Alternative Animal Models of Aging Research

Most research on mechanisms of aging is being conducted in a very limited number of classical model species, i.e., laboratory mouse (Mus musculus), rat (Rattus norvegicus domestica), the common fruit fly (Drosophila melanogaster) and roundworm (Caenorhabditis elegans). The obvious advantages of using these models are access to resources such as strains with known genetic properties, high-quality genomic and transcriptomic sequencing data, versatile experimental manipulation capabilities including well-established genome editing tools, as well as extensive experience in husbandry. However, this approach may introduce interpretation biases due to the specific characteristics of the investigated species, which may lead to inappropriate, or even false, generalization. For example, it is still unclear to what extent knowledge of aging mechanisms gained in short-lived model organisms is transferable to long-lived species such as humans. In addition, other specific adaptations favoring a long and healthy life from the immense evolutionary toolbox may be entirely missed. In this review, we summarize the specific characteristics of emerging animal models that have attracted the attention of gerontologists, we provide an overview of the available data and resources related to these models, and we summarize important insights gained from them in recent years. The models presented include short-lived ones such as killifish (Nothobranchius furzeri), long-lived ones such as primates (Callithrix jacchus, Cebus imitator, Macaca mulatta), bathyergid mole-rats (Heterocephalus glaber, Fukomys spp.), bats (Myotis spp.), birds, olms (Proteus anguinus), turtles, greenland sharks, bivalves (Arctica islandica), and potentially non-aging ones such as Hydra and Planaria.

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Reductionist thinking and animal models in neuropsychiatric research

Behavioral and Brain Sciences ◽

10.1017/s0140525x18001231 ◽

2019 ◽

Vol 42 ◽

Cited By ~ 1

Author(s):

Nicole M. Baran

Keyword(s):

Animal Models ◽

Mental Disorders ◽

Environmental Factors ◽

Neuropsychiatric Disorders ◽

Model Organisms ◽

Developmental Genetic ◽

Term Study ◽

Genetic And Environmental Factors ◽

Mechanisms Of Plasticity

AbstractReductionist thinking in neuroscience is manifest in the widespread use of animal models of neuropsychiatric disorders. Broader investigations of diverse behaviors in non-model organisms and longer-term study of the mechanisms of plasticity will yield fundamental insights into the neurobiological, developmental, genetic, and environmental factors contributing to the “massively multifactorial system networks” which go awry in mental disorders.

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Molecular and phenotypic analysis of rodent models reveals conserved and species-specific modulators of human sarcopenia

Communications Biology ◽

10.1038/s42003-021-01723-z ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Anastasiya Börsch ◽

Daniel J. Ham ◽

Nitish Mittal ◽

Lionel A. Tintignac ◽

Eugenia Migliavacca ◽

...

Keyword(s):

Muscle Mass ◽

Inflammatory Responses ◽

Molecular Data ◽

Model Organisms ◽

Sequencing Data ◽

Phenotypic Analysis ◽

Age Related ◽

Analogous Data ◽

Species Specific ◽

And Function

AbstractSarcopenia, the age-related loss of skeletal muscle mass and function, affects 5–13% of individuals aged over 60 years. While rodents are widely-used model organisms, which aspects of sarcopenia are recapitulated in different animal models is unknown. Here we generated a time series of phenotypic measurements and RNA sequencing data in mouse gastrocnemius muscle and analyzed them alongside analogous data from rats and humans. We found that rodents recapitulate mitochondrial changes observed in human sarcopenia, while inflammatory responses are conserved at pathway but not gene level. Perturbations in the extracellular matrix are shared by rats, while mice recapitulate changes in RNA processing and autophagy. We inferred transcription regulators of early and late transcriptome changes, which could be targeted therapeutically. Our study demonstrates that phenotypic measurements, such as muscle mass, are better indicators of muscle health than chronological age and should be considered when analyzing aging-related molecular data.

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Genome and Epigenome Editing in Mechanistic Studies of Human Aging and Aging-Related Disease

Gerontology ◽

10.1159/000452972 ◽

2016 ◽

Vol 63 (2) ◽

pp. 103-117 ◽

Cited By ~ 7

Author(s):

Cia-Hin Lau ◽

Yousin Suh

Keyword(s):

