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Type 2 diabetes (T2D) is a polygenic metabolic disease, characterized by high fasting blood glucose (FBG). The ability of cranberry (CRN) fruit to regulate glycemia in T2D patients is well known. Here, a cohort of 13 lines of the genetically diverse Collaborative Cross (CC) mouse model was assessed for the effect of non-dialyzable material (NDM) of cranberry extract in lowering fasting blood glucose.Eight-week-old mice were maintained on either a standard chow diet (control group) or a high-fat diet (HFD) for 12 weeks, followed by injections of intraperitoneal (IP) NDM (50 mg/kg) per mouse, three times a week for the next 6 weeks. Absolute FBG (mg/dl) was measured bi-weekly and percentage changes in FBG (%FBG) between weeks 0 and 12 were calculated.Statistical analysis showed a significant decrease in FBG between weeks 0 and 12 in male and female mice maintained on CHD. However, a non-significant increase in FBG values was observed in male and female mice maintained on HFD during the same period. Following administration of NDM during the following 6 weeks, the results show a variation in significant levels of FBG lowering between lines, male and female mice and under the different diets.The results suggest that the efficacy of NDM treatment in lowering FGB depends on host genetic background (pharmacogenetics), sex of the mouse (pharmacosex), and diet (pharmacodiet). All these results support the need for follow-up research to better understand and implement a personalized medicine approach/utilization of NDM for reducing FBG.

Background: Type 2 diabetes (T2D) is a metabolic disease with an imbalance in blood glucose concentration. There are significant studies currently showing association between T2D and intestinal cancer developments. High fat diet (HFD) plays part in the disease development of T2D, intestinal cancer, and infectious diseases through many biological mechanisms, including, but not limited to inflammation. Understanding the systems genetics of the multimorbidity of these diseases will provide an important knowledge and platform for dissecting the complexity of these diseases. Furthermore, in this study we used some machine learning (ML) models to explore more aspects of diabetes mellitus.Aims: The ultimate aim of this project is to study the genetic factors, which underline T2D development, associated with intestinal cancer in response to a HFD consumptions and oral co-infection, jointly or separately, on the same host genetic background. Materials & Methods: A cohort of 307 mice of eight different CC mouse lines in the four experimental groups was assessed. The mice were maintained on either HFD or Chow diet (CHD) for 12 weeks period, while half of each dietary group was either co-infected with oral bacteria or un-infected. Host response to a glucose load and clearance was assessed using intraperitoneal glucose tolerance test (IPGTT) at two time points (weeks 6 and 12) during the experiment period and, subsequently was translated to area under curve (AUC) values. At week 5 of the experiment, mice of group two and four were co-infected with Porphyromonas gingivalis (Pg) and Fusobacterium nucleatum (Fn) strains, three times a week, while keeping the other uninfected mice as a control group. At week 12, mice were sacrificed, small intestines and colon were extracted and subsequently the Polyp counts assessed, as well the intestine lengths and size measured. Results & conclusions: Our results have shown that there is a significant variation in polyp's number in different CC lines, with a spectrum between 2.5 and 12.8 total polyps on average. There was a significant correlation between area under curve (AUC) and intestine measurements, including polyp counts, length and size. In addition, our results have shown a significant sex-effect on polyp development and glucose tolerance ability with males more susceptible to HFD than females by showing higher AUC in the glucose tolerance test. The ML results showed that classification with random forest could reach the highest accuracy when all the attributes were used.These results provide an excellent platform for proceeding towards understanding the nature of the genes involved in resistance and rate of development of intestinal cancer and T2D induced by HFD and oral co-infection. Once obtained, such data can be used to predict individual risk for developing these diseases and to establish the genetically based strategy for their prevention and treatment.

Background Multimorbidity of intestinal cancer (IC), type 2 diabetes (T2D) and obesity is a complex set of diseases, affected by environmental and genetic risk factors. High‐fat diet (HFD) and oral bacterial infection play important roles in the etiology of these diseases through inflammation and various biological mechanisms. Methods To study the complexity of this multimorbidity, we used the collaborative cross (CC) mouse genetics reference population. We aimed to study the multimorbidity of IC, T2D, and obesity using CC lines, measuring their responses to HFD and oral bacterial infection. The study used 63 mice of both sexes generated from two CC lines (IL557 and IL711). For 12 weeks, experimental mice were maintained on specific dietary regimes combined with co‐infection with oral bacteria Porphyromonas gingivalis and Fusobacterium nucleatum, while control groups were not infected. Body weight (BW) and results of a intraperitoneal glucose tolerance test (IPGTT) were recorded at the end of 12 weeks, after which length and size of the intestines were assessed for polyp counts. Results Polyp counts ranged between 2 and 10 per CC line. The combination of HFD and infection significantly reduced (P

