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Preclinical models of neurodevelopmental and psychiatric conditions often rely on inbred mouse strains like C57BL/6J (B6), which exhibit limited genetic and behavioral variability. This limitation hampers the modeling of phenotypic heterogeneity, characteristic of these conditions. Recent efforts have explored the use of multiple genetically diverse hybrid strains to address this. In this study, we examined whether one single mixed genetic mouse background, C57BL/6J;129S2/SvPas (B6;129), could simultaneously recapitulate behavioral variability and display improved phenotypes relevant to neurodevelopmental and psychiatric conditions. Compared to the inbred B6 strain, mixed B6;129 mice displayed enhanced sociability and self-grooming, two key discriminating parameters between the two mouse backgrounds and core features of such conditions, alongside a broader spectrum of individual behavioral variability. Although overall behavioral variability was comparable across backgrounds, B6;129 mice were less susceptible to the effects of chronic social isolation than their B6 counterparts. Together, these findings support that the B6;129 mixed background offers a more representative model of individual variability and behavioral traits associated with neurodevelopmental and psychiatric conditions, thereby enhancing its translational relevance in preclinical studies.

(1) Background: Backcross (BC) breeding is a key technology of crop improvement, yet its efficiency largely depends on the precise assessment of the genetic background recovery. Conventional molecular marker-assisted techniques suffer from inadequate genomic coverage or an inability to resolve true chromosomal structure. (2) Methods: To address major issues in maize BC breeding, we devised a G2H block-scanning strategy. This approach converts high-density point markers into haplotype blocks, enabling precise evaluation of the genetic background in backcross progenies. A key innovation is the CFDI, which quantifies the distribution of unrecovered fragments, allowing for visual tracking of chromosomal recombination and identification of ideal individuals with both a high genetic background recovery rate and few small fragments retention. (3) Results: We validated the accuracy and effectiveness of the G2H strategy across multiple backcross generations. Through enabling a precise \"point-to-line-to-area\" panoramic assessment of genetic background, G2H provides a powerful tool for developing ideal breeding materials with pure genetic background and minimized linkage drag. (4) Conclusions: Notably, this strategy significantly shortens the breeding cycle by 2-3 generations compared to conventional background assessment methods, thereby accelerating precision molecular design breeding in crops.

The effects of an allelic substitution at a gene often depend critically on genetic background, i.e., the genotypes at other genes in the genome. During the domestication of maize from its wild ancestor (teosinte), an allelic substitution at teosinte branched (tb1) caused changes in both plant and ear architecture. The effects of tb1 on phenotype were shown to depend on multiple background loci, including one called enhancer of tb1.2 (etb1.2). We mapped etb1.2 to a YABBY class transcription factor (ZmYAB2.1) and showed that the maize alleles of ZmYAB2.1 are either expressed at a lower level than teosinte alleles or disrupted by insertions in the sequences. tb1 and etb1.2 interact epistatically to control the length of internodes within the maize ear, which affects how densely the kernels are packed on the ear. The interaction effect is also observed at the level of gene expression, with tb1 acting as a repressor of ZmYAB2.1 expression. Curiously, ZmYAB2.1 was previously identified as a candidate gene for another domestication trait in maize, nonshattering ears. Consistent with this proposed role, ZmYAB2.1 is expressed in a narrow band of cells in immature ears that appears to represent a vestigial abscission (shattering) zone. Expression in this band of cells may also underlie the effect on internode elongation. The identification of ZmYAB2.1 as a background factor interacting with tb1 is a first step toward a gene-level understanding of how tb1 and the background within which it works evolved in concert during maize domestication.

The intestinal microbiome has emerged as an important component involved in various diseases. Therefore, the interest in understanding the factors shaping its composition is growing. The gut microbiome, often defined as a complex trait, contains diverse components and its properties are determined by a combination of external and internal effects. Although much effort has been invested so far, it is still difficult to evaluate the extent to which human genetics shape the composition of the gut microbiota. However, in mouse studies, where the environmental factors are better controlled, the effect of the genetic background was significant. The purpose of this paper is to provide a current assessment of the role of human host genetics in shaping the gut microbiome composition. Despite the inconsistency of the reported results, it can be estimated that the genetic factor affects a portion of the microbiome. However, this effect is currently lower than the initial estimates, and it is difficult to separate the genetic influence from the environmental effect. Additionally, despite the differences between the microbial composition of humans and mice, results from mouse models can strengthen our knowledge of host genetics underlying the human gut microbial variation. The gut microbiome, often defined as a complex trait, contains diverse components and its properties are determined by a combination of environmental and genetic factors. It is still difficult to evaluate, which human genetics shape the composition of the gut microbiota, however mouse models were proven to be a powerful tool for this. The purpose of this paper is to provide a current assessment of the role of human and mouse host genetics in shaping the gut microbiome composition.

