scholarly journals Mouse Phenome Database

2009 ◽  
Vol 37 (Database) ◽  
pp. D720-D730 ◽  
Author(s):  
S. C. Grubb ◽  
T. P. Maddatu ◽  
C. J. Bult ◽  
M. A. Bogue
Keyword(s):  
Mouse Phenome Database

scholarly journals Mouse Phenome Database

2013 ◽  
Vol 42 (D1) ◽  
pp. D825-D834 ◽  
Author(s):  
Stephen C. Grubb ◽  
Carol J. Bult ◽  
Molly A. Bogue
Keyword(s):  
Mouse Phenome Database

Modeling Anemia of Aging in Inbred Mouse Strains

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 3444-3444
Author(s):  
Luanne L. Peters ◽  
Shirng-wern Tsaih ◽  
Rong Yuan
Keyword(s):  
Bone Marrow ◽  
Stem Cell ◽  
Hematopoietic Stem Cell ◽  
Kidney Function ◽  
Inbred Mouse ◽  
Mouse Strains ◽  
Inbred Mouse Strains ◽  
Hematopoietic Stem ◽  
Age Related ◽  
Mouse Phenome Database

Abstract Anemia of aging is now recognized as a significant medical problem. The National Health and Nutrition Examination Survey (NHANES III) revealed a steady increase in anemia in both males and females after the age of 50. Based upon the WHO definition of anemia (<13 g/dL hemoglobin (Hgb) in men; <12 g/dL in women), ~10% of the community dwelling population ≥ 65 years of age are anemic. Underlying causes fall into three broad groups, each representing ~1/3 of cases: nutritional deficits/blood loss; inflammation, kidney disease and myelodysplasia; and unexplained anemia. Although anemia of aging is usually mild, it is no longer considered a normal part of aging. It is associated with poor health and increased vulnerability to adverse outcomes in a multitude of circumstances, placing an enormous burden on the healthcare system that will only grow as the population continues to age. As part of The Jackson Laboratory Aging Center (http://agingmice.jax.org/), we are performing an extensive phenotypic analysis of multiple traits related to aging in 32 inbred mouse strains. All data are, or will be upon completion, publicly available via the Mouse Phenome Database (MPD, www.jax.org/phenome). Complete blood counts were obtained at 6, 12, 18, and 24 months of age in 30 strains. Two-way ANOVA reveals that both strain and age significantly impact Hgb in mice. A highly significant strain-by-age interaction is also seen. Substantial inter-strain and within strain sex variability in the decline in Hgb levels with age is seen among the strains analyzed, suggesting genetic influences. Significant declines in Hgb levels in females at 18 and/or 24 months vs. 6 months occurred in 21 of the 30 strains and, in males, 17 strains. Haplotype association mapping (HAM) using a dense SNP panel identified multiple distinct, age-related loci influencing Hgb levels. For example, a locus on chromosome (Chr) 13 significantly associated with Hgb levels at 12 months of age in males was not detected even at the suggestive level at 18 months of age where two new highly significant loci emerged (Chrs 14, 17). Only two strains show a statistically significant increase in percent circulating reticulocytes with age, indicative of a proliferative anemia. Failure of a significant reticulocyte response in all other strains suggests that an age-related compromise in bone marrow function (hematopoiesis-restricted anemia) predominates in aged, anemic mice. The ratio of urinary albumin to creatinine (ACR) is commonly used as an indicator of kidney damage in mice. In females, the ACR is stable and does not rise significantly with age in the majority of strains, suggesting that declining kidney function is not a major cause of anemia of aging in female inbred mice. Significant increases in IL-6 and TNFα are seen in strains 129SvImJ, C3H/HeJ, and DBA/2J, suggesting a pro-inflammatory state. From this preliminary analysis of a large ongoing project, we can conclude: Hgb levels in mice vary significantly by strain and sex, and decline significantly with age in many strains. Other baseline hematological traits (e.g., red blood cell counts, platelet counts) likewise vary by strain, age and sex. These data are available via the Mouse Phenome Database (project Peters4). The anemia of aging seen in most strains correlates most closely with restricted hematopoiesis, as indicated by the failure of the reticulocyte count to increase in response to declining Hgb levels. There is growing evidence that decrements in hematopoietic stem cell number and function play a role in the aging process in humans. Notably, hematopoietic stem cell numbers and bone marrow cellularity data will be available on the MPD as these analyses are completed. HAM analysis suggests that distinct age-related loci influence Hgb levels in mice. In a small subset of strains, anemia of aging may reflect declining kidney function, as occurs in humans. Preliminary data suggests an increase in cytokine levels in some strains, again mimicking the aging human population. Increased IL-6 levels as a cause of anemia of aging is of particular interest due to its inhibition of hepcidin and thus iron availability. Overall, the data indicate that anemia of aging occurs in mice and models that seen in elderly human populations. Additional data including iron levels, T4, BUN, and more on aging inbred mouse strains will be posted to the MPD in the near future.

