Biology and Diseases of Mice
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ABSTRACT:
SUBMITTER: Jacoby R
PROVIDER: S-EPMC7149984 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Today’s laboratory mouse, Mus musculus, has its origins as the ‘house mouse’ of North America and Europe. Beginning with mice bred by mouse fanciers, laboratory stocks (outbred) derived from M. musculus musculus from eastern Europe and M. m. domesticus from western Europe were developed into inbred strains. Since the mid-1980s, additional strains have been developed from Asian mice (M. m. castaneus from Thailand and M. m. molossinus from Japan) and from M. spretus which originated from the western Mediterranean region.
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2011-04-19 | E-GEOD-21411 | biostudies-arrayexpress
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Project description:Kidney diseases manifest in progressive loss of renal function, which ultimately leads to complete kidney failure. The mechanisms underlying the origins and progression of kidney diseases are not fully understood. Multiple factors involved in the pathogenesis of kidney diseases have made the traditional candidate gene approach of limited value toward full understanding of the molecular mechanisms of these diseases. A systems biology approach that integrates computational modeling with large-scale data gathering of the molecular changes could be useful in identifying the multiple interacting genes and their products that drive kidney diseases. Advances in biotechnology now make it possible to gather large data sets to characterize the role of the genome, epigenome, transcriptome, proteome, and metabolome in kidney diseases. When combined with computational analyses, these experimental approaches will provide a comprehensive understanding of the underlying biological processes. Multiscale analysis that connects the molecular interactions and cell biology of different kidney cells to renal physiology and pathology can be utilized to identify modules of biological and clinical importance that are perturbed in disease processes. This integration of experimental approaches and computational modeling is expected to generate new knowledge that can help to identify marker sets to guide the diagnosis, monitor disease progression, and identify new therapeutic targets.
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Project description:Beginning in 1931, an inbred rabbit colony was developed at the Phipps Institute for the Study, Treatment and Prevention of Tuberculosis at the University of Pennsylvania. This colony was used to study natural resistance to infection with tuberculosis (Robertson et al., 1966). Other inbred colonies or well-defined breeding colonies were also developed at the University of Illinois College of Medicine Center for Genetics, the Laboratories of the International Health Division of The Rockefeller Foundation, the University of Utrecht in the Netherlands, and Jackson Laboratories. These colonies were moved or closed in the years to follow. Since 1973, the U.S. Department of Agriculture has reported the total number of certain species of animals used by registered research facilities (1997). In 1973, 447,570 rabbits were used in research. There has been an overall decrease in numbers of rabbits used. This decreasing trend started in the mid-1990s. In 2010, 210,172 rabbits were used in research. Despite the overall drop in the number used in research, the rabbit is still a valuable model and tool for many disciplines.
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Project description:Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS) are the most common human adult-onset neurodegenerative diseases. They are characterized by prominent age-related neurodegeneration in selectively vulnerable neural systems. Some forms of AD, PD, and ALS are inherited, and genes causing these diseases have been identified. Nevertheless, the mechanisms of the neuronal degeneration in these familial diseases, and in the more common idiopathic (sporadic) diseases, are unresolved. Genetic, biochemical, and morphological analyses of human AD, PD, and ALS, as well as their cell and animal models, reveal that mitochondria could have roles in this neurodegeneration. The varied functions and properties of mitochondria might render subsets of selectively vulnerable neurons intrinsically susceptible to cellular aging and stress and the overlying genetic variations. In AD, alterations in enzymes involved in oxidative phosphorylation, oxidative damage, and mitochondrial binding of A? and amyloid precursor protein have been reported. In PD, mutations in mitochondrial proteins have been identified and mitochondrial DNA mutations have been found in neurons in the substantia nigra. In ALS, changes occur in mitochondrial respiratory chain enzymes and mitochondrial programmed cell death proteins. Transgenic mouse models of human neurodegenerative disease are beginning to reveal possible principles governing the biology of selective neuronal vulnerability that implicate mitochondria and the mitochondrial permeability transition pore. This chapter reviews several aspects of mitochondrial biology and how mitochondrial pathobiology might contribute to the mechanisms of neurodegeneration in AD, PD, and ALS.
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Project description:This submission corresponds to peptides identified from proteomics analysis of tryptic digests of lung homogenates from C57Bl/6 mice infected with avian influenza (A/VN/1203/04). Mice were infected at 10e2, 10e3, or 10e4 pfu (n = 5 each) and sacrificed at 1, 2, 4, and 7 d post-infection. Aliquots of lung proteins were combined to create two pools corresponding to early (1 and 2 d post-infection) and late (4 and 7 d post-infection) infection and each pool was then subjected to tryptic digestion, followed by strong-cation exchange fractionation, resulting in 24 fractions each. Capillary LC-MS/MS analysis was then performed on each fraction. This submission corresponds to proteomics data collected from capillary LC-MS/MS analysis of the early infection pool. The MS/MS datasets were searched with SEQUEST using no enzyme search rules, and the peptide identification results were filtered using the following cutoffs; XCorr >= 1.6, 2.4, or 3.2 for 1+, 2+, or 3+ for fully tryptic peptides, and >= 4.3 or 4.7 for 2+ or 3+ partially tryptic or non-tryptic protein terminal peptides. Additionally, to reduce the total data size to a reasonable level, each spectrum was filtered to only include the top 100 most abundant ions in each 100 m/z-wide window. This submission is the aggregation of 24 individual LC-MS/MS analyses into one pseudo dataset. More information can be found at the following web sites: https://www.systemsvirology.org/project/home/begin.view? http://omics.pnl.gov
2012-01-17 | PRD000594 | Pride
Project description:Complex diseases are caused by a combination of genetic and environmental factors. Uncovering the molecular pathways through which genetic factors affect a phenotype is always difficult, but in the case of complex diseases this is further complicated since genetic factors in affected individuals might be different. In recent years, systems biology approaches and, more specifically, network based approaches emerged as powerful tools for studying complex diseases. These approaches are often built on the knowledge of physical or functional interactions between molecules which are usually represented as an interaction network. An interaction network not only reports the binary relationships between individual nodes but also encodes hidden higher level organization of cellular communication. Computational biologists were challenged with the task of uncovering this organization and utilizing it for the understanding of disease complexity, which prompted rich and diverse algorithmic approaches to be proposed. We start this chapter with a description of the general characteristics of complex diseases followed by a brief introduction to physical and functional networks. Next we will show how these networks are used to leverage genotype, gene expression, and other types of data to identify dysregulated pathways, infer the relationships between genotype and phenotype, and explain disease heterogeneity. We group the methods by common underlying principles and first provide a high level description of the principles followed by more specific examples. We hope that this chapter will give readers an appreciation for the wealth of algorithmic techniques that have been developed for the purpose of studying complex diseases as well as insight into their strengths and limitations.
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