Background: Obesity and type 2 diabetes (T2D) promote inflammation, increasing the risk of colorectal cancer (CRC). High-fat diet (HFD)-induced obesity is key to these diseases through biological mechanisms. This study examined the impact of genetic background on the multimorbidity of intestinal cancer, T2D, and inflammation due to HFD-induced obesity. Methods: A cohort of 357 Collaborative Cross (CC) mice from 15 lines was fed either a control chow diet (CHD) or HFD for 12 weeks. Body weight was tracked biweekly, and blood glucose was assessed at weeks 6 and 12 via intraperitoneal glucose tolerance tests (IPGTT). At the study’s endpoint, intestinal polyps were counted, and cytokine profiles were analyzed to evaluate the inflammatory response. Results: HFD significantly increased blood glucose levels and body weight, with males showing higher susceptibility to T2D and obesity. Genetic variation across CC lines influenced glucose metabolism, body weight, and polyp development. Mice on HFD developed more intestinal polyps, with males showing higher counts than females. Cytokine analysis revealed diet-induced variations in pro-inflammatory markers like IL-6, IL-17A, and TNF-α, differing by genetic background and sex. Conclusions: Host genetics plays a crucial role in susceptibility to HFD-induced obesity, T2D, CRC, and inflammation. Genetic differences across CC lines contributed to variability in disease outcomes, providing insight into the genetic underpinnings of multimorbidity. This study supports gene-mapping efforts to develop personalized prevention and treatment strategies for these diseases.

Maize yield is mostly determined by its grain size. Although numerous quantitative trait loci (QTL) have been identified for kernel-related traits, the application of these QTL in breeding programs has been strongly hindered because the populations used for QTL mapping are often different from breeding populations. However, the effect of genetic background on the efficiency of QTL and the accuracy of trait genomic prediction has not been fully studied. Here, we used a set of reciprocal introgression lines (ILs) derived from 417F × 517F to evaluate how genetic background affects the detection of QTLassociated with kernel shape traits. A total of 51 QTL for kernel size were identified by chromosome segment lines (CSL) and genome-wide association studies (GWAS) methods. These were subsequently clustered into 13 common QTL based on their physical position, including 7 genetic-background-independent and 6 genetic-background-dependent QTL, respectively. Additionally, different digenic epistatic marker pairs were identified in the 417F and 517F ILs. Therefore, our results demonstrated that genetic background strongly affected not only the kernel size QTL mapping via CSL and GWAS but also the genomic prediction accuracy and epistatic detection, thereby enhancing our understanding of how genetic background affects the genetic dissection of grain size-related traits.

Unclear or misclassified genetic background of laboratory rodents or a lack of strain awareness causes a number of difficulties in performing or reproducing scientific experiments. Until now, genetic differentiation between strains and substrains of inbred mice has been a challenge. We have developed a screening method for analyzing inbred strains regarding their genetic background. It is based on 240 highly informative short tandem repeat (STR) markers covering the 19 autosomes as well as X and Y chromosomes. Combination of analysis results for presence of known C57BL/6 substrain-specific mutations together with autosomal STR markers and the Y-chromosomal STR-haplotype provides a comprehensive snapshot of the genetic background of mice. In this study, the genetic background of 72 mouse lines obtained from 18 scientific institutions in Germany and Austria was determined. By analyzing only 3 individuals per genetically modified line it was possible to detect mixed genetic backgrounds frequently. In several lines presence of a mispairing Y chromosome was detected. At least every second genetically modified line displayed a mixed genetic background which could lead to unexpected and non-reproducible results, irrespective of the investigated gene of interest.