scholarly journals Collaborative Cross and Diversity Outbred data resources in the Mouse Phenome Database

2015 ◽  
Vol 26 (9-10) ◽  
pp. 511-520 ◽  
Author(s):  
Molly A. Bogue ◽  
Gary A. Churchill ◽  
Elissa J. Chesler
Keyword(s):  
Collaborative Cross ◽  
Diversity Outbred ◽  
Mouse Phenome Database

scholarly journals Analysis of the sex-specific variability of blood parameters in data sets of the Mouse Phenome Database

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Bernhard Aigner
Keyword(s):  
Genetic Background ◽  
Large Range ◽  
Phenotypic Variability ◽  
Hematological Parameters ◽  
Blood Parameters ◽  
Data Sets ◽  
Laboratory Mice ◽  
Clinical Chemical ◽  
Strain Data ◽  
Mouse Phenome Database

Abstract Objective The use of mice as animal models in biomedical research allows the standardization of genetic background and environmental conditions, which both affect phenotypic variability. As the use of both sexes in experiments is strongly recommended, sex-specific phenotypic variability is discussed with regard to putative consequences on the group size which is necessary for achieving valid and reproducible results. In this study, the sex-specific variability of 25 clinical chemical and hematological parameters which represent a comprehensive blood screen of laboratory mice, was analyzed in data sets which have been submitted to the Mouse Phenome Database. Results The overall analysis comprising all 25 clinical chemical and hematological parameters showed no evidence for substantial and robust general sex-specific variability. A large range of the ratio of the female and male coefficient of variation (CV) was found for every parameter among the respective strain data sets. This clearly demonstrated the appearance of unpredictable major interactions between genotype and environment regarding the sex-specific variability of the blood parameters analyzed.

scholarly journals Mouse Phenome Database: an integrative database and analysis suite for curated empirical phenotype data from laboratory mice

2017 ◽  
Vol 46 (D1) ◽  
pp. D843-D850 ◽  
Author(s):  
Molly A Bogue ◽  
Stephen C Grubb ◽  
David O Walton ◽  
Vivek M Philip ◽  
Georgi Kolishovski ◽  
...  
Keyword(s):  
Laboratory Mice ◽  
Phenotype Data ◽  
Mouse Phenome Database

Characterization of blood pressure and morphological traits in cardiovascular-related organs in 13 different inbred mouse strains

2004 ◽  
Vol 97 (1) ◽  
pp. 369-376 ◽  
Author(s):  
Christian F. Deschepper ◽  
Jean L. Olson ◽  
Melissa Otis ◽  
Nicole Gallo-Payet
Keyword(s):  
Blood Pressure ◽  
Body Weight ◽  
Systolic Blood Pressure ◽  
Quantitative Traits ◽  
Inbred Mouse ◽  
Left Ventricular ◽  
Mouse Strains ◽  
Genome Database ◽  
Male And Female ◽  
Mouse Phenome Database