Introduction: Skeletal abnormalities and malocclusions have varied features that impact populations globally, impairing aesthetics and lowering life quality. The prevalence of the Skeletal Class III disease is the lowest among all angle malocclusions, with varied prevalence across nations. Environmental, genetic, and societal factors play a role in its numerous etiologies. In this study, we conducted a thorough search across the published data relating to quantitative trait loci (QTL) and the genes associated with Class III progression in humans, discussed these findings and their limitations, and proposed future directions and strategies for studying this phenotype. Methods: An inclusive search of published papers in the PubMed and Google Scholar search engines using the following terms: 1. Human skeletal Class III; 2. Genetics of Human skeletal Class III; 3. QTL mapping and gene associated with human skeletal Class III; 4. enriched skeletal Class-III-malocclusion-associated pathways. Results: Our search has found 53 genes linked with skeletal Class III malocclusion reported in humans, genes associated with epigenetics and phenomena, and the top 20 enriched pathways associated with skeletal Class III malocclusion. Conclusions: The human investigations yielded some contentious conclusions. We conducted a genome-wide association study (GWAS), an epigenetics-wide association study (EWAS), RNA-seq analysis, integrating GWAS and expression quantitative trait loci (eQTL), micro- and small-RNA, and long non-coding RNA analysis in tissues connected to skeletal Class III malocclusion phenotype in tissues connected with the skeletal phenotype. Finally, we invite regional, national, and international orthodontists and surgeons to join this effort by contributing human samples with skeletal Class III malocclusion following the accepted Helsinki ethical protocol to challenge these phenomena jointly.

The non-dialyzable material (NDM) of polyphenol-rich cranberry extract (CRE) powder (NDM-CRE) was studied for its effect of inducing body weight (BW) loss in 13 different mouse lines with well-defined genetically diverse backgrounds, named the collaborative cross (CC). From the age of 8 weeks, the mice were maintained on a high-fat diet (HFD) for 18 weeks, to induce obesity, and BW was measured biweekly. From week 12, CRE was injected intraperitoneally (IP) (50 mg kg-1) 3 times a week per mouse for a 6 week period. Statistical analysis results have shown a significant increase in body weight between week 0 and week 12; the increase in BW of 13 lines of mice on HFD was in the range of 10.41% to 68.65% for males and 9.78% to 64.74% for females. After injecting NDM-CRE extract, our analysis has shown an induced change in BW between week 12 and week 18. In males, NDM-CRE caused a significant decrease in BW of 5 out of the 13 lines in the range of -5.68% to -16.69% and a significant increase of 8.31% in BW of one male line, whereas in seven lines there was no significant decrease (-2.14% to -4.09%). In females, NDM-CRE caused a significant decrease in BW of 5 out of the 13 lines in the range of -3.90% to -11.83%, whereas in eight lines there were no significant changes in BW and it ranged between -1.50% and 4.90%. The broad-sense heritability (H2) and genetic coefficient of variation (CVg) were estimated and found to be between 0.71 and 0.81 for H2, and 0.18 and 0.24 for CVg of females and males, respectively, with respect to the efficacy of NDM-CRE on body weight reduction. Our results have shown that hosts with different genetic backgrounds respond differently to body weight increase, as well as to NDM-CRE treatment for body weight reduction. These results provide a platform for assessing more CC lines and mapping genes underlying the efficacy of the NDM-CRE treatment as a way of understanding pharmacogenomics.

When challenged with an invading pathogen, the host defense response is engaged to eliminate the pathogen (resistance) and to maintain health in the presence of the pathogen (disease tolerance). However, the identification of distinct molecular programs underpinning disease tolerance and resistance remained obscure. We exploited transcriptional and physiological monitoring across 33 mouse strains, during in vivo influenza virus infection, to identify two host-defense gene programs - one is associated with hallmarks of disease tolerance and the other with hallmarks of resistance. Both programs constitute generic responses in multiple mouse and human cell types. Our study describes the organizational principles of these programs and validates Arhgdia as a regulator of disease-tolerance states in epithelial cells. We further reveal that the baseline disease-tolerance state in macrophages is associated with the pathophysiological response to injury and infection. Our framework provides a paradigm for the understanding of disease tolerance and resistance at the molecular level.