Background Over the past few decades, a threefold increase in obesity and type 2 diabetes (T2D) has placed a heavy burden on the health‐care system and society. Previous studies have shown correlations between obesity, T2D, and neurodegenerative diseases, including dementia. It is imperative to further understand the relationship between obesity, T2D, and cognitive deficits. Methods This investigation tested and evaluated the cognitive impact of obesity and T2D induced by high‐fat diet (HFD) and the effect of the host genetic background on the severity of cognitive decline caused by obesity and T2D in collaborative cross (CC) mice. The CC mice are a genetically diverse panel derived from eight inbred strains. Results Our findings demonstrated significant variations in the recorded phenotypes across different CC lines compared to the reference mouse line, C57BL/6J. CC037 line exhibited a substantial increase in body weight on HFD, whereas line CC005 exhibited differing responses based on sex. Glucose tolerance tests revealed significant variations, with some lines like CC005 showing a marked increase in area under the curve (AUC) values on HFD. Organ weights, including brain, spleen, liver, and kidney, varied significantly among the lines and sexes in response to HFD. Behavioral tests using the Morris water maze indicated that cognitive performance was differentially affected by diet and genetic background. Conclusions Our study establishes a foundation for future quantitative trait loci mapping using CC lines and identifying genes underlying the comorbidity of Alzheimer's disease (AD), caused by obesity and T2D. The genetic components may offer new tools for early prediction and prevention. Previous studies have shown correlations between obesity, type 2 diabetes (T2D), and neurodegenerative diseases, including dementia. A wide range of illnesses can result in dementia, including Alzheimer's disease (AD). The disorders that fall under the general category of “dementia” are caused by abnormal alterations in the brain. To counteract their significant influence on public health, it is imperative to gain an in‐depth understanding of the relationship between obesity, T2D, and cognitive deficits. We evaluate the cognitive impact of obesity and T2D induced by high‐fat diet (HFD) in collaborative cross (CC) mice. Our findings have demonstrated differences in the assessed phenotypes between the different CC lines and the C57BL/6J reference line. These findings highlight the role the host's genetic background plays in determining the degree of obesity and T2D development, as well as how it affects different organ weights and cognitive deficiencies that could worsen into AD when faced with a HFD challenge.

The recognition of β-glucans by dectin-1 has been shown to mediate cell activation, cytokine production and a variety of antifungal responses. Here, we report that the functional activity of dectin-1 in mucosal immunity to Candida albicans is influenced by the genetic background of the host. Dectin-1 was required for the proper control of gastrointestinal and vaginal candidiasis in C57BL/6, but not BALB/c mice; in fact, the latter showed increased resistance in the absence of dectin-1. The susceptibility of dectin-1-deficient C57BL/6 mice to infection was associated with defects in IL-17A and aryl hydrocarbon receptor-dependent IL-22 production and in adaptive Th1 responses. In contrast, the resistance of dectin-1-deficient BALB/c mice was associated with increased IL-17A and IL-22 production and the skewing towards Th1/Treg immune responses that provide immunological memory. Disparate canonical/noncanonical NF-κB signaling pathways downstream of dectin-1 were activated in the two different mouse strains. Thus, the net activity of dectin-1 in antifungal mucosal immunity is dependent on the host's genetic background, which affects both the innate cytokine production and the adaptive Th1/Th17 cell activation upon dectin-1 signaling.

Soybean is important throughout the world not only due to the high seed protein and oil but also owing to the seed isoflavone. To improve the isoflavone concentration in seeds, detecting and mining the stable and reliable quantitative trait loci (QTLs) and related genes in multiple environments and genetic backgrounds become more and more important. In view of this, a F 6:7 recombinant inbred line (RIL) population of 345 lines derived from a cross between Zheng 92116 and Liaodou14 (ZL) was genotyped using 1739 polymorphic SNP and 127 SSR markers in this study and was phenotyped for individual and total seed isoflavone in four environments over 2 years. In total, 48 additive QTLs, which explained 3.00–29.83% of seed isoflavone variation, were identified. Of them, eight QTLs ( qDA1_1 , qGA1_1 , qTIA1_1 , qDA1_2 , qGA1_2 , qTIA1_2 , qDA1_3 , qTIA1_3 ) with phenotypic variation explained (PVE) ranging from 14.09 to 28.59% for daidzin, genistin, and total isoflavone were located on the same region of linkage group (LG) A1. These QTLs were further verified in another RIL population derived from Zheng 92116 × Qihuang 30 (ZQ). Meanwhile, the other four overlapping QTLs on linkage group B1, which were associated with glycitin content ( qGLB1_1 , qGLB1_2 , qGLB1_3 , qGLB1_4 ) and explained 16.52 to 29.83% of phenotypic variation, were also verified using the ZQ population. Moreover, the individuals with different genotypes at the common flanking SNP markers for these QTLs on LGs A1 and B1 in the two mapping populations showed significant different isoflavone content, which further validate the QTL mapping results. And also, some candidate genes might participate in the isoflavone biosynthesis processes were found in these stable QTL regions. Thus, the novel and stable QTLs identified and verified in this study could be applied in marker-assisted selection breeding or map-based candidate genes cloning in soybean seed isoflavone genetic improvement in future.