To better understand the contributions of various genetic backgrounds to complex quantitative phenotypes, we have measured several quantitative traits of cardiovascular interest [i.e., systolic blood pressure, weight (corrected by body weight) of several cardiac compartments and adrenals and kidneys, and histological correlates for kidneys and adrenals] in male and female mice from 13 different inbred strains. We selected strains so that each major genealogical group would be represented and to conform to priorities set by the Mouse Phenome Database project. Interstrain comparisons of phenotypes made it possible to identify strains that displayed values that belonged to either the low or the high end of the interstrain variance for quantitative traits, such as systolic blood pressure, body weight, left ventricular weight, and/or adrenocortical structure. For instance, both male and female C3H/HeJ and A/J mice displayed either low systolic blood pressure or low cardiac ventricular mass, respectively, and male C57BL6/J displayed low adrenal weight. Likewise, intersex comparisons made it possible to identify phenotypic values that were sexually dimorphic for some of the same traits. For instance, female AKR/J mice had relatively higher body weight and systolic blood pressure values than their male counterparts, perhaps constituting an animal model of the metabolic X syndrome. These strain- and sex-specific features will be of value both for future genetic and/or developmental studies and for the development of new animal models that will help in the generation of mechanistic hypotheses. All data have been deposited to the Mouse Phenome Database for future integration with the Mouse Genome Database and can be further analyzed and compared with tools available on the site.

scholarly journals Mouse Phenome Database (MPD)

2011 ◽  
Vol 40 (D1) ◽  
pp. D887-D894 ◽  
Author(s):  
Terry P. Maddatu ◽  
Stephen C. Grubb ◽  
Carol J. Bult ◽  
Molly A. Bogue
Keyword(s):  
Mouse Phenome Database

scholarly journals Improving replicability using interaction with laboratories: a multi-lab experimental assessment

2021 ◽  
Author(s):  
Iman Jaljuli ◽  
Neri Kafkafi ◽  
Eliezer Giladi ◽  
Ilan Golani ◽  
Illana Gozes ◽  
...  
Keyword(s):  
Pharmacological Treatment ◽  
Large Scale ◽  
Statistical Approach ◽  
Genetically Engineered ◽  
Adjustment Factor ◽  
Experimental Assessment ◽  
Lab Experiments ◽  
New Finding ◽  
Mouse Phenome Database ◽  
Single Laboratory

AbstractPhenotyping inbred and genetically-engineered mouse lines has become a central strategy for discovering mammalian gene function and evaluating pharmacological treatment. Yet the utility of any findings critically depends on their replicability in other laboratories. In previous publications we proposed a statistical approach for estimating the inter-laboratory replicability of novel discoveries in a single laboratory, and demonstrated that previous phenotyping results from multi-lab databases can be used to derive a Genotype-by-Lab (GxL) adjustment factor to ensure the replicability of single-lab results, for similarly measured phenotypes, even before making the effort of replicating the new finding in additional laboratories.The demonstration above, however, still raised several important questions that could only be answered by an additional large-scale prospective experiment: Does GxL-adjustment works in single-lab experiments that were not intended to be standardized across laboratories? With genotypes that were not included in the previous experiments? And can it be used to adjust the results of pharmacological treatment experiments? We replicated results from five studies in the Mouse Phenome Database (MPD), in three behavioral tests, across three laboratories, offering 212 comparisons including 60 involving a pharmacological treatment: 18 mg/kg/day fluoxetine. In addition, we define and use a dimensionless GxL factor, derived from dividing the GxL variance by the standard deviation between animals within groups, as the more robust vehicle to transfer the adjustment from the multi-lab analysis to very different labs and genotypes.For genotype comparisons, GxL-adjustment reduced the rate of non-replicable discoveries from 60% to 12%, for the price of reducing the power to make replicable discoveries from 87% to 66%. Another way to look at these results is noting that the adjustment could have prevented 23 failures to replicate, for the price of missing only three replicated ones. The tools and data needed for deployment of this method across other mouse experiments are publicly available in the Mouse Phenome Database.Our results further point at some phenotypes as more prone to produce non-replicable results, while others, known to be more difficult to measure, are as likely to produce replicable results (once adjusted) as the physiological body weight is.