Recent computational methods have enabled the inference of the cell-type-specificity of eQTLs based on bulk transcriptomes from highly heterogeneous tissues. However, these methods are limited in their scalability to highly heterogeneous tissues and limited in their broad applicability to any cell-type specificity of eQTLs. Here we present and demonstrate Cell Lineage Genetics (CeL-Gen), a novel computational approach that allows inference of eQTLs together with the subsets of cell types in which they have an effect, from bulk transcriptome data. To obtain improved scalability and broader applicability, CeL-Gen takes as input the known cell lineage tree and relies on the observation that dynamic changes in genetic effects occur relatively infrequently during cell differentiation. CeL-Gen can therefore be used not only to tease apart genetic effects derived from different cell types but also to infer the particular differentiation steps in which genetic effects are altered.

Oral squamous cell carcinoma (OSCC) is one of the most common human malignancies with complex etiology and poor prognosis. Although environmental carcinogens and carcinogenic viruses are still considered the main etiologic factors for OSCC development, genetic factors obviously play a key role in the initiation and progression of this neoplasm, given that not all individuals exposed to carcinogens develop the same severity of the disease, if any. Identifying genetic loci modulating OSCC risk may have several important clinical implications, including early detection, prevention and developing new treatment strategies. Due to limitations in controlled and standardized genetic studies in humans, genetic components underlying susceptibility of OSCC development remain largely unknown. A combination of quantitative trait loci mapping in mice, with complementary association studies in humans, has the potential to discover novel cancer risk loci. As of today, a limited number of genetic analyses were applied on rodent models to locate novel genetic loci associated with human OSCC. Here, we discuss the current status of the mouse models use for dissecting the genetic basis of OSCC and highlight how systems genetics analysis using mouse models, may increase our understanding of human OSCC susceptibility.

To suggest candidate genes involved in periodontitis, we combined gene expression data of periodontal biopsies from Collaborative Cross (CC) mouse lines, with previous reported quantitative trait loci (QTL) in mouse and with human genome-wide association studies (GWAS) associated with periodontitis. Periodontal samples from two susceptible, two resistant and two lines that showed bone formation after periodontal infection were collected during infection and naïve status. Differential expressed genes (DEGs) were analyzed in a case-control and case-only design. After infection, eleven protein-coding genes were significantly stronger expressed in resistant CC lines compared to susceptible ones. Of these, the most upregulated genes were MMP20 (P = 0.001), RSPO4 (P = 0.032), CALB1 (P = 1.06×10−4), and AMTN (P = 0.05). In addition, human orthologous of candidate genes were tested for their association in a case-controls samples of aggressive (AgP) and chronic (CP) periodontitis (5,095 cases, 9,908 controls). In this analysis, variants at two loci, TTLL11/PTGS1 (rs9695213, P = 5.77×10−5) and RNASE2 (rs2771342, P = 2.84×10−5) suggested association with both AgP and CP. In the association analysis with AgP only, the most significant associations were located at the HLA loci HLA-DQH1 (rs9271850, P = 2.52×10−14) and HLA-DPA1 (rs17214512, P = 5.14×10−5). This study demonstrates the utility of the CC RIL populations as a suitable model to investigate the mechanism of periodontal disease.

Background Familial adenomatous polyposis is an inherited genetic disease, characterized by colorectal polyps. It is caused by inactivating mutations in the Adenomatous polyposis coli (Apc) gene. Mice carrying a nonsense mutation in the Apc gene at R850, which is designated ApcMin/+ (Multiple intestinal neoplasia), develop intestinal adenomas. Several genetic modifier loci of Min (Mom) were previously mapped, but so far, most of the underlying genes have not been identified. To identify novel modifier loci associated with ApcMin/+, we performed quantitative trait loci (QTL) analysis for polyp development using 49 F1 crosses between different Collaborative Cross (CC) lines and C57BL/6 J-ApcMin/+mice. The CC population is a genetic reference panel of recombinant inbred lines, each line independently descended from eight genetically diverse founder strains. C57BL/6 J-ApcMin/+ males were mated with females from 49 CC lines. F1 offspring were terminated at 23 weeks and polyp counts from three sub-regions (SB1–3) of small intestinal and colon were recorded. Results The number of polyps in all these sub-regions and colon varied significantly between the different CC lines. At 95% genome-wide significance, we mapped nine novel QTL for variation in polyp number, with distinct QTL associated with each intestinal sub-region. QTL confidence intervals varied in width between 2.63–17.79 Mb. We extracted all genes in the mapped QTL at 90 and 95% CI levels using the BioInfoMiner online platform to extract, significantly enriched pathways and key linker genes, that act as regulatory and orchestrators of the phenotypic landscape associated with the ApcMin/+ mutation. Conclusions Genomic structure of the CC lines has allowed us to identify novel modifiers and confirmed some of the previously mapped modifiers. Key genes involved mainly in metabolic and immunological processes were identified. Future steps in this analysis will be to identify regulatory elements – and possible epistatic effects – located in the mapped QTL. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-07890-x.

