model organisms literature review

• Search Menu
• Sign in through your institution
• Advance Articles
• Perspectives
• Knowledgebase and Database Resources
• Nobel Laureates Collection
• China Virtual Outreach Webinar
• Neurogenetics
• Fungal Genetics and Genomics
• Multiparental Populations
• Genomic Prediction
• Plant Genetics and Genomics
• Genetic Models of Rare Diseases
• Genomic Data Analyses In Biobanks
• Genetics of Bacteria
• Why Publish
• Author Guidelines
• Submission Site
• Open Access Options
• Full Data Policy
• Self-Archiving Policy
• About Genetics
• About Genetics Society of America
• Editorial Board
• Early Career Reviewers
• Guidelines for Reviewers
• Advertising & Corporate Services
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

Literature cited.

• < Previous

Model Organisms: Nature’s Gift to Disease Research

• Article contents
• Figures & tables
• Supplementary Data

Model Organisms: Nature’s Gift to Disease Research, Genetics , Volume 214, Issue 2, 1 February 2020, Pages 233–234, https://doi.org/10.1534/genetics.120.303050

• Permissions Icon Permissions

The diversity of life on our planet is astounding. If that were the result of processes unique to each organism, understanding the basis of life would have been unreachable, because it would have required studying an almost limitless variety of species.

But we’ve known for over 150 years that all living things are related, giving us confidence that what we learn about one organism will likely inform how others are born, live, and die. Thus, we learned that DNA is the carrier of inheritance in all organisms from the study of a simple bacterium ( Avery et al. 1944 ). We learned much about the nature of the code embedded in DNA from studies of even simpler bacteriophages ( Crick et al. 1961 ). We learned universal rules of inheritance from studies of a fruit fly ( Bridges 1916 ). And we learned fundamental principles of how cells grow and divide by studying unicellular yeasts ( Hartwell 1991 ). Studies of relatively few select organisms brought us the answer to Schroedinger’s (1944) question: “What is Life?”.

Fundamental research on “model organisms” continues to reveal the workings of life. And this research also helps us understand how things go wrong. Human disease phenotypes caused by a defective gene are often recapitulated in model organisms when the orthologous gene is made similarly defective, providing an experimentally tractable model of the disease. Numerous examples of how studies in model organisms have informed human disease, in some cases leading to development of a treatment, are nicely described in a review published in this journal ( Wangler et al. 2017 ).

Pursuit of the genetic basis of disease increased dramatically over the last 10 years owing to sharply reduced costs of genome sequencing. Variants potentially responsible for rare and undiagnosed diseases are now identified by whole-genome or whole-exome sequencing. But thousands of variants are identified. Which one causes the disease? Model organisms often point to the answer by offering candidate genes whose function can be tested.

The potential for model organism research to speed diagnosis, mechanistic understanding, and treatment of human disease took a great leap forward with the organization of networks that connect clinicians with model organism geneticists. The Undiagnosed Disease Network (UDN) in the United States connects several clinical centers to, among other resources, a Model Organism Screening Center for functional exploration of candidate genes and variants ( Ramoni et al. 2017 ). In Canada, the Rare Diseases Models and Mechanisms (RDMM) Network has taken a novel distributive approach that identifies and seeds collaborations between model organism researchers and clinicians who have discovered a new disease gene variant. A Commentary published in the February 2020 issue of The American Journal of Human Genetics describes how the RDMM has, over the past five years, connected model organism geneticists with clinicians through a national registry, and summarizes outcomes that include validation of disease gene discovery, identification of possible therapies, and success in obtaining subsequent funding ( Boycott et al. 2020 ).

It’s fair to say that progress in identifying disease genes has been spectacular. Disease-gene associations are now being discovered at a rate of 300 per year (∼5 per week!); ∼5400 diseases caused by variants in 3800 genes have already been solved. There are estimated to be ∼5000 rare diseases for which disease gene variants have yet to be discovered. It’s not unreasonable to expect that essentially all Mendelian disease genes will be identified over the next 10 years.

But disease gene discovery is just the beginning of the quest to improve the lives of those living with genetic diseases. Understanding the function of those genes is necessary to develop rational approaches to disease prevention, management, and treatment. We are far from understanding the function of humans’ ∼20,000 genes, or how they are organized and regulated in pathways and networks. Due to their experimental power and the evolutionary conservation of gene function, research on model organisms will surely continue to provide answers far into the future. The success of the RDMM and UDN should attract more model organism geneticists to human disease research, resulting in more disease genes being the focus of dedicated research programs.

The Genetics Society of America strives to support and promote that work. The Allied Genetics Conference (TAGC) this April will showcase the ways model organisms are informing human disease. GENETICS has featured several such stories ( e.g. , Brooks et al. 2014 ; Hamza et al. 2015 ; Pena et al. 2017 ; Lansdon et al. 2018 ), and we look forward to publishing more of them. And there are sure to be many more of these stories, because model organisms are nature’s gift to science ( Brenner 2003 ) that keeps on giving.

Avery , O T , C M   Macleod , and M   McCarty , 1944   Studies on the chemical nature of the substance inducing transformation of pneumococcal types: Induction of transformation by a desoxyribonucleic acid fraction isolated from Pneumococcus type III.   J. Exp. Med.   79 : 137 – 158 . 10.1084/jem.79.2.137

Google Scholar

Boycott , K M , P M   Campeau , H E   Howley , P   Pavlidis , S   Rogic  et al.  , 2020   The Canadian Rare Diseases Models and Mechanisms (RDMM) Network: connecting understudied genes to model organisms.   Am. J. Hum. Genet.   https://doi.org/10.1016/j.ajhg.2020.01.009

Brenner , S , 2003  Nobel Lecture: Nature’s Gift to Science. Chembiochem. 4: 683–687. https://onlinelibrary.wiley.com/doi/abs/10.1002/cbic.200300625

Bridges , C B , 1916   Non-disjunction as proof of the chromosome theory of heredity.   Genetics   1 : 1 – 52 .

Brooks , S S , A L   Wall , C   Golzio , D W   Reid , A   Kondyles  et al.  , 2014   A novel ribosomopathy caused by dysfunction of RPL10 disrupts neurodevelopment and causes X–linked microcephaly in humans.   Genetics   198 : 723 – 733 . 10.1534/genetics.114.168211

Crick , F H , L   Barnett , S   Brenner , and R J   Watts-Tobin , 1961   General nature of the genetic code for proteins.   Nature   192 : 1227 – 1232 . 10.1038/1921227a0

Hamza , A , E   Tammpere , M   Kofoed , C   Keong , J   Chiang  et al.  , 2015   Complementation of yeast genes with human genes as an experimental platform for functional testing of human genetic variants.   Genetics   201 : 1263 – 1274 . 10.1534/genetics.115.181099

Hartwell , L H , 1991   Twenty-five years of cell cycle genetics.   Genetics   129 : 975 – 980 .

Lansdon , L A , B W   Darbro , A L   Petrin , A M   Hulstrand , J M   Standley  et al.  , 2018   Identification of Isthmin 1 as a novel clefting and craniofacial patterning gene in humans.   Genetics   208 : 283 – 296 . 10.1534/genetics.117.300535

McGurk , L , A   Berson , and N M   Bonini , 2015   Drosophila as an in vivo model for human neurodegenerative disease.   Genetics   201 : 377 – 402 . 10.1534/genetics.115.179457

Pena , I A , Y   Roussel , K   Daniel , K   Mongeon , D   Johnstone  et al.  , 2017   Pyridoxine-dependent epilepsy in zebrafish caused by Aldh7a1 deficiency.   Genetics   207 : 1501 – 1518 . 10.1534/genetics.117.300137

Ramoni , R B , J J   Mulvihill , D R   Adams , P   Allard , E A   Ashley  et al.  , 2017   The undiagnosed diseases network: accelerating discovery about health and disease.   Am. J. Hum. Genet.   100 : 185 – 192 . 10.1016/j.ajhg.2017.01.006

Schroedinger , E , 1944   What Is Life? The Physical Aspect of the Living Cell , Cambridge University Press , Cambridge, UK .

Google Preview

Wangler , M F , S   Yamamoto , H-T   Chao , J E   Posey , M   Westerfield  et al.  , 2017   Model organisms facilitate rare disease diagnosis and therapeutic research.   Genetics   207 : 9 – 27 . 10.1534/genetics.117.203067

Email alerts

Citing articles via.

• Recommend to Your Librarian
• Advertising and Corporate Services
• Journals Career Network

Affiliations

• Online ISSN 1943-2631
• Copyright © 2024 Genetics Society of America
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

• Position statement
• Open access
• Published: 07 May 2021

Model organisms contribute to diagnosis and discovery in the undiagnosed diseases network: current state and a future vision

• Dustin Baldridge   ORCID: orcid.org/0000-0002-6027-6020 1   na1 ,
• Michael F. Wangler 2 , 3 , 4 , 5   na1 ,
• Angela N. Bowman 6 , 7 ,
• Shinya Yamamoto   ORCID: orcid.org/0000-0003-2172-8036 2 , 4 , 5 , 8 ,
• Undiagnosed Diseases Network ,
• Tim Schedl 7 , 9 ,
• Stephen C. Pak 1 ,
• John H. Postlethwait 10 ,
• Jimann Shin 6 ,
• Lilianna Solnica-Krezel 6 , 7 ,
• Hugo J. Bellen 2 , 4 , 5 , 8 , 11 &
• Monte Westerfield 10  

Orphanet Journal of Rare Diseases volume  16 , Article number:  206 ( 2021 ) Cite this article

11k Accesses

46 Citations

15 Altmetric

Metrics details

Decreased sequencing costs have led to an explosion of genetic and genomic data. These data have revealed thousands of candidate human disease variants. Establishing which variants cause phenotypes and diseases, however, has remained challenging. Significant progress has been made, including advances by the National Institutes of Health (NIH)-funded Undiagnosed Diseases Network (UDN). However, 6000–13,000 additional disease genes remain to be identified. The continued discovery of rare diseases and their genetic underpinnings provides benefits to affected patients, of whom there are more than 400 million worldwide, and also advances understanding the mechanisms of more common diseases. Platforms employing model organisms enable discovery of novel gene-disease relationships, help establish variant pathogenicity, and often lead to the exploration of underlying mechanisms of pathophysiology that suggest new therapies. The Model Organism Screening Center (MOSC) of the UDN is a unique resource dedicated to utilizing informatics and functional studies in model organisms, including worm ( Caenorhabditis elegans ), fly ( Drosophila melanogaster ), and zebrafish ( Danio rerio ), to aid in diagnosis. The MOSC has directly contributed to the diagnosis of challenging cases, including multiple patients with complex, multi-organ phenotypes. In addition, the MOSC provides a framework for how basic scientists and clinicians can collaborate to drive diagnoses. Customized experimental plans take into account patient presentations, specific genes and variant(s), and appropriateness of each model organism for analysis. The MOSC also generates bioinformatic and experimental tools and reagents for the wider scientific community. Two elements of the MOSC that have been instrumental in its success are (1) multidisciplinary teams with expertise in variant bioinformatics and in human and model organism genetics, and (2) mechanisms for ongoing communication with clinical teams. Here we provide a position statement regarding the central role of model organisms for continued discovery of disease genes, and we advocate for the continuation and expansion of MOSC-type research entities as a Model Organisms Network (MON) to be funded through grant applications submitted to the NIH, family groups focused on specific rare diseases, other philanthropic organizations, industry partnerships, and other sources of support.