Animal Models ◽

Genetic Manipulation ◽

Logic Gate ◽

Therapeutic Interventions ◽

Human Aging ◽

Aging Research ◽

Epigenome Editing ◽

Related Disease ◽

Age Related

The recent advent of genome and epigenome editing technologies has provided a new paradigm in which the landscape of the human genome and epigenome can be precisely manipulated in their native context. Genome and epigenome editing technologies can be applied to many aspects of aging research and offer the potential to develop novel therapeutics against age-related diseases. Here, we discuss the latest technological advances in the CRISPR-based genome and epigenome editing toolbox, and provide insight into how these synthetic biology tools could facilitate aging research by establishing in vitro cell and in vivo animal models to dissect genetic and epigenetic mechanisms underlying aging and age-related diseases. We discuss recent developments in the field with the aims to precisely modulate gene expression and dynamic epigenetic landscapes in a spatial and temporal manner in cellular and animal models, by complementing the CRISPR-based editing capability with conditional genetic manipulation tools including chemically inducible expression systems, optogenetics, logic gate genetic circuits, tissue-specific promoters, and the serotype-specific adeno-associated virus. We also discuss how the combined use of genome and epigenome editing tools permits investigators to uncover novel molecular pathways involved in the pathophysiology and etiology conferred by risk variants associated with aging and aging-related disease. A better understanding of the genetic and epigenetic regulatory mechanisms underlying human aging and age-related disease will significantly contribute to the developments of new therapeutic interventions for extending health span and life span, ultimately improving the quality of life in the elderly populations.

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Subject objects

Feminist Theory ◽

10.1177/1464700111404205 ◽

2011 ◽

Vol 12 (2) ◽

pp. 119-145 ◽

Cited By ~ 41

Author(s):

Lucy Suchman

Keyword(s):

Biological Sciences ◽

Animal Models ◽

Humanoid Robot ◽

Model Organisms

The focus of my inquiry in this article is the figure of the Human that is enacted in the design of the humanoid robot. The humanoid or anthropomorphic robot is a model (in)organism, engineered in the roboticist’s laboratory in ways that both align with and diverge from the model organisms of biology. Like other model organisms, the laboratory robot’s life is inextricably infused with its inherited materialities and with the ongoing — or truncated — labours of its affiliated humans. But while animal models are rendered progressively more standardised and replicable as tools for the biological sciences, the humanoid robot is individuated and naturalised. Three stagings of human— robot encounters (with the robots Mertz, Kismet and Robota respectively) demonstrate different possibilities for conceptualising these subject objects, for the claims about humanness that they corporealise, and for the kinds of witnessing that they presuppose.

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AStrap: identification of alternative splicing from transcript sequences without a reference genome

Bioinformatics ◽

10.1093/bioinformatics/bty1008 ◽

2018 ◽

Vol 35 (15) ◽

pp. 2654-2656 ◽

Cited By ~ 5

Author(s):

Guoli Ji ◽

Wenbin Ye ◽

Yaru Su ◽

Moliang Chen ◽

Guangzao Huang ◽

...

Keyword(s):

Machine Learning ◽

Alternative Splicing ◽

Single Molecule ◽

Reference Genome ◽

De Novo ◽

Supplementary Information ◽

Model Organisms ◽

Sequencing Data ◽

Extensive Evaluation ◽

Reference Genomes

Abstract Summary Alternative splicing (AS) is a well-established mechanism for increasing transcriptome and proteome diversity, however, detecting AS events and distinguishing among AS types in organisms without available reference genomes remains challenging. We developed a de novo approach called AStrap for AS analysis without using a reference genome. AStrap identifies AS events by extensive pair-wise alignments of transcript sequences and predicts AS types by a machine-learning model integrating more than 500 assembled features. We evaluated AStrap using collected AS events from reference genomes of rice and human as well as single-molecule real-time sequencing data from Amborella trichopoda. Results show that AStrap can identify much more AS events with comparable or higher accuracy than the competing method. AStrap also possesses a unique feature of predicting AS types, which achieves an overall accuracy of ∼0.87 for different species. Extensive evaluation of AStrap using different parameters, sample sizes and machine-learning models on different species also demonstrates the robustness and flexibility of AStrap. AStrap could be a valuable addition to the community for the study of AS in non-model organisms with limited genetic resources. Availability and implementation AStrap is available for download at https://github.com/BMILAB/AStrap. Supplementary information Supplementary data are available at Bioinformatics online.

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Spontaneous and Induced Animal Models for Cancer Research

Diagnostics ◽

10.3390/diagnostics10090660 ◽

2020 ◽

Vol 10 (9) ◽

pp. 660

Author(s):

Anca Onaciu ◽

Raluca Munteanu ◽

Vlad Cristian Munteanu ◽

Diana Gulei ◽

Lajos Raduly ◽

...