Low bone mass and an increased risk of fracture are predictors of osteoporosis. Individuals who share the same bone-mineral density (BMD) vary in their fracture risk, suggesting that microstructural architecture is an important determinant of skeletal strength. Here, we utilized the rich diversity of the Collaborative Cross mice to identify putative causal genes that contribute to the risk of fractures. Using microcomputed tomography, we examined key structural features that pertain to bone quality in the femoral cortical and trabecular compartments of male and female mice. We estimated the broad-sense heritability to be 50–60% for all examined traits, and we identified five quantitative trait loci (QTL) significantly associated with six traits. We refined each QTL by combining information inferred from the ancestry of the mice, ranging from RNA-Seq data and published literature to shortlist candidate genes. We found strong evidence for new candidate genes, particularly Rhbdf2, whose close association with the trabecular bone volume fraction and number was strongly suggested by our analyses. We confirmed our findings with mRNA expression assays of Rhbdf2 in extreme-phenotype mice, and by phenotyping bones of Rhbdf2 knockout mice. Our results indicate that Rhbdf2 plays a decisive role in bone mass accrual and microarchitecture.

Respiratory infection caused by P. aeruginosa is one of the most critical health burdens worldwide. People affected by P. aeruginosa infection include patients with a weakened immune system, such as those with cystic fibrosis (CF) genetic disease or non-CF bronchiectasis. Disease outcomes range from fatal pneumonia to chronic life-threatening infection and inflammation leading to the progressive deterioration of pulmonary function. The development of these respiratory infections is mediated by multiple causes. However, the genetic factors underlying infection susceptibility are poorly known and difficult to predict. Our study employed novel approaches and improved mouse disease models to identify genetic modifiers that affect the severity of P. aeruginosa lung infection. We identified candidate genes to enhance our understanding of P. aeruginosa infection in humans and provide a proof of concept that could be exploited for other human pathologies mediated by bacterial infection., Human genetics influence a range of pathological and clinical phenotypes in respiratory infections; however, the contributions of disease modifiers remain underappreciated. We exploited the Collaborative Cross (CC) mouse genetic-reference population to map genetic modifiers that affect the severity of Pseudomonas aeruginosa lung infection. Screening for P. aeruginosa respiratory infection in a cohort of 39 CC lines exhibits distinct disease phenotypes ranging from complete resistance to lethal disease. Based on major changes in the survival times, a quantitative-trait locus (QTL) was mapped on murine chromosome 3 to the genomic interval of Mb 110.4 to 120.5. Within this locus, composed of 31 protein-coding genes, two candidate genes, namely, dihydropyrimidine dehydrogenase (Dpyd) and sphingosine-1-phosphate receptor 1 (S1pr1), were identified according to the level of genome-wide significance and disease gene prioritization. Functional validation of the S1pr1 gene by pharmacological targeting in C57BL/6NCrl mice confirmed its relevance in P. aeruginosa pathophysiology. However, in a cohort of Canadian patients with cystic fibrosis (CF) disease, regional genetic-association analysis of the syntenic human locus on chromosome 1 (Mb 97.0 to 105.0) identified two single-nucleotide polymorphisms (rs10875080 and rs11582736) annotated to the Dpyd gene that were significantly associated with age at first P. aeruginosa infection. Thus, there is evidence that both genes might be implicated in this disease. Our results demonstrate that the discovery of murine modifier loci may generate information that is relevant to human disease progression.

