erbb2 model, showing that like findings in human patients, mouse tumors with low mTSGSS were most likely to respond to treatment.ConclusionOur investigation has unveiled many novel genes predisposing individuals to ERBB2-driven cancer. Translational findings indicate that mTSGSS holds promise as a biomarker to refine treatment strategies for BC patients.

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Host genetics and gut microbiota cooperatively contribute to azoxymethane-induced acute toxicity in Collaborative Cross mice

Cited by 12 publications

References 43 publications

Using the Collaborative Cross to identify the role of host genetics in defining the murine gut microbiome

Using the Collaborative Cross to identify the role of host genetics in defining the murine gut microbiome

Genetic and microbial determinants of azoxymethane-induced colorectal tumor susceptibility in Collaborative Cross mice and their implication in human cancer

A susceptibility gene signature for ERBB2-driven mammary tumor development and metastasis in Collaborative Cross mice with human translational value

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