Keyword(s):

Cancer Research ◽

Animal Models ◽

Mouse Models ◽

Behavioral Science ◽

Laboratory Mouse ◽

Human Tumor ◽

Large Animal ◽

Large Animal Models ◽

Oncology Research ◽

Genetically Engineered Mice

Considering the complexity of the current framework in oncology, the relevance of animal models in biomedical research is critical in light of the capacity to produce valuable data with clinical translation. The laboratory mouse is the most common animal model used in cancer research due to its high adaptation to different environments, genetic variability, and physiological similarities with humans. Beginning with spontaneous mutations arising in mice colonies that allow for pursuing studies of specific pathological conditions, this area of in vivo research has significantly evolved, now capable of generating humanized mice models encompassing the human immune system in biological correlation with human tumor xenografts. Moreover, the era of genetic engineering, especially of the hijacking CRISPR/Cas9 technique, offers powerful tools in designing and developing various mouse strains. Within this article, we will cover the principal mouse models used in oncology research, beginning with behavioral science of animals vs. humans, and continuing on with genetically engineered mice, microsurgical-induced cancer models, and avatar mouse models for personalized cancer therapy. Moreover, the area of spontaneous large animal models for cancer research will be briefly presented.

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Genome-scale metabolic network reconstruction of model animals as a platform for translational research

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2102344118 ◽

2021 ◽

Vol 118 (30) ◽

pp. e2102344118

Author(s):

Hao Wang ◽

Jonathan L. Robinson ◽

Pinar Kocabas ◽

Johan Gustafsson ◽

Mihail Anton ◽

...

Keyword(s):

Transgenic Mice ◽

Metabolic Network ◽

Model Organisms ◽

Protein Overexpression ◽

Sequencing Data ◽

Proteomics Data ◽

Gm2 Ganglioside ◽

Species Specific ◽

Specific Reactions ◽

Genome Scale

Genome-scale metabolic models (GEMs) are used extensively for analysis of mechanisms underlying human diseases and metabolic malfunctions. However, the lack of comprehensive and high-quality GEMs for model organisms restricts translational utilization of omics data accumulating from the use of various disease models. Here we present a unified platform of GEMs that covers five major model animals, including Mouse1 (Mus musculus), Rat1 (Rattus norvegicus), Zebrafish1 (Danio rerio), Fruitfly1 (Drosophila melanogaster), and Worm1 (Caenorhabditis elegans). These GEMs represent the most comprehensive coverage of the metabolic network by considering both orthology-based pathways and species-specific reactions. All GEMs can be interactively queried via the accompanying web portal Metabolic Atlas. Specifically, through integrative analysis of Mouse1 with RNA-sequencing data from brain tissues of transgenic mice we identified a coordinated up-regulation of lysosomal GM2 ganglioside and peptide degradation pathways which appears to be a signature metabolic alteration in Alzheimer’s disease (AD) mouse models with a phenotype of amyloid precursor protein overexpression. This metabolic shift was further validated with proteomics data from transgenic mice and cerebrospinal fluid samples from human patients. The elevated lysosomal enzymes thus hold potential to be used as a biomarker for early diagnosis of AD. Taken together, we foresee that this evolving open-source platform will serve as an important resource to facilitate the development of systems medicines and translational biomedical applications.

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Accurate allele frequencies from ultra-low coverage pool-seq samples in evolve-and-resequence experiments

10.1101/244004 ◽

2018 ◽

Author(s):

Susanne Tilk ◽

Alan Bergland ◽

Aaron Goodman ◽

Paul Schmidt ◽

Dmitri Petrov ◽

...

Keyword(s):

Allele Frequency ◽

Model Organism ◽

Software Tool ◽

Allele Frequencies ◽

Model Organisms ◽

Sequencing Data ◽

High Coverage ◽

Next Generation Sequencing Technology ◽

Low Coverage ◽

Pooled Samples

AbstractEvolve-and-resequence (E+R) experiments leverage next-generation sequencing technology to track the allele frequency dynamics of populations as they evolve. While previous work has shown that adaptive alleles can be detected by comparing frequency trajectories from many replicate populations, this power comes at the expense of high-coverage (>100x) sequencing of many pooled samples, which can be cost-prohibitive. Here, we show that accurate estimates of allele frequencies can be achieved with very shallow sequencing depths (<5x) via inference of known founder haplotypes in small genomic windows. This technique can be used to efficiently estimate frequencies for any number of bi-allelic SNPs in populations of any model organism founded with sequenced homozygous strains. Using both experimentally-pooled and simulated samples of Drosophila melanogaster, we show that haplotype inference can improve allele frequency accuracy by orders of magnitude for up to 50 generations of recombination, and is robust to moderate levels of missing data, as well as different selection regimes. Finally, we show that a simple linear model generated from these simulations can predict the accuracy of haplotype-derived allele frequencies in other model organisms and experimental designs. To make these results broadly accessible for use in E+R experiments, we introduce HAF-pipe, an open-source software tool for calculating haplotype-derived allele frequencies from raw sequencing data. Ultimately, by reducing sequencing costs without sacrificing accuracy, our method facilitates E+R designs with higher replication and resolution, and thereby, increased power to detect adaptive alleles.