Background Host genetic background and sex, play central roles in defining the pathogenesis of type 2 diabetes (T2D), obesity and infectious diseases. Our previous studies demonstrated the utilization of genetically highly diverse inbred mouse lines, namely collaborative cross (CC), for dissecting host susceptibility for the development of T2D and obesity, showing significant variations following high‐fat (42% fat) diet (HFD). Here, we aimed to assessing the host genetic background and sex effects on T2D and obesity development in response to oral‐mixed bacterial infection and HFD using the CC lines. Materials and Methods Study cohort consists of 97 mice from 2 CC lines (both sexes), maintained on either HFD or Standard diet (CHD) for 12 weeks. At week 5 a group of mice from each diet were infected with Porphyromonas gingivalis (Pg) and Fusobacterium nucleatum (Fn) bacteria (control groups without infection). Body weight (BW) and glucose tolerance ability were assessed at the end time point of the experiment. Results The CC lines varied (P

Background: The Collaborative Cross (CC) mouse population is a valuable resource to study the genetic basis of complex traits, such as obesity. Although the development of obesity is influenced by environmental factors, underlying genetic mechanisms play a crucial role in the response to these factors. The interplay between the genetic background and the gene expression pattern can provide further insight into this response, but we lack robust and easily reproducible workflows to integrate genomic and transcriptomic information in the CC mouse population. Results: We established an automated and reproducible integrative workflow to analyse complex traits in the CC mouse genetic reference panel at the genomic and transcriptomic levels. We implemented the analytical workflow to assess the underlying genetic mechanisms of host susceptibility to diet induced obesity and integrated these results with diet induced changes in the hepatic gene expression of susceptible and resistant mice. Hepatic gene expression differs significantly between obese and non-obese mice, with a significant sex effect, where male and female mice exhibit different responses and coping mechanisms. Conclusion: Integration of the data showed that different genes but similar pathways are involved in the genetic susceptibility and disturbed in diet induced obesity. Genetic mechanisms underlying susceptibility to high-fat diet induced obesity are different in female and male mice. The clear distinction we observed in the systemic response to the high-fat diet challenge and to obesity between male and female mice points to the need for further research into distinct sex-related mechanisms in metabolic disease. This work was funded by the Operational Program ”Competitiveness, Entrepreneurship and Innovation 2014–2020″ (co-funded by the European Regional Development Fund) and managed by the General Secretariat of Research and Technology, Ministry Of Education, Research & Religious Affairs, under the project” Innovative Nanopharmaceuticals: Targeting Breast CancerStem Cells by a Novel Combination of Epigenetic and Anticancer Drugs with Gene Therapy (INNOCENT)” (7th Joint Translational Call - 2016, European Innovative Research and Technological Development Projects In Nanomedicine) of the ERA-NET EuroNanoMed II and by “BioS: Digital Skills on Computational Biology”, ΕRASMUS+, EACEA/04/2017, “KA2: Cooperation for innovation and the exchange of good practices - Sector Skills Alliances” for the development of the bioinformatic analysis pipeline, Hendrech and Eiran Gotwert Fund for studying diabetes, Wellcome Trust grants 085906/Z/ 08/Z, 075491/Z/04 and 090532/Z/09/Z; for financially supporting the initiation, breeding, genotyping and maintaining the CC mouse colony at TAU, core funding by Tel-Aviv University (TAU) for supporting staff at Iraqi’s lab, Israeli Science foundation (ISF) grants 429/09 and 1085/18, Binational Science Foundation (BSF) grant number 2015077 and German Israeli Science Foundation (GIF) grant I-63-410.20-2017 for performing the mouse assessment for T2D and technical staff, and European Sequencing and Genotyping Infrastructure (ESGI) consortium grant for conducting the RNAseq analysis.Ilona Binenbaum’s PhD thesis was supported by a scholarship from the State Scholarship Foundation in Greece (IKY) (Operational Program “Human Resources Development - Education and Lifelong Learning” Partnership Agreement (PA) 2014–2020)

Background Liver weight is a complex trait, controlled by polygenic factors and differs within populations. Dissecting the genetic architecture underlying these variations will facilitate the search for key role candidate genes involved directly in the hepatomegaly process and indirectly involved in related diseases etiology. Methods Liver weight of 506 mice generated from 39 different Collaborative Cross (CC) lines with both sexes at age 20 weeks old was determined using an electronic balance. Genomic DNA of the CC lines was genotyped with high‐density single nucleotide polymorphic markers. Results Statistical analysis revealed a significant (P

