A combination of genetics and microbiota influences the severity of the obesity phenotype in diet-induced obesity

Smoczek, Margarethe; Vital, Marius; Wedekind, Dirk; Basic, Marijana; Zschemisch, Nils-Holger; Pieper, Dietmar H.; Siebert, Anja; Büettner, Manuela

doi:10.1038/s41598-020-63340-w

Cited by 8 publications

(21 citation statements)

References 60 publications

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“…However, given that the substrains are widely considered interchangeable, significant phenotypic differences among substrains are often overlooked. The [167,168]. Use of wild-type littermates might standardize the microbiota, thereby facilitating investigation of the phenotype or function of specific genes that could be affected by the microbial community [169,170].…”

Section: Large-scale and Complex Resourcesmentioning

confidence: 99%

Substrains matter in phenotyping of C57BL/6 mice

Mekada

Yoshiki

2021

Exp. Anim.

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The inbred mouse strain C57BL/6 has been widely used as a background strain for spontaneous and induced mutations. Developed in the 1930s, the C57BL/6 strain diverged into two major groups in the 1950s, namely, C57BL/6J and C57BL/6N, and more than 20 substrains have been established from them worldwide. We previously reported genetic differences among C57BL/6 substrains in 2009 and 2015. Since then, dozens of reports have been published on phenotypic differences in behavioral, neurological, cardiovascular, and metabolic traits. Substrains need to be chosen according to the purpose of the study because phenotypic differences might affect the experimental results. In this paper, we review recent reports of phenotypic and genetic differences among C57BL/6 substrains, focus our attention on the proper use of C57BL/6 and other inbred strains in the era of genome editing, and provide the life science research community wider knowledge about this subject.

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Section: Large-scale and Complex Resourcesmentioning

confidence: 99%

Substrains matter in phenotyping of C57BL/6 mice

Mekada

Yoshiki

2021

Exp. Anim.

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“…Phenotypic differences among B6 substrains have been reported in many studies in a variety of fields, including metabolism [7, 12, 14-19, 31, 33], alcohol preference [35-38], stress response [27, 32, 39-44], cardiovascular [20, 21, 45], oxidative stress [22,23], vision [8-10], bone [28], liver [29, 30, 46], kidney [24, 47, 48], microbiota [15], and seizures [49]. However, the genes responsible for these phenotypic differences have not been identified.…”

Section: Discussionmentioning

confidence: 99%

“…The copyright holder for this preprint this version posted July 7, 2022. ; https://doi.org/10.1101/2022.07.06.499024 doi: bioRxiv preprint variety of fields, including metabolism [7, 12, 14-19, 31, 33], alcohol preference [35][36][37][38], stress response [27,32,[39][40][41][42][43][44], cardiovascular [20,21,45], oxidative stress [22,23], vision [8][9][10], bone [28], liver [29,30,46], kidney [24,47,48], microbiota [15], and seizures [49]. However, the genes responsible for these phenotypic differences have not been identified.…”

Section: Discussionmentioning

confidence: 99%

“…Many phenotypic differences [11][12][13][14][15][16][17][18][19][20][21][22][23][24] between B6J and B6N, especially in metabolic traits such as glucose tolerance, insulin secretion, and body weight, have been attributed to loss of function mutations in the nicotinamide nucleotide transhydrogenase (Nnt) gene, including deletion of five exons and a missense mutation within the Nnt locus, which is harbored only in B6J mice [11,25,26]. However, conflicting data that do not correlate with Nnt mutations exist for differences in body weight between the two substrains, with reports of B6J mice weighing more than B6N mice [12-14, 27, 28], B6J mice weighing less than B6N mice [15,[29][30][31][32], and no difference in body weight between the two substrains [16,17,33]. In addition, conflicting data also exist regarding whether glucose tolerance and insulin secretion are impaired in B6J or B6N [12-19, 29, 31, 33].…”

Section: Introductionmentioning

confidence: 99%

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Metabolic differences and differentially expressed genes between C57BL/6J and C57BL/6N mice substrains