scholarly journals Mouse Phenome Database: a data repository and analysis suite for curated primary mouse phenotype data

2019 ◽  
Author(s):  
Molly A Bogue ◽  
Vivek M Philip ◽  
David O Walton ◽  
Stephen C Grubb ◽  
Matthew H Dunn ◽  
...  
Keyword(s):  
Laboratory Mouse ◽  
Data Repository ◽  
F1 Hybrid ◽  
Analysis Tools ◽  
Animal Data ◽  
Diversity Outbred ◽  
Phenotype Data ◽  
Primary Mouse ◽  
Environmental Agents ◽  
Mouse Phenome Database

Abstract The Mouse Phenome Database (MPD; https://phenome.jax.org) is a widely accessed and highly functional data repository housing primary phenotype data for the laboratory mouse accessible via APIs and providing tools to analyze and visualize those data. Data come from investigators around the world and represent a broad scope of phenotyping endpoints and disease-related traits in naïve mice and those exposed to drugs, environmental agents or other treatments. MPD houses rigorously curated per-animal data with detailed protocols. Public ontologies and controlled vocabularies are used for annotation. In addition to phenotype tools, genetic analysis tools enable users to integrate and interpret genome–phenome relations across the database. Strain types and populations include inbred, recombinant inbred, F1 hybrid, transgenic, targeted mutants, chromosome substitution, Collaborative Cross, Diversity Outbred and other mapping populations. Our new analysis tools allow users to apply selected data in an integrated fashion to address problems in trait associations, reproducibility, polygenic syndrome model selection and multi-trait modeling. As we refine these tools and approaches, we will continue to provide users a means to identify consistent, quality studies that have high translational relevance.

scholarly journals Identification of a blood test-based biomarker of aging through deep learning of aging trajectories in large phenotypic datasets of mice

2020 ◽  
Author(s):  
Konstantin Avchaciov ◽  
Marina P. Antoch ◽  
Ekaterina L. Andrianova ◽  
Andrei E. Tarkhov ◽  
Leonid I. Menshikov ◽  
...  
Keyword(s):  
Frailty Index ◽  
Blood Parameters ◽  
Health State ◽  
Large Set ◽  
Physical Decline ◽  
Markers Of Inflammation ◽  
Single Organism ◽  
Mouse Phenome Database ◽  
Complete Blood Counts ◽  
Biomarker Of Aging

We proposed and characterized a novel biomarker of aging and frailty in mice trained from the large set of the most conventional, easily measured blood parameters such as Complete Blood Counts (CBC) from the open-access Mouse Phenome Database (MPD). Instead of postulating the existence of an aging clock associated with any particular subsystem of an aging organism, we assumed that aging arises cooperatively from positive feedback loops spanning across physiological compartments and leading to an organism-level instability of the underlying regulatory network. To analyze the data, we employed a deep artificial neural network including auto-encoder (AE) and auto-regression (AR) components. The AE was used for dimensionality reduction and denoising the data. The AR was used to describe the dynamics of an individual mouse’s health state by means of stochastic evolution of a single organism state variable, the “dynamic frailty index” (dFI), that is the linear combination of the latent AE features and has the meaning of the total number of regulatory abnormalities developed up to the point of the measurement or, more formally, the order parameter associated with the instability. We used neither the chronological age nor the remaining lifespan of the animals while training the model. Nevertheless, dFI fully described aging on the organism level, that is it increased exponentially with age and predicted remaining lifespan. Notably, dFI correlated strongly with multiple hallmarks of aging such as physiological frailty index, indications of physical decline, molecular markers of inflammation and accumulation of senescent cells. The dynamic nature of dFI was demonstrated in mice subjected to aging acceleration by placement on a high-fat diet and aging deceleration by treatment with rapamycin.

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