The Collaborative Cross (CC) mouse resource is a next-generation mouse genetic reference population (GRP) designed for high-resolution mapping of quantitative trait loci (QTL) of large effect affecting complex traits during health and disease. The CC resource consists of a set of 72 recombinant inbred lines (RILs) generated by reciprocal crossing of five classical and three wild-derived mouse founder strains. Complex traits are controlled by variations within multiple genes and environmental factors, and their mutual interactions. These traits are observed at multiple levels of the animals' systems, including metabolism, body weight, immune profile, and susceptibility or resistance to the development and progress of infectious or chronic diseases. Herein, we present general guidelines for design of QTL mapping experiments using the CC resource-along with full step-by-step protocols and methods that were implemented in our lab for the phenotypic and genotypic characterization of the different CC lines-for mapping the genes underlying host response to infectious and chronic diseases. © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: CC lines for whole body mass index (BMI) Basic Protocol 2: A detailed assessment of the power to detect effect sizes based on the number of lines used, and the number of replicates per line Basic Protocol 3: Obtaining power for QTL with given target effect by interpolating in Table 1 of Keele et al. (2019).

Type-2 diabetes (T2D) is a complex metabolic disease characterized by impaired glucose tolerance. Despite environmental high risk factors, host genetic background is a strong component of T2D development. Herein, novel highly genetically diverse strains of collaborative cross (CC) lines from mice were assessed to map quantitative trait loci (QTL) associated with variations of glucose-tolerance response. In total, 501 mice of 58 CC lines were maintained on high-fat (42 % fat) diet for 12 weeks. Thereafter, an intraperitoneal glucose tolerance test (IPGTT) was performed for 180 min. Subsequently, the values of Area under curve for the glucose at zero and 180 min (AUC0-180), were measured, and used for QTL mapping. Heritability and coefficient of variations in glucose tolerance (CVg) were calculated. One-way analysis of variation was significant (P

Hepatic gene expression is known to differ between healthy and type 2 diabetes conditions. Identifying these variations will provide better knowledge to the development of gene-targeted therapies. The aim of this study is to assess diet-induced hepatic gene expression of susceptible versus resistant CC lines to T2D development. Next-generation RNA-sequencing was performed for 84 livers of diabetic and non-diabetic mice of 41 different CC lines (both sexes) following 12 weeks on high-fat diet (42% fat). Data analysis revealed significant variations of hepatic gene expression in diabetic versus non-diabetic mice with significant sex effect, where 601 genes were differentially expressed (DE) in overall population (males and females), 718 genes in female mice, and 599 genes in male mice. Top prioritized DE candidate genes were Lepr, Ins2, Mb, Ckm, Mrap2, and Ckmt2 for the overall population; for females-only group were Hdc, Serpina12, Socs1, Socs2, and Mb, while for males-only group were Serpine1, Mb, Ren1, Slc4a1, and Atp2a1. Data analysis for sex differences revealed 193 DE genes in health (Top: Lepr, Cav1, Socs2, Abcg2, and Col5a3), and 389 genes DE between diabetic females versus males (Top: Lepr, Clps, Ins2, Cav1, and Mrap2). Furthermore, integrating gene expression results with previously published QTL, we identified significant variants mapped at chromosomes at positions 36–49 Mb, 62–71 Mb, and 79–99 Mb, on chromosomes 9, 11, and 12, respectively. Our findings emphasize the complexity of T2D development and that significantly controlled by host complex genetic factors. As well, we demonstrate the significant sex differences between males and females during health and increasing to extent levels during disease/diabetes. Altogether, opening the venue for further studies targets the discovery of effective sex-specific and personalized preventions and therapies.

This protocol focuses on assessing the glucose tolerance ability of the mice as an indicator for the development of impaired glucose tolerance (type 2 diabetes) at different developmental stages, possibly in normal environmental conditions or in response to different diets/environmental challenges. In this assay we use the Intraperitoneal (IP) route of administartion for the glucose solution load following 6 hours of fasting (with free acces to water). At the end of 6 hours fasting, we measure the blood glucose levels of the mouse (time zero), and thereafter perform a body weight adjusted IP glucose load, and monitor the glusoe levels at different time points for 180 minutes. Eventually, at the end of 180 minutes, we have full data of glucose levels at different time point, which generate a glucose clearance curve, presenting the mouse glucose tolerance ability. Further information on data analysis of the IPGTT results is availble at Atamni HJ. et al. (2016) High-fat-diet induced development of increased fasting glucose levels and impaired response to intraperitoneal glucose challenge in the collaborative cross mouse genetic reference population. BMC Genet. 17:10.