Nemoto

Kubota

2022

Preprint

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C57BL/6J (B6J) and C57BL/6N (B6N) mice are the most frequently used substrains in C57BL/6 (B6) inbred mice, serving as physiological models for in vivo studies and as background strains to build transgenic mice. However, little attention has been paid to the phenotypic differences between B6J and B6N mice, and they have often been attributed to mutations in the nicotinamide nucleotide transhydrogenase (Nnt) gene, which was found only in B6J. Nevertheless, phenotypic differences between the two that cannot be explained by Nnt mutations alone, especially in metabolic traits, indicate the presence of genetic variants associated with metabolism other than Nnt. We aimed to identify these genetic differences between B6J and B6N mice. Our results provide insights into differentially expressed genes (DEGs) in adipose tissues, skeletal muscle, liver, hypothalamus, and hippocampus, and phenotypic differences in metabolic traits between B6J and B6N. B6J mice had significantly lower body weight than B6N mice, regardless of a normal or high-fat diet. Blood insulin levels in B6J mice were significantly lower than those in B6N mice and glucose levels during dietary obesity were higher in B6J mice. Metabolic assessments revealed greater physical activity, less food intake, and higher energy expenditure in B6J mice than in B6N mice. Among the DEGs that were highly expressed in B6J mice compared to B6N mice, three genes—insulin degrading enzyme, adenylosuccinate synthase 2, and ectonucleotide triphosphate diphosphohydrolase 4—and the DEGs that had lower expression in B6J mice compared to B6N mice— Nnt , WD repeat and FYVE domain containing 1, and dynein light chain Tctex-type 1—were overlapped in all seven tissues. Our study provides insights into DEGs between B6J and B6N, which will be useful for substrain selection for mouse experiments, avoiding erroneous experimental results, and reviewing the results of studies that have used B6J, B6N, or mixed substrains.

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“…Reduction of gut microbiota diversity has been associated with obesity (7) . Gut microbiota are trillions of microorganisms present in the human gut, their weigh about 1.5 kg.…”

Section: Introductionmentioning

confidence: 99%

The Association of Gut Microbiota 16s RNA Target Region with Obesity

Soliman

Gazareen

Shehab-Eldin

et al. 2021

The Egyptian Journal of Hospital Medicine

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Background: Gut dysbiosis is strongly associated with obesity and T2DM pathogenesis. Objective: To study the possible associations of gut microbiota 16s RNA target region with obesity. Patients and Methods: A case-control study that was done on 60 subjects (20 obese subjects without diabetes, 20 obese diabetic patients, and 20 control normal subjects). This study was based on gut microbiota 16s rRNA detection by DNA extraction from fresh frozen stool samples of study groups. Gene amplification was done by PCR. Using gene sequencing to identify the gut microbiota species. Results: 2 cases were positive for 16s RNA gene in control group, the sequence of this gene was belonging to Succinivibrio dextrinosolvens. 3 cases were positive for 16s RNA gene in obese without diabetes group; all were belonging to Prevotella copri. 5 cases were positive for 16s RNA gene in obese with diabetes group; 3 cases were belonging to Prevotella copri and 2 cases were belonging to Succinivibrio dextrinosolvens. Statistically there was no significant difference among the three study groups regarding the 16s RNA gene, nor abundant organism. Although there was a significant difference between obese without DM group & lean group regarding type of organism as 100% of obese without DM group had Prevotella copri compared to 0% in group A (P = 0.04). However, there was no statistical difference between lean group and obese with DM group as (p = 0.20). In addition, there was no difference between obese group and obese with DM group (P= 0.27) regarding type of organisms. Conclusion:Obesity is associated with microbial composition alteration using 16s rRNA sequences method.

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A combination of genetics and microbiota influences the severity of the obesity phenotype in diet-induced obesity

Cited by 8 publications

References 60 publications

Substrains matter in phenotyping of C57BL/6 mice

Substrains matter in phenotyping of C57BL/6 mice

Metabolic differences and differentially expressed genes between C57BL/6J and C57BL/6N mice substrains

The Association of Gut Microbiota 16s RNA Target Region with Obesity

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