The future of human genetics

Even though the human genome was sequenced in 2003, the era of functional genomics is just beginning. The deployment of next-generation sequencing revealed a staggering number of variants across individuals, with each human genome containing an average of more than 3 million single nucleotide variants when compared to the reference sequence [ 1 , 2 ]. Of the approximately 20,000 human genes, only ~ 4000 are currently linked to monogenic disease and/or rare disease in Online Mendelian Inheritance in Man (OMIM) [ 3 , 4 ] and Orphanet [ 5 ].

Importantly, although a single rare disease might impact only a few individuals, as a whole, rare diseases affect up to 25 million people in the US alone according to the Centers for Disease Control and Prevention (CDC) [ 6 ]. Bamshad et al. proposed that there are 6000–13,000 additional disease genes that remain to be identified for Mendelian traits and rare diseases [ 7 ]. Thus, disease gene discovery will continue for many years.

Patients with rare diseases typically have long, expensive, and frustrating diagnostic odysseys, and research with model organisms can significantly shorten their journeys by identifying causative genetic variants and disease mechanisms. The major goal of the NIH-funded MOSC, as an essential component of the UDN, is to provide experimental results to help evaluate a diagnosis, thus concluding the diagnostic odyssey. Such genetic discovery efforts typically lead to the identification of new disease genes. Although uncovering the genetic underpinnings of rare diseases for diagnosis has inherent value (e.g., for reproductive planning), it also provides significant opportunities to study rare disease biology. Such findings can contribute to a better understanding of basic biological systems and pathways, leading to development of treatments and cures and linking rare conditions with more common disease mechanisms [ 8 , 9 ].

The value of model organism screening centers

The purpose of the MOSC is to use genetic approaches in non-mammalian model organisms to evaluate the hypothesis that specific genes and variants identified in patients enrolled in the UDN are likely to cause patient clinical phenotypes. The UDN is an NIH Common Fund program arising from the earlier intramural NIH Undiagnosed Diseases Program (UDP), and now consists of a network of academic medical centers dedicated to solving medical mysteries [ 10 ]. Through the use of in-depth clinical evaluations and exome or genome sequencing and analysis, numerous patients with challenging and medically complex conditions are able to obtain a molecular diagnosis through participation in the UDN [ 11 ]. In many cases, the identification of an ‘n = 1’ potentially pathogenic variant from sequencing alone does not provide sufficient evidence that the variant is indeed causative. A subset of these cases may be solved by identifying several similarly affected patients who harbor putative pathogenic variants in the same gene, a process that is facilitated by platforms like the Matchmaker Exchange [ 12 ]. Unfortunately, this process is costly, slow, and frequently unsuccessful. Therefore, due to the recurring need for functional assessment of putative pathogenic variants, the UDN established the MOSC during Phase I of the program (September 2015 to August 2018), and expanded the MOSC in Phase II (September 2018–July 2022) [ 13 ].

The initial MOSC structure included a bioinformatics component, a Drosophila melanogaster ( Drosophila ; Fly) Core, and a Zebrafish Core. We note that the term “Center” is used for the overall structure of the MOSC, and the term “Core” is used for individual model organism teams due to the administrative structure specified in the NIH funding opportunity announcement. However, activities conducted by the MOSC Cores are significantly more advanced than those typically conducted by traditional research core facilities. The bioinformatics component analyzes specific genes and variants submitted, including the use of public databases of “control” individuals with respect to monogenic disease, such as ExAC and gnomAD [ 14 , 15 ], and Mendelian disease databases, such as the Centers for Mendelian Genomics (CMG), to look for matching cases and variants [ 4 , 12 , 16 ]. These searches are integrated with specific searches throughout the literature and across model organism, gene, protein, and protein structure databases to identify tools and reagents available for potential studies in a given organism. Based on the vast amount of time spent on bioinformatic searches and the need for computational tools to help prioritize model organism studies, the Phase I MOSC developed a robust integrated platform called MARRVEL (Model organism Aggregated Resources for Rare Variant ExpLoration; http://marrvel.org/ ) that is freely available online and now widely used [ 17 ]. MARRVEL supports integration of more than 20 online database searches into a single search [ 18 , 19 ].

Through extensive model organism studies and the use of MARRVEL, the MOSC provided key contributions and new scientific insights during Phase I of the UDN. During this period, 239 variants in 183 genes from 122 UDN probands were submitted to the MOSC (Fig.  1 ). Of these, 59 genes were studied in the Fly Core and 16 in the Zebrafish Core, including two genes studied in both cores. The Phase I MOSC provided in-depth biological data for 19 genes that led directly to diagnosis (Table 1 ), with studies for additional genes ongoing. These discoveries included novel gene discoveries, phenotypic expansions, new biological insights, novel therapeutic targets, the ability to solve cases with only 1 or 2 patients, and extrapolation of rare undiagnosed disease mechanisms to common diseases [ 20 ].

Overview of Phase I activity of the Model Organism Screening Center (MOSC) of the Undiagnosed Diseases Network (UDN). A total of 239 variants were submitted for consideration from the 907 cases evaluated at Phase I UDN Clinical Sites. States with Phase I Clinical Sites are marked in red. After bioinformatic analysis on all submissions, 59 genes were selected for study by the Fly Core and 16 genes by the Zebrafish Core. Gene names in red indicate novel disease gene candidates, whereas those in black represent proposed phenotypic expansions, according to the assessment by the clinical sites at the time of submission to the MOSC. Gene names that are in bold and underlined indicate cases where data from the MOSC directly led to a diagnosis (see Table 1 for details)

The success of the Phase I MOSC led to an expansion in Phase II with an allocation of additional UDN resources to functional studies. The current MOSC incorporates a Worm ( C. elegans ) Core, a Fly ( Drosophila ) Core, and two Zebrafish Cores. The current MOSC uses a two-step evaluation system: an initial review process to screen variants primarily based on human genetics information, followed by Core level reviews to evaluate their appropriateness for specific model organism studies. As of December 2020, the Phase II MOSC has processed 143 variants in 109 genes for 108 UDN cases and assigned 60 genes for modeling in one of the three model organisms.

MOSC discovery—historical outcomes and costs

Table 1 lists gene discoveries from the UDN MOSC in chronological order of publication and illustrates the breadth of disease phenotypes investigated. Each discovery has the potential to change medicine for that individual gene, disease, and patient and provides direct benefits outlined below. Estimating costs for each discovery is challenging due to wide variability from case to case, but based on Phase I data, an effort like the MOSC can be expected to deliver approximately six high impact gene discoveries per year for $900,000 total, or $150,000 per gene discovery. This estimate accounts for the cost associated with the discovery itself, as well as studies of other candidate disease genes for patients. Note that some efforts do not lead to diagnosis and discovery; for example, because each case typically has multiple candidate genes but typically only one is studied, failure to reveal a phenotype in a model organism may be due to study of a candidate that was not the causal gene. We note that a team-based approach increases the efficiency and lowers the cost of gene discovery through optimization of resource allocation and avoiding duplication of effort.

In addition to providing evidence that supports diagnoses, the MOSC also generates tools of significant value for further studies, such as the bioinformatic MARRVEL platform [ 17 ] and valuable in vivo reagents for the scientific community. This includes model organism mutants with loss of function alleles, lines with the patient variant(s) knocked into the endogenous gene, and tools to exogenously express human cDNA. The MOSC makes research organism reagents available to the international scientific community through NIH-supported public stock centers ( Caenorhabditis Genetics Center, https://cgc.umn.edu ; Bloomington Drosophila Stock Center, https://bdsc.indiana.edu ; the Drosophila Genomics Research Center, dgrc.bio.indiana.edu; Zebrafish International Resource Center, https://zebrafish.org ) so that they can be used for further diagnoses, in-depth mechanistic studies, and proof-of-concept translational and preclinical trial experiments.

Benefits of undiagnosed disease gene discovery in general

Although the main goal of the UDN is to provide a diagnosis, disease gene discovery also contributes significantly to the lives of patients and their families. Gene discovery helps by: (1) ending the “diagnostic odyssey” of individual patients, reducing unnecessary diagnostic tests, offering prenatal diagnosis options for some families, and improving medical care for individual patients; (2) leading to diagnoses for patients outside of the UDN as diagnostic laboratories incorporate published new disease gene discoveries, including those from the UDN, into their sequencing interpretation and reanalysis processes; (3) facilitating the formation of social media groups, including family advocacy and support organizations that arise from the more precise molecular diagnoses; (4) enabling the future development of precision therapies that target the underlying molecular basis of rare genetic disorders and more common diseases, and (5) driving an interest in and a positive public perception of genomic research for human health, leading to greater public interest and understanding of genomics and rare and undiagnosed disease. While there is clear economic value to the patient and family members that have received a diagnosis based on functional studies performed by the MOSC, it is extremely difficult, if not impossible, to calculate the precise value of these benefits. Achieving a diagnosis prevents the added expenses for patients who would have sought evaluation from additional specialists until they get an answer, and such answers may not be found for many more years in the future if the patient’s condition is novel. In addition, the work by the MOSC has value beyond the individual UDN patient or family because the new disease gene discoveries and phenotypic expansions discovered by the MOSC (Table 1 ) accelerate diagnoses of patients that are not part of the UDN but who have the same genetic condition, thereby reducing costs for many families and third party payers.

Benefits of the existing MOSC structure

The MOSC has been a productive center, and its existing structure provides an efficient mechanism for validation and further characterization of disease genes and variants using model organisms. In contrast, private companies, even those few that produce model organism reagents, do not offer model organism phenotyping. They also do not generally collaborate directly with clinicians, typically because these commercial laboratories do not have the collective expertise needed. Distributing work across model organism laboratories requires a central effort to organize and coordinate activities as well as frequent and open communication among the Model Organism Cores. For example, review of the clinical phenotype can have an impact on which model organism laboratory is best suited to study particular phenotypes or genetic pathways. The two key aspects of (1) multidisciplinary teams (Fig.  2 and Table 2 ) and (2) collaborative communication contribute to the high rate of gene discovery by the MOSC.