Single-cell expression profiling (scRNA-seq) is a rich resource of cellular heterogeneity. While profiling every sample under study would be advantageous, it is time-consuming and costly. Here we introduce Cell Population Mapping (CPM), a deconvolution algorithm in which the composition of cell types and states is inferred from the bulk transcriptome using reference scRNA-seq profiles ('scBio' CRAN R-package). Analysis of individual variations in lungs of influenza virus-infected mice, using CPM, revealed that the relationship between cell abundance and clinical symptoms is a cell-state-specific property that varies gradually along the continuum of cell-activation states. The gradual change was confirmed in subsequent experiments and was further explained by a mathematical model in which clinical outcomes relate to cell-state dynamics along the activation process. Our results demonstrate the power of CPM in reconstructing the continuous spectrum of cell states within heterogeneous tissues.

Background Animal models for neurodevelopmental disorders (NDD) generally rely on a single genetic mutation on a fixed genetic background. Recent human genetic studies however indicate that a clinical diagnosis with ASDAutism Spectrum Disorder (ASD) is almost always associated with multiple genetic fore- and background changes. The translational value of animal model studies would be greatly enhanced if genetic insults could be studied in a more quantitative framework across genetic backgrounds. Methods We used the Collaborative Cross (CC), a novel mouse genetic reference population, to investigate the quantitative genetic architecture of mouse behavioral phenotypes commonly used in animal models for NDD. Results Classical tests of social recognition and grooming phenotypes appeared insufficient for quantitative studies due to genetic dilution and limited heritability. In contrast, digging, locomotor activity, and stereotyped exploratory patterns were characterized by continuous distribution across our CC sample and also mapped to quantitative trait loci containing genes associated with corresponding phenotypes in human populations. Conclusions These findings show that the CC can move animal model studies beyond comparative single gene-single background designs, and point out which type of behavioral phenotypes are most suitable to quantify the effect of developmental etiologies across multiple genetic backgrounds. Electronic supplementary material The online version of this article (10.1186/s13229-018-0252-2) contains supplementary material, which is available to authorized users.

Li–Fraumeni syndrome (LFS), also known as the sarcoma, breast, leukemia, and adrenal gland (SBLA) syndrome, is a rare, autosomal dominant, hereditary disorder that predisposes carriers to cancer development. An early diagnosis and prognosis of LFS and identification of a novel target gene are critically important so that affected families can seek appropriate genetic counseling as well as screening for early detection and personalized therapy of cancer. We created a p53 knockdown collaborative-cross (CC) model to isolate the nuclear morphology features' genes. CC consists of recombinant inbred lines generated by reciprocal crosses between numerous founder lines. The CC animal model is a reference to apply genetic traits mapping with high-resolution phenotypes such as digital pathology features. The tumors developed in the LFS-CC Model were extracted, embedded in paraffin, sectioned, stained with Hematoxylin-eosin, and subjected to whole-slide scanning. We developed a high-throughput infrastructure for nuclear segmentation as a tool to create new LFS tumor biomarkers. These novel biomarkers are based on extracted features from 1) classical image segmentation and feature calculation tools (QuPath). 2) convolutional neural networks (CNN) from a deep learning nuclei segmentation classifier. 3) combining the two approaches by calculating features with QuPath for the nuclei detected by the CNN. The k-mean grouping of the features extracted from the digital pathology shows mediastinal (90-95% accuracy) vs. non-mediastinal LFS-lymphoma (30% – 50%) diagnosis. K-mean grouping of the nuclear morphological features and survival analysis (Kaplan-Meier) demonstrate the prognosis capacity of the digital morphology groups (seven significantly different survival curcles with p-values ranging between Citation Format: Ilan Tsarfaty, Ayman Iraqi, Or Megides, Nizan Cauzmer, Hila Shacham, Doruk Barokas, Fuad Iraqi, Dvora Kidron. AI and digital pathology based on nucleus morphology for diagnosis, prognosis, and morphological-gene isolation Li Fraumeni as a model [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-012.