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Subsecond dopamine fluctuations in human striatum encode superposed error signals about actual and counterfactual reward

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1513619112 ◽

2015 ◽

Vol 113 (1) ◽

pp. 200-205 ◽

Cited By ~ 92

Author(s):

Kenneth T. Kishida ◽

Ignacio Saez ◽

Terry Lohrenz ◽

Mark R. Witcher ◽

Adrian W. Laxton ◽

...

Keyword(s):

Parkinson’S Disease ◽

Decision Making ◽

Parkinson's Disease ◽

Animal Models ◽

Large Body ◽

Reward Processing ◽

Dopamine Neurons ◽

Model Organisms ◽

Mammalian Brain ◽

Prediction Errors

In the mammalian brain, dopamine is a critical neuromodulator whose actions underlie learning, decision-making, and behavioral control. Degeneration of dopamine neurons causes Parkinson’s disease, whereas dysregulation of dopamine signaling is believed to contribute to psychiatric conditions such as schizophrenia, addiction, and depression. Experiments in animal models suggest the hypothesis that dopamine release in human striatum encodes reward prediction errors (RPEs) (the difference between actual and expected outcomes) during ongoing decision-making. Blood oxygen level-dependent (BOLD) imaging experiments in humans support the idea that RPEs are tracked in the striatum; however, BOLD measurements cannot be used to infer the action of any one specific neurotransmitter. We monitored dopamine levels with subsecond temporal resolution in humans (n = 17) with Parkinson’s disease while they executed a sequential decision-making task. Participants placed bets and experienced monetary gains or losses. Dopamine fluctuations in the striatum fail to encode RPEs, as anticipated by a large body of work in model organisms. Instead, subsecond dopamine fluctuations encode an integration of RPEs with counterfactual prediction errors, the latter defined by how much better or worse the experienced outcome could have been. How dopamine fluctuations combine the actual and counterfactual is unknown. One possibility is that this process is the normal behavior of reward processing dopamine neurons, which previously had not been tested by experiments in animal models. Alternatively, this superposition of error terms may result from an additional yet-to-be-identified subclass of dopamine neurons.

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Mouse Gut Microbiome-Encoded β-Glucuronidases Identified Using Metagenome Analysis Guided by Protein Structure

mSystems ◽

10.1128/msystems.00452-19 ◽

2019 ◽

Vol 4 (4) ◽

Cited By ~ 5

Author(s):

Benjamin C. Creekmore ◽

Josh H. Gray ◽

William G. Walton ◽

Kristen A. Biernat ◽

Michael S. Little ◽

...

Keyword(s):

Protein Structure ◽

Active Site ◽

Human Microbiome ◽

Drug Efficacy ◽

Human Microbiome Project ◽

Structural Features ◽

Model Organisms ◽

Mouse Strains ◽

Sequencing Data ◽

Metagenome Analysis

ABSTRACT Gut microbial β-glucuronidase (GUS) enzymes play important roles in drug efficacy and toxicity, intestinal carcinogenesis, and mammalian-microbial symbiosis. Recently, the first catalog of human gut GUS proteins was provided for the Human Microbiome Project stool sample database and revealed 279 unique GUS enzymes organized into six categories based on active-site structural features. Because mice represent a model biomedical research organism, here we provide an analogous catalog of mouse intestinal microbial GUS proteins—a mouse gut GUSome. Using metagenome analysis guided by protein structure, we examined 2.5 million unique proteins from a comprehensive mouse gut metagenome created from several mouse strains, providers, housing conditions, and diets. We identified 444 unique GUS proteins and organized them into six categories based on active-site features, similarly to the human GUSome analysis. GUS enzymes were encoded by the major gut microbial phyla, including Firmicutes (60%) and Bacteroidetes (21%), and there were nearly 20% for which taxonomy could not be assigned. No differences in gut microbial gus gene composition were observed for mice based on sex. However, mice exhibited gus differences based on active-site features associated with provider, location, strain, and diet. Furthermore, diet yielded the largest differences in gus composition. Biochemical analysis of two low-fat-associated GUS enzymes revealed that they are variable with respect to their efficacy of processing both sulfated and nonsulfated heparan nonasaccharides containing terminal glucuronides. IMPORTANCE Mice are commonly employed as model organisms of mammalian disease; as such, our understanding of the compositions of their gut microbiomes is critical to appreciating how the mouse and human gastrointestinal tracts mirror one another. GUS enzymes, with importance in normal physiology and disease, are an attractive set of proteins to use for such analyses. Here we show that while the specific GUS enzymes differ at the sequence level, a core GUSome functionality appears conserved between mouse and human gastrointestinal bacteria. Mouse strain, provider, housing location, and diet exhibit distinct GUSomes and gus gene compositions, but sex seems not to affect the GUSome. These data provide a basis for understanding the gut microbial GUS enzymes present in commonly used laboratory mice. Further, they demonstrate the utility of metagenome analysis guided by protein structure to provide specific sets of functionally related proteins from whole-genome metagenome sequencing data.

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