Schematic of the relationships among teams that make up the Model Organism Screening Center (MOSC). Functions of the MOSC and Clinical Sites are noted in blue. Arrows symbolize the collaborative communication among teams

A multidisciplinary team effort is the first and most important factor for success, because this collaboration brings together many groups spanning different areas of biological science. These benefits include: (1) bridging clinical/medical terms and model organism jargon, (2) coming to a consensus on the current understanding of the genes of interest in the context of medical genetics and model organism genetics, (3) understanding genome sequence analysis and potential pitfalls associated with DNA testing, and (4) having the unique expertise needed to develop and characterize model organism reagents that are robust and reliable to produce data relevant to the patient.

Collaboration and frequent bidirectional communication (represented by arrows in Fig.  2 ) is the second key feature of the MOSC. The MOSC uses a centralized system for some aspects of communication, called the UDN Gateway, which is an online system developed by the UDN Coordinating Center to facilitate data sharing and communication. The current MOSC relies on a network of expert clinical centers that are actively engaged in rare and undiagnosed diseases research, and whose participation is essential for the MOSC discovery process. Clinicians at the UDN Clinical Sites provide clinical information about the participant, explain the rationale for prioritizing candidate genes and variants that may contribute to disease phenotypes, and submit one to five genes/variants per case for further consideration. Clinical Sites submit variants to the MOSC via a built-in feature in the Gateway. Clinical sites and the MOSC teams attend a monthly call of the Model Organisms Working Group (MOWG), which facilitates communication about submissions, expected phenotypes, and model organism assignments. The MOSC also returns decisions via the Gateway to the Clinical Sites, including which model organism is appropriate for studying a specific variant, and eventually, results from model organism studies.

One of the key bidirectional communications is the interaction between Clinical Sites and the MOSC Bioinformatics Team. When Clinical Sites submit candidate variants to the Bioinformatics Team, the latter requests any additional necessary information from the Clinical Site to assess whether the gene/variant candidates are likely to be the cause of the disease before the submissions are passed on to informatics teams of each Model Organism Core. The Bioinformatics Team communicates the results of variant assessments and returns variants that are not appropriate for MOSC model organism work to the Clinical Site. The MOSC has a wide variety of genetic tools, but there are nonetheless specific variant types that are difficult to tackle using model organisms (Table 3 ). Currently, complex multigene interactions and environmental triggers are considered lower priority due to the scale of experimental approach that would be required to test these hypotheses. However, it is possible that new tools and resources generated in the future could be incorporated to assess these proposed mechanisms of disease in model organisms.

Another important set of interactions occur among the MOSC Bioinformatics Team, the Model Organism Cores, and the Clinical Sites. The information from the Clinical Sites and the bioinformatics analysis are communicated to the Cores, and in a further step, model organism experts evaluate each variant in the context of their specific model, leading to proposals for experimental work. The Bioinformatics Team and Model Organism Core teams communicate back and forth about the specific genes including homology, human genetics evidence, and hypothesized genetic mechanisms in preparation for the regular MOWG calls with the Clinical Sites. In addition, each Model Organism Core communicates directly with the other Model Organism Cores on a regular basis and during the MOWG calls, which allows the larger MOSC team as a whole to understand how each model could potentially contribute to the diagnosis of a particular undiagnosed patient. It is important to select the best model on a case-by-case basis, allowing optimization of resources for each case. Determining the Model Organism Core that is best suited to obtain diagnostic or biological insight is also part of a bidirectional dialogue involving the Clinical Sites and the MOSC Bioinformatics Team, as are discussions within and between the Model Organism Cores during the MOWG calls. Also, the cores that ultimately begin experiments on a gene communicate directly with the Clinical Site that submitted the case, so that information from the model can be conveyed to the clinicians as new data become available so that action plans can be developed.

In summary, some unique hallmarks of the MOSC are robust, bidirectional, and open communication, as well as interdisciplinary collaboration among basic scientists, human geneticists, and clinicians through regular individual meetings and monthly working group calls. This communication is an essential component of the MOSC and a key scientific justification for a MOSC structure. These multiple levels and mechanisms of communications between individuals in separate scientific fields and with complementary expertise ensure that everyone understands expectations and progress in data generation, reducing inefficiencies and potential work at cross-purposes. Beyond the UDN, the MOSC also engages members of other model organism research communities to apply the benefits of different models, dovetail efforts, and share best practices. These features could not be provided if the teams and lines of communication outlined above did not exist. In conclusion, this effort embodies a truly collaborative spirit.

Benefits of the bioinformatic efforts of the MOSC

A robust system of informatics for quality control of potential variants is integral to MOSC operations and discoveries. In the current phase of the UDN, we have identified Human Genome Variation Society (HGVS) nomenclature issues in more than 20% of variants submitted to the MOSC. Examples include mismatch between cDNA and genomic coordinates, incorrect representations of short insertions or deletions, and mistakes when manually transcribing information from clinical genetic reports. Even though these submissions have come from top medical genetics centers, the presence of such a high error rate means that the MOSC needs a robust system to perform variant analysis and quality control. A bioinformatics team of integrated physician scientists, clinicians, bioinformaticians, rare and undiagnosed diseases researchers, geneticists, and clinical DNA testing experts facilitate this work. Providing this interdisciplinary resource for clinicians, who usually do not have model organism expertise, is a cost-effective and time-efficient mechanism for assessing the appropriateness of candidate variants for experimental analysis in each of the model systems available, discussed in detail below. The current system involves researchers at Baylor College of Medicine, Washington University in St. Louis, and the University of Oregon who analyze variants for (1) variant nomenclature, (2) minor allele frequencies in public and CMG databases, (3) gene-based metrics and prediction scores from public genomic resources, such as gnomAD, and (4) variant-based in silico prediction scores. The bioinformatics team also examines the clinical scenario as presented by the Clinical Site, studies gene information using OMIM and other databases, and confirms a shared understanding of the clinical question motivating the proposal for model organism studies. This team then communicates these data to the Model Organism Cores for further analysis, and likewise facilitates communication between the Cores and the Clinical Sites. The MARRVEL resource, discussed above, is a crucial tool designed to provide rapid access to the data needed to evaluate a candidate gene and variant for model organism studies, and has saved many hours of research time by conducting searches using this integrative tool versus separate searches across multiple databases. Bioinformatic analyses also leverage the Alliance of Genome Resources (AGR) [ 37 ], which aims to catalog human and model organism data, when reviewing model organism gene expression and functional information.

Benefits of each model organism in the MOSC

The MOSC utilizes the experimental and genetic tools of three premier genetic model organisms: worm ( Caenorhabditis elegans ), fly ( Drosophila melanogaster ), and zebrafish ( Danio rerio ). Indeed, numerous Nobel prizes in Physiology and Medicine have been awarded to non-mammalian model organism researchers for their insights into human biology [ 13 , 38 ]. Recent examples include Nobel prizes in Physiology and Medicine for circadian rhythms using fruit flies (2017), innate immunity using flies (2011), RNA interference in worm (2006), apoptosis in worm (2002), and embryonic development in flies (1995). Importantly, these are awards for contributions to medicine resulted directly from model organism studies including those organisms utilized by the MOSC.

Caenorhabditis elegans ( C. elegans , a 1 mm-long nematode worm) is a major research organism for studies of animal cell and developmental biology [ 39 ]. Research in the worm has provided key insights into human biology in areas such as apoptosis, cell migration, nervous system wiring, aging, microRNAs, and insulin-like signaling, because of the conservation of molecular machines (e.g. spliceosome), intracellular pathways (e.g. autophagy), intercellular signaling pathways (e.g. Notch signaling), and multicellular processes (e.g. basement membrane biology) across animal biology [ 40 ]. The use of C. elegans in studies of human disease has defined new Mendelian conditions [ 41 ], uncovered phenotypic expansion [ 42 ], and provided the first key mechanistic understanding for some diseases (e.g., spinal muscular atrophy [ 43 ]). The high efficiency of knocking in patient missense variants into the orthologous C. elegans gene (which is uniformly done for the UDN MOSC cases), the short four-day generation time, the large body of acquired knowledge, and the publicly available biological reagents (WormBase, https://www.wormbase.org/ ) facilitate rapid functional studies of candidate disease gene variants. Such investigations can provide information on the pathogenicity of the patient variant, evidence in support of the mode of inheritance including the nature of dominance (e.g., antimorph vs. hypermorph), insight into disease mechanisms, and possible routes to treatment.

Drosophila melanogaster (fruit fly) has been used as a model organism to understand fundamental principles of genetics, developmental biology, immunity, and neuroscience for the past century [ 44 , 45 ]. In the last two decades, Drosophila has become an important model system to dissect and understand the molecular mechanisms that underlie human diseases. This is in part because ~ 75% of human genes shown to cause human diseases were found to be conserved in Drosophila when the first genome-wide survey was conducted on ~ 1000 genes registered in OMIM [ 46 ]. Of the ~ 4000 human disease-linked genes currently displayed in OMIM, ~ 85% have homologs in flies. Considering that ~ 65% of protein coding genes are conserved between fly and human [ 17 , 47 ], the data suggest that genes that are conserved between these species have a higher likelihood of causing genetic diseases in human. In addition to being used as a tool to dissect mechanisms of both common and rare diseases, and to explore potential therapeutic avenues, the fly has emerged as a critical tool to interpret variants of uncertain significance found in patients [ 20 ]. This is because state-of-the-art techniques to manipulate the Drosophila genome allow researchers to engineer flies in many different ways [ 48 , 49 , 50 ]. By integrating techniques to knock-out, knock-in, knock-down, or overexpress endogenous and exogenous proteins in a spatiotemporally controlled manner, fly biologists can quickly unravel the biological function of a gene of interest in vivo. One can further test whether the function of the gene is conserved between flies and human through gene-replacement experiments in which the human cDNA is used to functionally rescue loss-of-function alleles of the fly gene. In this paradigm, the ability of the human reference cDNA to rescue the fly mutations allows the testing of variants from undiagnosed patients in a relatively short (~ 6 months) time frame [ 44 ]. Detailed description and discussion of these strategies employed by the UDN MOSC fly core can be found in Bellen et al. [ 20 ]. All of this work is made possible due to rich public resources that support fly research, including a centralized database that actively collects and curates the literature (FlyBase, http://flybase.org/ ), public stock centers that distribute > 80,000 different strains of flies (Bloomington Drosophila Stock Center, https://bdsc.indiana.edu ) and > 1,000,000 DNA clones ( Drosophila Genomics Resource Center, https://dgrc.bio.indiana.edu/ ) supported by the NIH. Genes and variants found in an undiagnosed patient that are confirmed to be deleterious can be further studied in flies to identify disease mechanisms or test FDA-approved drugs that may be beneficial for the patient through high-throughput screens. This approach has already been effective in identifying several personalized treatments that can be returned to the bedside in a short timeframe [ 9 , 32 , 51 ].