Triple-negative breast cancer (TNBC) is a type of aggressive breast cancer lacking the expression of estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor receptor-2 (HER-2). TNBC represents a heterogeneous subtype of breast cancer that carries a poorer prognosis. There remains a need to identify novel driver and modifier genes of TNBC, representing targets to treat the disease. MET tyrosine kinase is the receptor for hepatocyte growth factor/scatter factor (HGF/SF). MET overexpression and constitutively activated mutation promotes tumor growth, cell motility and metastases, making MET an ideal target for anti-metastatic precision therapy. In mice carrying MET constitutive activation mutation (METmut), the gene initiates tumorigenicity associated with inherited driver modifier genes (IDMGs). The Collaborative Cross (CC) animal model consists of recombinant inbred lines generated by reciprocal crosses between numerous founder lines. It is a reference to apply genetic traits mapping with high-resolution phenotypes such as radiomic features. In this study, we established a new machine learning prognostic model of overall survival (OS) of METmut-induced mammary carcinoma. QTL (Quantitative Trait Locus) mapping analysis was used to identify candidate genes that may contribute to intra-tumor heterogenicity and OS. Two CT images cohorts were included: METmut-FVB/N and METmut-CC mice that developed mammary carcinoma (n=13 and n=15, respectively). Each segmented tumor was divided into 3D cubes, and radiomic features were extracted from each cube using a novel in-house tool. We performed Principle Component Analysis (PCA) and cluster analysis (k-means) on the radiomic features to produce cube cluster groups with similar radiomic characteristics, such as tumor region intensity and morphology. We then assessed each cluster group's proportion per tumor – the cluster proportion - as a radiomic phenotype for QTL. QTL analysis identified loci in chromosomes #6 and #15 that code for new radiomics morphological candidate genes that may alter MET signaling and reduce OS. To analyze tumor heterogenicity, median and standard deviation of cubes PCA values were calculated per tumor and used as covariates in Cox regression against OS. Four out of the eight covariates were significantly correlated with OS (P < 0.01, Xi 21.59 on 8 df). Using PCA and cluster analysis in radiomic feature analysis can create a unique prognostic signature for breast cancer patients. CT-radiomics can be used to isolate tumor genes, substituting tumor biopsies in patients with breast cancer metastases to brain and providing novel targets for precision therapy. Citation Format: Ilan Tsarfaty, Eran Dotan, Michal Bloom, Judith Horev, Fuad Iraqi. AI and CT-radiomics as a tool for breast cancer prognosis and radiomics-gene isolation: Activated MET induced mammary carcinoma as a model [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-067.

Non-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease in the western world, with spectrum from simple steatosis to non-alcoholic steatohepatitis, which can progress to cirrhosis. NAFLD developments are known to be affected by host genetic background. Herein we emphasize the power of collaborative cross (CC) mouse for dissecting this complex trait and revealing quantitative trait loci (QTL) controlling hepatic fat accumulation in mice. 168 female and 338 male mice from 24 and 37 CC lines, respectively, of 18-20 weeks old, maintained on standard rodent diet, since weaning. Hepatic fat content was assessed, using dual DEXA scan in the liver. Using the available high-density genotype markers of the CC line, QTL mapping associated with percentage liver fat accumulation was performed. Our results revealed significant fatty liver accumulation QTL that were specifically, mapped in females. Two significant QTLs on chromosomes 17 and 18, with genomic intervals 3 and 2 Mb, respectively, were mapped. A third QTL, with a less significant P value, was mapped to chromosome 4, with genomic interval of 2 Mb. These QTLs were named Flal1-Flal3, referring to Fatty Liver Accumulation Locus 1-3, for the QTLs on chromosomes 17, 18, and 4, respectively. Unfortunately, no QTL was mapped with males. Searching the mouse genome database suggested several candidate genes involved in hepatic fat accumulation. Our results show that susceptibility to hepatic fat accumulations is a complex trait, controlled by multiple genetic factors in female mice, but not in male.

The scientific community is increasingly concerned with cases of published “discoveries” that are not replicated in further studies. The field of mouse behavioral phenotyping was one of the first to raise this concern, and to relate it to other complicated methodological issues: the complex interaction between genotype and environment; the definitions of behavioral constructs; and the use of the mouse as a model animal for human health and disease mechanisms. In January 2015, researchers from various disciplines including genetics, behavior genetics, neuroscience, ethology, statistics and bioinformatics gathered in Tel Aviv University to discuss these issues. The general consent presented here was that the issue is prevalent and of concern, and should be addressed at the statistical, methodological and policy levels, but is not so severe as to call into question the validity and the usefulness of model organisms as a whole. Well-organized community efforts, coupled with improved data and metadata sharing, were agreed by all to have a key role to play in identifying specific problems and promoting effective solutions. As replicability is related to validity and may also affect generalizability and translation of findings, the implications of the present discussion reach far beyond the issue of replicability of mouse phenotypes but may be highly relevant throughout biomedical research.