Impact of C57BL/6 substrain on sex-dependent differences in mouse stroke models

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

Supplier-origin mouse microbiomes significantly influence locomotor and anxiety-related behavior, body morphology, and metabolism

The mouse is the most commonly used model species in biomedical research. Just as human physical and mental health are influenced by the commensal gut bacteria, mouse models of disease are influenced by the fecal microbiome (FM). The source of mice represents one of the strongest influences on the FM and can influence the phenotype of disease models. The FM influences behavior in mice leading to the hypothesis that mice of the same genetic background from different vendors, will have different behavioral phenotypes. To test this hypothesis, colonies of CD-1 mice, rederived via embryo transfer into surrogate dams from four different suppliers, were subjected to phenotyping assays assessing behavior and physiological parameters. Significant differences in behavior, growth rate, metabolism, and hematological parameters were observed. Collectively, these findings show the profound influence of supplier-origin FMs on host behavior and physiology in healthy, genetically similar, wild-type mice maintained in identical environments.

Substrains matter in phenotyping of C57BL/6 mice

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.

Facilitating Complex Trait Analysis via Reduced Complexity Crosses

Genetically diverse inbred strains are frequently used in quantitative trait mapping to identify sequence variants underlying trait variation. Poor locus resolution and high genetic complexity impede variant discovery. As a solution, we explore reduced complexity crosses (RCCs) between phenotypically divergent, yet genetically similar, rodent substrains. RCCs accelerate functional variant discovery via decreasing the number of segregating variants by orders of magnitude. The simplified genetic architecture of RCCs often permit immediate identification of causal variants or rapid fine-mapping of broad loci to smaller intervals. Whole-genome sequences of substrains make RCCs possible by supporting the development of array- and targeted sequencing-based genotyping platforms, coupled with rapid genome editing for variant validation. In summary, RCCs enhance discovery-based genetics of complex traits.

cGMP-dependent protein kinase I in vascular smooth muscle cells improves ischemic stroke outcome in mice

Recent works highlight the therapeutic potential of targeting cyclic guanosine monophosphate (cGMP)-dependent pathways in the context of brain ischemia/reperfusion injury (IRI). Although cGMP-dependent protein kinase I (cGKI) has emerged as a key mediator of the protective effects of nitric oxide (NO) and cGMP, the mechanisms by which cGKI attenuates IRI remain poorly understood. We used a novel, conditional cGKI knockout mouse model to study its role in cerebral IRI. We assessed neurological deficit, infarct volume, and cerebral perfusion in tamoxifen-inducible vascular smooth muscle cell-specific cGKI knockout mice and control animals. Stroke experiments revealed greater cerebral infarct volume in smooth muscle cell specific cGKI knockout mice (males: 96 ± 16 mm3; females: 93 ± 12 mm3, mean±SD) than in all control groups: wild type (males: 66 ± 19; females: 64 ± 14), cGKI control (males: 65 ± 18; females: 62 ± 14), cGKI control with tamoxifen (males: 70 ± 8; females: 68 ± 10). Our results identify, for the first time, a protective role of cGKI in vascular smooth muscle cells during ischemic stroke injury. Moreover, this protective effect of cGKI was found to be independent of gender and was mediated via improved reperfusion. These results suggest that cGKI in vascular smooth muscle cells should be targeted by therapies designed to protect brain tissue against ischemic stroke.