The zebrafish ( Danio rerio ) has emerged as a premier organism to study human biology [ 52 ]. Being a vertebrate, zebrafish have almost all of the same organs and systems as humans, but are much smaller and develop much faster, thus supporting rapid studies at organismal, cellular, and subcellular resolution. Powerful techniques allow efficient generation, recovery, and analysis of mutations affecting genes that regulate developmental patterning, organogenesis, physiology, and behavior. It is easy to study gene function by injecting synthetic RNAs into early zebrafish embryos, generating transgenic zebrafish, or by altering gene function with genome editing technologies, such as the CRISPR/Cas9 system [ 53 , 54 ]. The genome has been sequenced, and 71% of all human genes and 82% of human-disease related genes have zebrafish orthologs [ 55 ]. Targeted gene knock-out technology is robust and is the most frequent approach used by the UDN MOSC fish core, although some patient-specific knock-in models have also been generated. Further, studying zebrafish duplicates of human genes facilitates dissection of multi-function genes due to the evolutionary process of sub-functionalization that occurred after the teleost genome duplication [ 56 , 57 ]. Advanced public resources facilitate these increasingly sophisticated experimental approaches in zebrafish, including a centralized database that actively collects and curates the literature (The Zebrafish Information Network, http://zfin.org ) and public stock centers that distribute mutant and transgenic zebrafish strains and molecular reagents (The Zebrafish International Resource Center, https://zebrafish.org ), both of which are supported by the NIH. Because organs, cell types, and gene functions are well conserved across vertebrates, analysis of zebrafish mutants provides insights into gene functions in other vertebrates, including humans [ 58 , 59 ]. Zebrafish are used widely to validate candidate human disease genes and elucidate the molecular mechanisms and pathophysiology of disease [ 27 , 28 , 33 , 60 , 61 , 62 ] as well as for drug discovery [ 63 ].

Often the tissue or organismal phenotype studied in worm or fly, and occasionally in zebrafish, does not resemble the phenotype of disruption of the orthologous human gene. Nevertheless, variant-induced dysfunction and genetic mechanisms can be assessed in model organisms because underlying molecular, cell biological, and genetic pathways are conserved. The term ‘phenolog’ stands for orthologous phenotypes and has been used when different phenotypes are observed from the disruption of orthologous genes [ 64 ], which occurs due to diverged organismal biology of the different species. Two examples of the use of phenologs in gene-variant assessment are wing defects in flies versus aortic abnormalities in humans, which both involve disrupted Notch signaling [ 65 ] and egg laying defects in worm versus craniosynostosis in humans caused by missense variants in Twist family genes [ 41 ]. The rapid assessment of the relevant phenolog for a missense variant in worms or flies provides functional information supporting a timely diagnosis. It also provides a simple phenotypic readout to dissect the underlying pathogenic genetic mechanism and supports the utility of more involved studies of cell and molecular mechanism.

The MOSC considers multiple factors when determining which model organism is most appropriate for a UDN case, including gene and variant evolutionary conservation and availability of reagents. If multiple organisms are appropriate for a single case, then the MOSC generally recommends only the simplest and fastest model organism in order to maximize the use of limited resources and to provide information to aid in a diagnosis as quickly as possible. The worm and fly lineage diverged from the human lineage before the fish and human lineages diverged, but these invertebrates allow rapid functional characterization of variants of interests and further probe into molecular mechanisms of disease. In some situations where clinical phenotypes relate to vertebrate-specific organs or cell types, zebrafish may be preferred and recommended. Another consideration is whether the proposed variant is a missense or protein truncating variant, which is straight forward for all models, or whether a patient-specific knock-in is necessary which is much more rapid in worms and flies. These decisions can be quite complex and require extensive communication among the specific Model Organism Cores and the Clinical Sites to weigh competing issues so that all parties can have a shared understanding of the organism-specific benefits and limitations of the proposed experimental work as well as the intended goal of the studies. In summary, the overall endeavor of undiagnosed disease gene discovery, the structure and multidisciplinary nature of the MOSC, and each of the Model Organism Cores all contribute to the successful diagnosis of undiagnosed patients.

Vision for the future: proposal for a model organisms network (MON, formerly MOSC)

We propose sustaining and updating the MOSC through the creation of a Model Organisms Network (MON), which would include: (1) a central MOSC-like structure that is focused on providing functional information for timely diagnosis, and (2) deep mechanistic studies that extend to a larger network of researchers.

For (1), a MOSC-like structure, we envision continuation of a multidisciplinary central MON team, including the communication elements detailed above. We note that such an effort may extend beyond the needs or priorities of any single NIH institute or center, in keeping with the observation that most undiagnosed patients are medically complex and have multiple organ systems affected, and that undiagnosed diseases afflict both children and adults. This funding model would sustain and expand a team and system with similar concepts, structures, and components as the current MOSC, but would also integrate additional specialists in the model organism research field who have the expertise to pursue mechanistic and translational studies related to newly discovered disease genes or specific clinical phenotypes. In addition, we envision the central MON could garner additional support from philanthropy and rare disease family groups to fund mechanistic studies that not only extend and deepen discoveries from currently NIH-funded gene discovery programs like the UDN and Centers for Mendelian Genomics (CMG), but also include the many other historically identified disease genes where the underlying disease mechanism is not currently known. These mechanistic studies could focus on genes under study in the MON and on solving undiagnosed diseases.

For (2), mechanistic studies, we envision that studies by the MON would extend to examining pathways and therapeutics and constitute “deep dives” into individual genes and variants. Such studies have traditionally been funded through disparate investigator-initiated ‘R’ grant mechanisms. Although these mechanistic studies have thus far not been a formal part of the MOSC, they have been undertaken for some diseases in parallel to the ongoing diagnosis efforts using alternative funding sources, including administrative supplements, non-NIH grants, and institutional as well as philanthropic support [ 26 , 30 , 34 ]. We argue that the future network needs to balance ongoing disease gene discovery with deep mechanistic studies. These mechanistic studies could leverage the animal disease models and other tools generated by the MON, and could be undertaken by any external investigator with a robust approach, expertise, and reagents for investigating the gene, pathway, and disease uncovered by the MON. We suggest that these principles could establish a framework that could inform efforts beyond the current MOSC and could in principle incorporate other organisms, other funding mechanisms, and other functional approaches.

We envision that genetic variants will continue to be submitted by clinicians in various research initiatives to the future MON and will flow through the following pipeline: (1) sequencing and bioinformatics, (2) pathogenicity studies in one of the Model Organism Cores, that includes three organism cores outlined and justified above (diagnosis), and (3) mechanistic studies in select cases (Fig.  3 ). The major changes we are proposing from the current MOSC workflow, and which we describe in more detail below, include the potential for expanded sources of variant submissions and the interface with deeper mechanistic studies.

Overview of information flow and activities, including original evaluation of the patient and candidate variant identification to model organism (MO) studies. Outputs include pathogenicity assessment and, in some cases, a “deeper dive” into the underlying mechanism. Proposed Model Organism Network (MON) activities include identifying disease mechanisms for additional genes through collaborations with other model organism experts. The red box indicates potential interactions with ongoing gene discovery programs such as the Centers for Mendelian Genomics (CMG) or its future equivalents

Specific components of the proposed MON

Robust teams and communication.

The future effort of the MON will require a multidisciplinary team, as well as regular communication, as exemplified by current MOWG calls and in-person meetings of the UDN. This process includes the need for a set of academic clinical centers focused on undiagnosed diseases that continue to study the most challenging cases and apply state-of-the-art genomic sequencing technologies to identify candidate variants for submission to the MON. Other needs are a MON bioinformatics team familiar with human DNA testing and sequencing data analysis to ensure quality control, and, of course, Model Organism Cores with broad biological expertise in the newest genetic technologies in each organism. Informatics efforts will become even more important because a future MON could potentially include a wider set of variant sources, leading to a greater need to harmonize data and assess each variant with consistent quality control measures.

Variant sources from academic centers with excellence in undiagnosed diseases

In our vision of the future MON, we foresee an expansion of variant sources beyond the current UDN Clinical Sites. However, we emphasize that committed academic centers, such as the current UDN sites, are necessary to ensure successful, high quality clinical evaluations and sequencing, which are the starting points for identification of candidate disease genes and variants. We anticipate an ongoing need for timely functional studies; given the estimated 6000–13,000 additional Mendelian disease genes remaining to be identified [ 7 ] and the persistently falling costs of sequencing, patients with variants in candidate genes will continue to be identified regularly in the near future. Based on our experience, it will be necessary to have a certification process to identify sites that follow accepted practices for ensuring high quality submissions, including both clinical information and DNA sequences. We also envision that over time, sites could be educated through training modules, and that this process could lead to certification of new sites. Also, as noted above, the participation of experienced clinical teams actively engaged in identifying patients with variants in potentially novel disease genes is essential for the success of the MON. In addition to including existing UDN sites, we also propose that sources of variant submissions for MON analysis be expanded to include variants proposed by selected entities that are not presently part of the UDN. One logical choice would be for the MON to potentially collaborate with the highly successful NIH-funded CMG [ 66 ], and/or the future Mendelian Genomic Research Centers. The CMG has made more than 600 novel disease gene discoveries over the past eight years [ 66 ], and the current MOSC has already been collaborating and publishing with CMG researchers [ 23 , 67 , 68 , 69 , 70 ]. However, an additional ~ 1200 “Tier 2” genes are not yet definitive disease genes and these cases would directly benefit from functional evaluation by the MON [ 66 ]. In addition, it may be reasonable for the MON to partner with other groups pursuing gene discovery for rare and undiagnosed diseases, including the NIH-funded Rare Diseases Clinical Research Network (RDCRN) [ 71 ], as noted below.

MARRVEL and artificial intelligence platforms

Informatic tools provide rapid access to the data needed to evaluate a candidate gene and variant or model organism studies. The ability of computer-based methods, including artificial intelligence and deep learning, to predict the pathogenicity of variants of uncertain significance is likely to improve in coming years. The MARRVEL resource will continue to expand and add additional databases, pathogenicity prediction programs, and widgets to its platform. This type of effort is essential for the future MON. We envision that the MON will both support the development of these tools and integrate them into its workflows as they become robust, to identify appropriate candidate variants efficiently and chose the most effective model organism for variant validation.

Model organism core teams and additional approaches

Based on the justification above and our past experience, we suggest that, at minimum, the MON will include Worm, Fly, and Fish Cores following the current structure of the MOSC. These models have proven the most successful, rapid, and cost-effective for studying undiagnosed diseases and will provide the most mechanistic insight, given the experience and increasingly sophisticated experimental tools that have been and are being developed in each system within a reasonable budget. Although the three proposed organisms have outstanding ability to model a large proportion of human variants quickly and inexpensively, cases may exist in which none of the organisms are suitable, or supplementation with human cell culture studies would provide unique information not possible with worm, fly, or fish. Based on submissions to the current MOSC, up to 10% of proposed variants in candidate human disease genes do not have sufficient evolutionary conservation to be studied in any of the three MOSC model organisms (especially when including synonymous, intronic or splicing, and UTR variants). In addition, some questions related to specific cell types affected in the patient might benefit from the use of patient biopsy or derived cells. The MON should have ways to incorporate or establish collaborations that provide mouse models, cellular transfection models, patient derived cells (e.g., fibroblasts), and human pluripotent stem cell-derived models of relevant cell or organ types whenever necessary.

The current MOSC does not take direct advantage of the mouse ( Mus musculus ) because large-scale functional studies using mice were cost-prohibitive at the time that the NIH conceived the MOSC idea (~ 2015). Considering the value of investigations using mice in the context of rare diseases [ 72 , 73 ], the MOSC has been closely working with the Knockout Mouse Phenotyping Program (KOMP2, https://commonfund.nih.gov/komp2 ) and International Mouse Phenotyping consortium (IMPC, https://www.mousephenotype.org ) to leverage the phenotypic data of null mutant animals in the informatic pipeline for variant prioritization. Due to rapid advancements in CRISPR-based gene knock-in and knock-out technologies in mouse and other species [ 74 , 75 ], there is no reason for the MON to exclude any organism that can be genetically manipulated and phenotyped within a reasonable timeframe and cost.

Another complementary approach, but also beyond the current scope of the MOSC, is the use of patient-derived induced pluripotent stem cells (iPSCs), which can be differentiated into disease-relevant cell types and organoids to attempt to recapitulate the patient’s condition [ 76 , 77 ]. Some current challenges inherent in the use of iPSCs include the ongoing need to develop and disseminate standardized differentiation protocols, the significant cost and time required to generate cell-types of interest, and the high degree of variability that can be observed from cell line to cell line. If highly reliable and reproducible protocols and functional assays relevant to the patient’s condition can be established with reasonable cost and timeline, such approaches will be highly synergistic with studies carried out in intact organisms, especially to test genetic variants that lack model organism orthologs and that are in human-specific non-coding elements.

Challenges to scalability

As we describe above, much progress has been made in the development of model organism research as a tool for rare disease gene discovery. However, several challenges remain before these processes can become scalable and as easy to execute as some existing fee-for-service tests, such as exome or genome sequencing. First, disease modeling requires significant understanding of model organism biology and genetics to tailor the experimental design and analysis to the specific gene, the specific variant(s), and patient-specific clinical information in the context of the particular focal research organism. For example, to uncover a variant-specific disease mechanism, even when the null phenotype in the model organism is known, research organism experimental design often must be modified based on patient genetics, human population information, the possibility of incomplete penetrance/expressivity, and the possibility of a gain-of-function or dominant negative effect. Second, due to these complexities, the bulk of this research requires PhD-level personnel with sufficient expertise and experience to navigate the existing information, determine feasibilities of the model organism, design an experimental strategy to support pathogenicity, perform the experiments, and then bring the discovery to publication. It can be challenging to identify qualified research scientists to carry out this work in a sustainable fashion.

Dual goals of diagnosis and mechanism

We envision NIH support for deep dives into mechanisms that would extend beyond the MON program and which would be supported by multiple NIH institutes, perhaps through competitive ‘R’ grants. Importantly, such support would also enable external model organism experts to join the MON. We are advocating for support for two distinct and important activities that will be carried out by the future MON: (1) providing rapid diagnosis and (2) uncovering disease mechanisms. To expand further, Activity (1), the diagnosis of undiagnosed diseases patients, involves using model organism experiments to provide data that solve a medical mystery for a patient in a timely manner; and Activity (2) the mechanistic understanding of previously undiagnosed diseases, includes understanding the underlying biology of disease, using rare diseases to understand common diseases, and preclinical identification and testing of therapeutics, which is a more in-depth effort.

The key feature of the components and activities of the current MOSC that distinguish it from other efforts is that they target a particular undiagnosed patient to provide timely information for diagnosis. In addition to the defining contribution of the MOSC towards diagnosis (i.e., by providing evidence for or against pathogenicity of a specific variant), the future MON should also make significant contributions towards understanding the mechanistic basis for how a variant contributes to disease pathophysiology. Although mechanistic studies are not warranted in all cases, we strongly believe that they are a powerful extension of MOSC diagnostic work on new and unstudied disease genes. Moreover, MOSC researchers generate animal models, acquire relevant expertise, and are thus well-positioned to carry out such mechanistic studies. In addition, because mechanistic studies require time, expertise, and resources, investigators outside of the central MON team should have the opportunity to drive these mechanistic studies. Given the large number of known disease genes with unknown mechanisms and expertise existing in laboratories outside of the MON, we envision that these future collaborations with experts in particular genes and pathways would become part of a larger NIH effort to uncover genetic disease mechanisms, in which the future MON might be only one of the contributors. These investigator-driven mechanistic studies could be proposed using any model organism or cellular or biochemical system, and combinations thereof. We envision that these studies could be supported by specifically targeted R01, R03, or R21 mechanisms. The Coordinating Center of the UDN has been exploring the benefit of providing funding ($150,000 per proposal) to recruit external researchers with expertise in specific genes and pathways, and these efforts have indeed facilitated the mechanistic understanding of disease mechanisms ( https://undiagnosed.hms.harvard.edu/research/funding-opportunities/ ). The scientific justification for this dual set of goals (diagnosis and mechanism) is that the work on diagnosis must progress in a timely manner to provide answers for patients and their families. However, at the same time, more in depth biological studies, albeit slower, must also be supported to translate these discoveries to therapeutics and to common disease biology. Furthermore, even when initial studies do not support the conclusion that nominated variants cause the particular patients’ diseases, such negative results are valuable for the diagnostic mission because they prompt the clinical group to consider other candidate genes and variants. In addition, this work defines the functions of the investigated genes and variants, which might fit a different undiagnosed disease, especially for previously unstudied genes.

Communication of the MON with other NIH-supported variant modeling efforts

Although the MOSC and future MON are unique frameworks within which to model human variants, we recognize a number of other ongoing efforts, both nationally and internationally. Some NIH-funded efforts include the Eunice Kennedy Shriver Intellectual and Developmental Disabilities Research Centers (EKS-IDDRCs, NICHD), the Rare and Atypical Diabetes Network (RADIANT, NIDDK), the Accelerating Medicines Partnership Type 2 Diabetes Consortium (AMP TD2, NIDDK), and the Rare Disease Clinical Research Network (RDCRN, NCATS). We believe these groups would benefit from ongoing and open communication with the future MON and the UDN to ensure that patients are reviewed by the most appropriate group and to avoid duplication of efforts.

Consideration of Canadian and other international model organism approaches for rare disease

The UDN MOSC is a centralized system of several laboratories with broad expertise, knowledge, and techniques working collaboratively to solve many cases together. An alternative model is for many individual laboratories with gene-specific expertise to work on particular cases in which their genes of interest are the prime candidate of the undiagnosed condition. The Rare Diseases: Models & Mechanisms Network (RDMM) in Canada is a national network of model organism researchers and clinicians that has been using this “distributive model” of functional studies for the past four years [ 78 ]. The UDN MOSC signed a Memorandum of Understanding with the RDMM in 2016 to exchange data, knowledge, and expertise to support each other's mission.

In the RDMM approach, clinicians from around the country submit genes and variants of interest together with the clinical description of the patient. A group of clinicians that form the Clinical Advisory Committee (CAC) reviews these submissions and assesses the quality of candidate variants. The CAC passes information about appropriate gene variants to a group of biologists and bioinformaticians that form the Scientific Advisory Committee (SAC). Next, the SAC searches an internal database that contains information about model organism researchers in Canada and their expertise, to match the clinician and model organism researchers and encourage collaboration. The clinician and model organism researcher can then make specific research plans and co-submit a short research proposal back to the SAC. The SAC reviews these applications and decides whether or not to fund the project. Successful applicants receive a CAD$25,000 grant for one year to pursue the project. Interest in the project is extremely high: as of early 2020, 88% (543) of model organism laboratories across Canada had enrolled in the database, and RDMM had funded 105 projects related to 87 genes. The network published 20 peer reviewed research articles including new disease gene discoveries, phenotypic expansions of known disease genes, or mechanistic studies of known rare diseases. Due to its success in Canada, funding agencies in Japan (IRUD/J-RDMM) ( https://j-rdmm.org/indexEn.html ), Australia (AFGN) ( https://www.functionalgenomics.org.au/ ), and Europe (Solve-RD) ( http://solve-rd.eu/rdmm-europe/ ) have developed RDMM-like networks over the past two years.

Although the RDMM has been successful, there are some limitations to this model, including potential difficulties in establishing a new collaboration for each disease gene studied and the relatively limited funds and project period provided per gene. In addition, although the RDMM system is very effective in studying variants and genes for which some knowledge about their biological functions is available, genes without any in vivo studies in any pre-existing model organism tend to be left unstudied due to lack of a specific researcher with expertise. The centralized MOSC system provides flexibility and resources for researchers to tackle these “genes of unknown significance” by generating the first gene knock-out lines and other reagents. We feel that the UDN MOSC-like centralized facility that allows exploration of variants in unstudied genes with a quick turnaround time and RDMM-like matchmaking programs that involve a number of scientists and experts in well-studied genes are complementary approaches. A dual funding system such as the proposed MON that supports both types of activities will likely maximize the benefit of clinicians, basic scientists, as well as patients and family members suffering from rare and undiagnosed conditions.

Summary and call to action

As the UDN reaches the end of its funding period from the NIH Common Fund, we propose that multiple NIH Institutes (such as NCATS, NEI, NHLBI, NHGRI, NICHD, NIDCD, NIDDK, NIGMS, NINDS, ORIP and others) work together to sustain and expand a competitive program for an ongoing UDN MOSC in the form of a MON, because most undiagnosed patients have multiple organ systems affected. It is possible that grant funding to establish the MON or MON-like structure could also be prioritized either through mechanisms such as an entirely new Common Fund initiative that is more focused on in vivo functional studies and mechanistic research, or through specific efforts by different NIH institutes. We argue that the work to sustain the MOSC and its transformation into a larger MON is highly justified and that efforts to sustain a steady pace of high impact gene discovery will pay off for rare and undiagnosed diseases, as well as impacting our understanding of common diseases.

Availability of data and materials

Not applicable.

Abbreviations

Accelerating Medicines Partnership Type 2 Diabetes Consortium

Baylor College of Medicine

Clinical Advisory Committee

Centers for Disease Control and Prevention

Centers for Mendelian Genomics

Foundation Fighting Blindness

Human Genome Variation Society

National Center for Advancing Translational Sciences

National Eye Institute

National Heart, Lung, and Blood Institute

National Human Genome Research Institute

Eunice Kennedy Shriver National Institute of Child Health and Human Development

National Institute on Deafness and Other Communication Disorders

National Institute of Diabetes and Digestive and Kidney Diseases

National Institute of General Medical Sciences

National Institutes of Health

National Institute of Neurological Disorders and Stroke

Model organism Aggregated Resources for Rare Variant ExpLoration

• Model organisms

Model Organisms Network

Model Organism Screening Center

Online Mendelian Inheritance in Man

Office of Research Infrastructure Programs

Rare and Atypical Diabetes Network

Rare Disease Clinical Research Network

Rare Diseases Models and Mechanisms

Request for applications

Scientific Advisory Committee

Undiagnosed Diseases Network

Undiagnosed Diseases Program

Lupski JR, Belmont JW, Boerwinkle E, Gibbs RA. Clan genomics and the complex architecture of human disease. Cell. 2011;147(1):32–43.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Ng SB, Nickerson DA, Bamshad MJ, Shendure J. Massively parallel sequencing and rare disease. Hum Mol Genet. 2010;19(R2):R119–24.

Bamshad MJ, Shendure JA, Valle D, Hamosh A, Lupski JR, Gibbs RA, et al. The Centers for Mendelian Genomics: a new large-scale initiative to identify the genes underlying rare Mendelian conditions. Am J Med Genet A. 2012;158A(7):1523–5.

Article   PubMed   Google Scholar  

Chong JX, Buckingham KJ, Jhangiani SN, Boehm C, Sobreira N, Smith JD, et al. The genetic basis of Mendelian phenotypes: discoveries, challenges, and opportunities. Am J Hum Genet. 2015;97(2):199–215.

Rath A, Olry A, Dhombres F, Brandt MM, Urbero B, Ayme S. Representation of rare diseases in health information systems: the orphanet approach to serve a wide range of end users. Hum Mutat. 2012;33(5):803–8.

Valdez R, Ouyang L, Bolen J. Public health and rare diseases: oxymoron no more. Prev Chronic Dis. 2016;13:E05.

PubMed   PubMed Central   Google Scholar  

Bamshad MJ, Nickerson DA, Chong JX. Mendelian gene discovery: fast and furious with no end in sight. Am J Hum Genet. 2019;105(3):448–55.

Liu L, MacKenzie KR, Putluri N, Maletic-Savatic M, Bellen HJ. The glia-neuron lactate shuttle and elevated ROS promote lipid synthesis in neurons and lipid droplet accumulation in glia via APOE/D. Cell Metab. 2017;26(5):719–37.

Lin G, Wang L, Marcogliese PC, Bellen HJ. Sphingolipids in the pathogenesis of Parkinson’s disease and Parkinsonism. Trends Endocrinol Metab. 2019;30(2):106–17.

Article   CAS   PubMed   Google Scholar  

Ramoni RB, Mulvihill JJ, Adams DR, Allard P, Ashley EA, Bernstein JA, et al. The undiagnosed diseases network: accelerating discovery about health and disease. Am J Hum Genet. 2017;100(2):185–92.

Splinter K, Adams DR, Bacino CA, Bellen HJ, Bernstein JA, Cheatle-Jarvela AM, et al. Effect of genetic diagnosis on patients with previously undiagnosed disease. N Engl J Med. 2018;379(22):2131–9.

Sobreira NLM, Arachchi H, Buske OJ, Chong JX, Hutton B, Foreman J, et al. Matchmaker exchange. Curr Protoc Hum Genet. 2017;95:9.

Wangler MF, Yamamoto S, Chao HT, Posey JE, Westerfield M, Postlethwait J, et al. Model organisms facilitate rare disease diagnosis and therapeutic research. Genetics. 2017;207(1):9–27.

Karczewski KJ, Weisburd B, Thomas B, Solomonson M, Ruderfer DM, Kavanagh D, et al. The ExAC browser: displaying reference data information from over 60 000 exomes. Nucleic Acids Res. 2017;45(D1):D840–5.

Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016;536(7616):285–91.

Boycott KM, Rath A, Chong JX, Hartley T, Alkuraya FS, Baynam G, et al. International cooperation to enable the diagnosis of all rare genetic diseases. Am J Hum Genet. 2017;100(5):695–705.

Wang J, Al-Ouran R, Hu Y, Kim SY, Wan YW, Wangler MF, et al. MARRVEL: integration of human and model organism genetic resources to facilitate functional annotation of the human genome. Am J Hum Genet. 2017;100(6):843–53.

Wang J, Liu Z, Bellen HJ, Yamamoto S. Navigating MARRVEL, a web-based tool that integrates human genomics and model organism genetics information. J Vis Exp. 2019;(150).

Wang J, Mao D, Fazal F, Kim SY, Yamamoto S, Bellen H, et al. Using MARRVEL v1.2 for bioinformatics analysis of human genes and variant pathogenicity. Curr Protocols Bioinform. 2019;67(1):e85.

Google Scholar  

Bellen HJ, Wangler MF, Yamamoto S. The fruit fly at the interface of diagnosis and pathogenic mechanisms of rare and common human diseases. Hum Mol Genet. 2019;28(R2):R207–14.

Chao HT, Davids M, Burke E, Pappas JG, Rosenfeld JA, McCarty AJ, et al. A syndromic neurodevelopmental disorder caused by De Novo variants in EBF3. Am J Hum Genet. 2017;100(1):128–37.

Schoch K, Meng L, Szelinger S, Bearden DR, Stray-Pedersen A, Busk OL, et al. A recurrent De Novo variant in NACC1 causes a syndrome characterized by infantile epilepsy, cataracts, and profound developmental delay. Am J Hum Genet. 2017;100(2):343–51.

Luo X, Rosenfeld JA, Yamamoto S, Harel T, Zuo Z, Hall M, et al. Clinically severe CACNA1A alleles affect synaptic function and neurodegeneration differentially. PLoS Genet. 2017;13(7):e1006905.

Article   PubMed   PubMed Central   CAS   Google Scholar  

Olahova M, Yoon WH, Thompson K, Jangam S, Fernandez L, Davidson JM, et al. Biallelic mutations in ATP5F1D, which encodes a subunit of ATP synthase, cause a metabolic disorder. Am J Hum Genet. 2018;102(3):494–504.

Liu N, Schoch K, Luo X, Pena LDM, Bhavana VH, Kukolich MK, et al. Functional variants in TBX2 are associated with a syndromic cardiovascular and skeletal developmental disorder. Hum Mol Genet. 2018;27(14):2454–65.

Marcogliese PC, Shashi V, Spillmann RC, Stong N, Rosenfeld JA, Koenig MK, et al. IRF2BPL is associated with neurological phenotypes. Am J Hum Genet. 2018;103(2):245–60.

Ferreira CR, Xia ZJ, Clement A, Parry DA, Davids M, Taylan F, et al. A recurrent De Novo heterozygous COG4 substitution leads to Saul-Wilson syndrome, disrupted vesicular trafficking, and altered proteoglycan glycosylation. Am J Hum Genet. 2018;103(4):553–67.

Burrage LC, Reynolds JJ, Baratang NV, Phillips JB, Wegner J, McFarquhar A, et al. Bi-allelic variants in TONSL cause SPONASTRIME dysplasia and a spectrum of skeletal dysplasia phenotypes. Am J Hum Genet. 2019;104(3):422–38.

Kanca O, Andrews JC, Lee PT, Patel C, Braddock SR, Slavotinek AM, et al. De Novo variants in WDR37 are associated with epilepsy, colobomas, dysmorphism, developmental delay, intellectual disability, and cerebellar hypoplasia. Am J Hum Genet. 2019;105(2):413–24.

Chung HL, Wangler MF, Marcogliese PC, Jo J, Ravenscroft TA, Zuo Z, et al. Loss- or gain-of-function mutations in ACOX1 cause axonal loss via different mechanisms. Neuron. 2020;106(4):589–606.

Dutta D, Briere LC, Kanca O, Marcogliese PC, Walker MA, High FA, et al. De novo mutations in TOMM70, a receptor of the mitochondrial import translocase, cause neurological impairment. Hum Mol Genet. 2020;29(9):1568–79.

Chung HL, Mao X, Wang H, Park YJ, Marcogliese PC, Rosenfeld JA, et al. De Novo variants in CDK19 are associated with a syndrome involving intellectual disability and epileptic encephalopathy. Am J Hum Genet. 2020;106(5):717–25.

Blanco-Sanchez B, Clement A, Stednitz SJ, Kyle J, Peirce JL, McFadden M, et al. Yippee like 3 (ypel3) is a novel gene required for myelinating and perineurial glia development. PLoS Genet. 2020;16(6):e1008841.

Barish S, Barakat TS, Michel BC, Mashtalir N, Phillips JB, Valencia AM, et al. BICRA, a SWI/SNF complex member, is associated with BAF-disorder related phenotypes in humans and model organisms. Am J Hum Genet. 2020;107(6):1096–112.

Marom R, Burrage LC, Clément A, Blanco-Sánchez B, Venditti R, Jain M, et al. COPB2 haploinsufficiency causes a coatopathy with osteoporosis and developmental delay. bioRxiv. 2020:2020.09.14.297234.

Luo X, Schoch K, Jangam SV, Bhavana VH, Graves HK, Kansagra S, et al. Rare deleterious de novo missense variants in RNF2/RING2 are associated with a neurodevelopmental disorder with unique clinical features. Hum Mol Genet. 2021 (in press) .

Alliance of Genome Resources C. Alliance of Genome Resources Portal: unified model organism research platform. Nucleic Acids Res 2020;48(D1):D650-D8.

Bellen HJ, Tong C, Tsuda H. 100 years of Drosophila research and its impact on vertebrate neuroscience: a history lesson for the future. Nat Rev Neurosci. 2010;11(7):514–22.

Corsi AK, Wightman B, Chalfie M. A transparent window into biology: a primer on caenorhabditis elegans. Genetics. 2015;200(2):387–407.

Alberts B. Molecular biology of the cell. 6th ed. New York: Garland Science, Taylor and Francis Group; 2015.

Kim S, Twigg SRF, Scanlon VA, Chandra A, Hansen TJ, Alsubait A, et al. Localized TWIST1 and TWIST2 basic domain substitutions cause four distinct human diseases that can be modeled in Caenorhabditis elegans. Hum Mol Genet. 2017;26(11):2118–32.

Aoyagi K, Rossignol E, Hamdan FF, Mulcahy B, Xie L, Nagamatsu S, et al. A gain-of-function mutation in NALCN in a child with intellectual disability, ataxia, and arthrogryposis. Hum Mutat. 2015;36(8):753–7.

Dimitriadi M, Derdowski A, Kalloo G, Maginnis MS, O’Hern P, Bliska B, et al. Decreased function of survival motor neuron protein impairs endocytic pathways. Proc Natl Acad Sci USA. 2016;113(30):E4377–86.

Bellen HJ, Yamamoto S. Morgan’s legacy: fruit flies and the functional annotation of conserved genes. Cell. 2015;163(1):12–4.

Wangler MF, Yamamoto S, Bellen HJ. Fruit flies in biomedical research. Genetics. 2015;199(3):639–53.

Reiter LT, Potocki L, Chien S, Gribskov M, Bier E. A systematic analysis of human disease-associated gene sequences in Drosophila melanogaster. Genome Res. 2001;11(6):1114–25.

Hu Y, Flockhart I, Vinayagam A, Bergwitz C, Berger B, Perrimon N, et al. An integrative approach to ortholog prediction for disease-focused and other functional studies. BMC Bioinform. 2011;12:357.

Article   Google Scholar  

Harnish JM, Deal SL, Chao HT, Wangler MF, Yamamoto S. In vivo functional study of disease-associated rare human variants using Drosophila. J Vis Exp. 2019;(150).

Kanca O, Bellen HJ, Schnorrer F. Gene tagging strategies to assess protein expression, localization, and function in Drosophila. Genetics. 2017;207(2):389–412.

CAS   PubMed   PubMed Central   Google Scholar  

Kanca O, Zirin J, Garcia-Marques J, Knight SM, Yang-Zhou D, Amador G, et al. An efficient CRISPR-based strategy to insert small and large fragments of DNA using short homology arms. eLife 2019;8:e51539.

Bangi E, Ang C, Smibert P, Uzilov AV, Teague AG, Antipin Y, et al. A personalized platform identifies trametinib plus zoledronate for a patient with KRAS-mutant metastatic colorectal cancer. Sci Adv 2019;5(5):eaav6528.

Eisen JS. Chapter 1: History of zebrafish research. In: Cartner SC, Eisen JS, Farmer SC, Guillemin KJ, Kent ML, Sanders GE, editors. The zebrafish in biomedical research. Academic Press; 2020. p. 3–14.

Chapter   Google Scholar  

Carney TJ, Mosimann C. Switch and trace: recombinase genetics in zebrafish. Trends Genet TIG. 2018;34(5):362–78.

Adamson KI, Sheridan E, Grierson AJ. Use of zebrafish models to investigate rare human disease. J Med Genet. 2018;55(10):641–9.

Howe K, Clark MD, Torroja CF, Torrance J, Berthelot C, Muffato M, et al. The zebrafish reference genome sequence and its relationship to the human genome. Nature. 2013;496(7446):498–503.

Force A, Lynch M, Pickett FB, Amores A, Yan YL, Postlethwait J. Preservation of duplicate genes by complementary, degenerative mutations. Genetics. 1999;151(4):1531–45.

Postlethwait J, Amores A, Force A, Yan YL. The zebrafish genome. Methods Cell Biol. 1999;60:149–63.

Blanco-Sanchez B, Clement A, Phillips JB, Westerfield M. Zebrafish models of human eye and inner ear diseases. Methods Cell Biol. 2017;138:415–67.

Phillips JB, Westerfield M. Zebrafish models in translational research: tipping the scales toward advancements in human health. Dis Models Mech. 2014;7(7):739–43.

Article   CAS   Google Scholar  

Geng X, Speirs C, Lagutin O, Inbal A, Liu W, Solnica-Krezel L, et al. Haploinsufficiency of Six3 fails to activate Sonic hedgehog expression in the ventral forebrain and causes holoprosencephaly. Dev Cell. 2008;15(2):236–47.

Beck BB, Phillips JB, Bartram MP, Wegner J, Thoenes M, Pannes A, et al. Mutation of POC1B in a severe syndromic retinal ciliopathy. Hum Mutat. 2014;35(10):1153–62.

Ebermann I, Phillips JB, Liebau MC, Koenekoop RK, Schermer B, Lopez I, et al. PDZD7 is a modifier of retinal disease and a contributor to digenic Usher syndrome. J Clin Investig. 2010;120(6):1812–23.

Cully M. Zebrafish earn their drug discovery stripes. Nat Rev Drug Discov. 2019;18(11):811–3.

McGary KL, Park TJ, Woods JO, Cha HJ, Wallingford JB, Marcotte EM. Systematic discovery of nonobvious human disease models through orthologous phenotypes. Proc Natl Acad Sci U S A. 2010;107(14):6544–9.

Salazar JL, Yamamoto S. Integration of Drosophila and human genetics to understand notch signaling related diseases. Adv Exp Med Biol. 2018;1066:141–85.

Posey JE, O’Donnell-Luria AH, Chong JX, Harel T, Jhangiani SN, Coban Akdemir ZH, et al. Insights into genetics, human biology and disease gleaned from family based genomic studies. Genet Med Off J Am Coll Med Genet. 2019;21(4):798–812.

Yamamoto S, Jaiswal M, Charng WL, Gambin T, Karaca E, Mirzaa G, et al. A drosophila genetic resource of mutants to study mechanisms underlying human genetic diseases. Cell. 2014;159(1):200–14.

Harel T, Yoon WH, Garone C, Gu S, Coban-Akdemir Z, Eldomery MK, et al. Recurrent De Novo and biallelic variation of ATAD3A, encoding a mitochondrial membrane protein, results in distinct neurological syndromes. Am J Hum Genet. 2016;99(4):831–45.

Yoon WH, Sandoval H, Nagarkar-Jaiswal S, Jaiswal M, Yamamoto S, Haelterman NA, et al. Loss of nardilysin, a mitochondrial co-chaperone for alpha-ketoglutarate dehydrogenase, promotes mTORC1 activation and neurodegeneration. Neuron. 2017;93(1):115–31.

Link N, Chung H, Jolly A, Withers M, Tepe B, Arenkiel BR, et al. Mutations in ANKLE2, a ZIKA virus target, disrupt an asymmetric cell division pathway in Drosophila neuroblasts to cause microcephaly. Dev Cell. 2019;51(6):713–29.

Krischer JP, Gopal-Srivastava R, Groft SC, Eckstein DJ, Rare Diseases Clinical Research Network’s. The Rare Diseases Clinical Research Network’s organization and approach to observational research and health outcomes research. J Gen Intern Med. 2014;29(Suppl 3):S739–44.

Dickinson ME, Flenniken AM, Ji X, Teboul L, Wong MD, White JK, et al. High-throughput discovery of novel developmental phenotypes. Nature. 2016;537(7621):508–14.

Cacheiro P, Haendel MA, Smedley D, International Mouse Phenotyping C, the Monarch I. New models for human disease from the International Mouse Phenotyping Consortium. Mamm Genome Off J Int Mamm Genome Soc. 2019;30(5–6):143–50.

Clark JF, Dinsmore CJ, Soriano P. A most formidable arsenal: genetic technologies for building a better mouse. Genes Dev. 2020;34(19–20):1256–86.

Russell JJ, Theriot JA, Sood P, Marshall WF, Landweber LF, Fritz-Laylin L, et al. Non-model model organisms. BMC Biol. 2017;15(1):55.

Volpato V, Webber C. Addressing variability in iPSC-derived models of human disease: guidelines to promote reproducibility. Dis Model Mech. 2020;13(1).

Schutgens F, Clevers H. Human organoids: tools for understanding biology and treating diseases. Annu Rev Pathol. 2020;15:211–34.

Boycott KM, Campeau PM, Howley HE, Pavlidis P, Rogic S, Oriel C, et al. The Canadian Rare Diseases Models and Mechanisms (RDMM) network: connecting understudied genes to model organisms. Am J Hum Genet. 2020;106(2):143–52.

Download references

Acknowledgements

We are grateful to Stephanie Morrison for her assistance in drafting and improving the figures in this manuscript. We especially thank Antonella Pignata, Daniel Wegner, May Malicdan, and the members of the UDN Model Organisms Working Group, as well as the UDN clinicians, investigators, and NIH staff who are part of the UDN for all of their contributions to this team effort.

Research reported in this manuscript was supported by the NIH Common Fund, through the Office of Strategic Coordination/Office of the NIH Director under Award Numbers U54NS108251 (D.B., A.N.B., T.S., S.C.P., and L.S.-K.), U54NS093793 (M.F.W., S.Y., J.H.P., H.J.B., and M.W.), and R01OD011116 (J.H.P.), by the Office of the NIH Director under Award Number R24OD022005 (H.J.B), by the National Institute of General Medical Sciences under Award Number R01GM067858 (H.J.B), and by the National Human Genome Research Institute under Award Number K08HG010154 (D.B.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. H.J.B. is an investigator of the Howard Hughes Medical Institute (HHMI).

Author information

Dustin Baldridge and Michael F. Wangler: co-first and co-corresponding authors

Authors and Affiliations

Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, 63110, USA

Dustin Baldridge & Stephen C. Pak

Department of Molecular and Human Genetics, Baylor College of Medicine (BCM), Houston, TX, 77030, USA

Michael F. Wangler, Shinya Yamamoto & Hugo J. Bellen

Department of Pediatrics, BCM, Houston, TX, 77030, USA

Michael F. Wangler

Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX, 77030, USA

Development, Disease Models & Therapeutics Graduate Program, BCM, Houston, TX, 77030, USA

Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, 63110, USA

Angela N. Bowman, Jimann Shin & Lilianna Solnica-Krezel

Center of Regenerative Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA

Angela N. Bowman, Tim Schedl & Lilianna Solnica-Krezel

Department of Neuroscience, BCM, Houston, TX, 77030, USA

Shinya Yamamoto & Hugo J. Bellen

Department of Genetics, Washington University School of Medicine, St. Louis, MO, 63110, USA

Institute of Neuroscience, University of Oregon, Eugene, OR, 97403, USA

John H. Postlethwait & Monte Westerfield

Howard Hughes Medical Institute, Houston, TX, 77030, USA

Hugo J. Bellen

You can also search for this author in PubMed   Google Scholar

• Maria T. Acosta
• , Margaret Adam
• , David R. Adams
• , Pankaj B. Agrawal
• , Mercedes E. Alejandro
• , Justin Alvey
• , Laura Amendola
• , Ashley Andrews
• , Euan A. Ashley
• , Mahshid S. Azamian
• , Carlos A. Bacino
• , Guney Bademci
• , Eva Baker
• , Ashok Balasubramanyam
• , Dustin Baldridge
• , Michael Bamshad
• , Deborah Barbouth
• , Pinar Bayrak-Toydemir
• , Anita Beck
• , Alan H. Beggs
• , Edward Behrens
• , Gill Bejerano
• , Jimmy Bennet
• , Beverly Berg-Rood
• , Jonathan A. Bernstein
• , Gerard T. Berry
• , Anna Bican
• , Stephanie Bivona
• , Elizabeth Blue
• , John Bohnsack
• , Carsten Bonnenmann
• , Devon Bonner
• , Lorenzo Botto
• , Brenna Boyd
• , Lauren C. Briere
• , Elly Brokamp
• , Gabrielle Brown
• , Elizabeth A. Burke
• , Lindsay C. Burrage
• , Manish J. Butte
• , Peter Byers
• , William E. Byrd
• , John Carey
• , Olveen Carrasquillo
• , Ta Chen Peter Chang
• , Sirisak Chanprasert
• , Hsiao-Tuan Chao
• , Gary D. Clark
• , Terra R. Coakley
• , Laurel A. Cobban
• , Joy D. Cogan
• , Matthew Coggins
• , F. Sessions Cole
• , Heather A. Colley
• , Cynthia M. Cooper
• , Heidi Cope
• , William J. Craigen
• , Andrew B. Crouse
• , Michael Cunningham
• , Precilla D’Souza
• , Hongzheng Dai
• , Surendra Dasari
• , Joie Davis
• , Jyoti G. Dayal
• , Matthew Deardorff
• , Esteban C. Dell’Angelica
• , Shweta U. Dhar
• , Katrina Dipple
• , Daniel Doherty
• , Naghmeh Dorrani
• , Argenia L. Doss
• , Emilie D. Douine
• , David D. Draper
• , Laura Duncan
• , Dawn Earl
• , David J. Eckstein
• , Lisa T. Emrick
• , Christine M. Eng
• , Cecilia Esteves
• , Marni Falk
• , Liliana Fernandez
• , Carlos Ferreira
• , Elizabeth L. Fieg
• , Laurie C. Findley
• , Paul G. Fisher
• , Brent L. Fogel
• , Irman Forghani
• , Laure Fresard
• , William A. Gahl
• , Ian Glass
• , Bernadette Gochuico
• , Rena A. Godfrey
• , Katie Golden-Grant
• , Alica M. Goldman
• , Madison P. Goldrich
• , David B. Goldstein
• , Alana Grajewski
• , Catherine A. Groden
• , Irma Gutierrez
• , Sihoun Hahn
• , Rizwan Hamid
• , Neil A. Hanchard
• , Kelly Hassey
• , Nichole Hayes
• , Frances High
• , Anne Hing
• , Fuki M. Hisama
• , Ingrid A. Holm
• , Jason Hom
• , Martha Horike-Pyne
• , Alden Huang
• , Yong Huang
• , Laryssa Huryn
• , Rosario Isasi
• , Fariha Jamal
• , Gail P. Jarvik
• , Jeffrey Jarvik
• , Suman Jayadev
• , Lefkothea Karaviti
• , Jennifer Kennedy
• , Dana Kiley
• , Shilpa N. Kobren
• , Isaac S. Kohane
• , Jennefer N. Kohler
• , Deborah Krakow
• , Donna M. Krasnewich
• , Elijah Kravets
• , Susan Korrick
• , Mary Koziura
• , Joel B. Krier
• , Seema R. Lalani
• , Byron Lam
• , Christina Lam
• , Grace L. LaMoure
• , Brendan C. Lanpher
• , Ian R. Lanza
• , Lea Latham
• , Kimberly LeBlanc
• , Brendan H. Lee
• , Roy Levitt
• , Richard A. Lewis
• , Sharyn A. Lincoln
• , Pengfei Liu
• , Xue Zhong Liu
• , Nicola Longo
• , Sandra K. Loo
• , Joseph Loscalzo
• , Richard L. Maas
• , John MacDowall
• , Ellen F. Macnamara
• , Calum A. MacRae
• , Valerie V. Maduro
• , Marta M. Majcherska
• , Bryan C. Mak
• , May Christine V. Malicdan
• , Laura A. Mamounas
• , Teri A. Manolio
• , Kenneth Maravilla
• , Thomas C. Markello
• , Ronit Marom
• , Gabor Marth
• , Beth A. Martin
• , Martin G. Martin
• , Julian A. Martínez-Agosto
• , Shruti Marwaha
• , Jacob McCauley
• , Allyn McConkie-Rosell
• , Colleen E. McCormack
• , Alexa T. McCray
• , Elisabeth McGee
• , Heather Mefford
• , J. Lawrence Merritt
• , Matthew Might
• , Ghayda Mirzaa
• , Eva Morava
• , Paolo M. Moretti
• , Deborah Mosbrook-Davis
• , John J. Mulvihill
• , David R. Murdock
• , Anna Nagy
• , Mariko Nakano-Okuno
• , Stan F. Nelson
• , John H. Newman
• , Sarah K. Nicholas
• , Deborah Nickerson
• , Shirley Nieves-Rodriguez
• , Donna Novacic
• , Devin Oglesbee
• , James P. Orengo
• , Laura Pace
• , Stephen Pak
• , J. Carl Pallais
• , Christina GS. Palmer
• , Jeanette C. Papp
• , Neil H. Parker
• , John A. Phillips III
• , Jennifer E. Posey
• , Lorraine Potocki
• , Bradley Power
• , Barbara N. Pusey
• , Aaron Quinlan
• , Wendy Raskind
• , Archana N. Raja
• , Deepak A. Rao
• , Genecee Renteria
• , Chloe M. Reuter
• , Lynette Rives
• , Amy K. Robertson
• , Lance H. Rodan
• , Jill A. Rosenfeld
• , Natalie Rosenwasser
• , Francis Rossignol
• , Maura Ruzhnikov
• , Ralph Sacco
• , Jacinda B. Sampson
• , Susan L. Samson
• , Mario Saporta
• , C. Ron Scott
• , Judy Schaechter
• , Timothy Schedl
• , Kelly Schoch
• , Daryl A. Scott
• , Vandana Shashi
• , Jimann Shin
• , Rebecca Signer
• , Edwin K. Silverman
• , Janet S. Sinsheimer
• , Kathy Sisco
• , Edward C. Smith
• , Kevin S. Smith
• , Emily Solem
• , Lilianna Solnica-Krezel
• , Ben Solomon
• , Rebecca C. Spillmann
• , Joan M. Stoler
• , Jennifer A. Sullivan
• , Kathleen Sullivan
• , Angela Sun
• , Shirley Sutton
• , David A. Sweetser
• , Virginia Sybert
• , Holly K. Tabor
• , Amelia L. M. Tan
• , Queenie K.-G. Tan
• , Mustafa Tekin
• , Fred Telischi
• , Willa Thorson
• , Audrey Thurm
• , Cynthia J. Tifft
• , Camilo Toro
• , Alyssa A. Tran
• , Brianna M. Tucker
• , Tiina K. Urv
• , Adeline Vanderver
• , Matt Velinder
• , Dave Viskochil
• , Tiphanie P. Vogel
• , Colleen E. Wahl
• , Stephanie Wallace
• , Nicole M. Walley
• , Chris A. Walsh
• , Melissa Walker
• , Jennifer Wambach
• , Jijun Wan
• , Lee-kai Wang
• , Michael F. Wangler
• , Patricia A. Ward
• , Daniel Wegner
• , Mark Wener
• , Tara Wenger
• , Katherine Wesseling Perry
• , Monte Westerfield
• , Matthew T. Wheeler
• , Jordan Whitlock
• , Lynne A. Wolfe
• , Jeremy D. Woods
• , Shinya Yamamoto
• , John Yang
• , Muhammad Yousef
• , Diane B. Zastrow
• , Wadih Zein
• , Chunli Zhao
•  & Stephan Zuchner

Contributions

DB, MFW, ANB, and SY drafted sections of the manuscript which was edited by TS, SCP, JHP, LS-K, HJB, and MW. All authors read and approved the final manuscript.

Members of the Undiagnosed Diseases Network: Maria T. Acosta, Margaret Adam, David R. Adams, Pankaj B. Agrawal, Mercedes E. Alejandro, Justin Alvey, Laura Amendola, Ashley Andrews, Euan A. Ashley, Mahshid S. Azamian, Carlos A. Bacino, Guney Bademci, Eva Baker, Ashok Balasubramanyam, Dustin Baldridge, Jim Bale, Michael Bamshad, Deborah Barbouth, Pinar Bayrak-Toydemir, Anita Beck, Alan H. Beggs, Edward Behrens, Gill Bejerano, Jimmy Bennet, Beverly Berg-Rood, Jonathan A. Bernstein, Gerard T. Berry, Anna Bican, Stephanie Bivona, Elizabeth Blue, John Bohnsack, Carsten Bonnenmann, Devon Bonner, Lorenzo Botto, Brenna Boyd, Lauren C. Briere, Elly Brokamp, Gabrielle Brown, Elizabeth A. Burke, Lindsay C. Burrage, Manish J. Butte, Peter Byers, William E. Byrd, John Carey, Olveen Carrasquillo, Ta Chen Peter Chang, Sirisak Chanprasert, Hsiao-Tuan Chao, Gary D. Clark, Terra R. Coakley, Laurel A. Cobban, Joy D. Cogan, Matthew Coggins, F. Sessions Cole, Heather A. Colley, Cynthia M. Cooper, Heidi Cope, William J. Craigen, Andrew B. Crouse, Michael Cunningham, Precilla D'Souza, Hongzheng Dai, Surendra Dasari, Joie Davis, Jyoti G. Dayal, Matthew Deardorff, Esteban C. Dell'Angelica, Shweta U. Dhar, Katrina Dipple, Daniel Doherty, Naghmeh Dorrani, Argenia L. Doss, Emilie D. Douine, David D. Draper, Laura Duncan, Dawn Earl, David J. Eckstein, Lisa T. Emrick, Christine M. Eng, Cecilia Esteves, Marni Falk, Liliana Fernandez, Carlos Ferreira, Elizabeth L. Fieg, Laurie C. Findley, Paul G. Fisher, Brent L. Fogel, Irman Forghani, Laure Fresard, William A. Gahl, Ian Glass, Bernadette Gochuico, Rena A. Godfrey, Katie Golden-Grant, Alica M. Goldman, Madison P. Goldrich, David B. Goldstein, Alana Grajewski, Catherine A. Groden, Irma Gutierrez, Sihoun Hahn, Rizwan Hamid, Neil A. Hanchard, Kelly Hassey, Nichole Hayes, Frances High, Anne Hing, Fuki M. Hisama, Ingrid A. Holm, Jason Hom, Martha Horike-Pyne, Alden Huang, Yong Huang, Laryssa Huryn, Rosario Isasi, Fariha Jamal, Gail P. Jarvik, Jeffrey Jarvik, Suman Jayadev, Lefkothea Karaviti, Jennifer Kennedy, Dana Kiley, Shilpa N. Kobren, Isaac S. Kohane, Jennefer N. Kohler, Deborah Krakow, Donna M. Krasnewich, Elijah Kravets, Susan Korrick, Mary Koziura, Joel B. Krier, Seema R. Lalani, Byron Lam, Christina Lam, Grace L. LaMoure, Brendan C. Lanpher, Ian R. Lanza, Lea Latham, Kimberly LeBlanc, Brendan H. Lee, Hane Lee, Roy Levitt, Richard A. Lewis, Sharyn A. Lincoln, Pengfei Liu, Xue Zhong Liu, Nicola Longo, Sandra K. Loo, Joseph Loscalzo, Richard L. Maas, John MacDowall, Ellen F. Macnamara, Calum A. MacRae, Valerie V. Maduro, Marta M. Majcherska, Bryan C. Mak, May Christine V. Malicdan, Laura A. Mamounas, Teri A. Manolio, Rong Mao, Kenneth Maravilla, Thomas C. Markello, Ronit Marom, Gabor Marth, Beth A. Martin, Martin G. Martin, Julian A. Martínez-Agosto, Shruti Marwaha, Jacob McCauley, Allyn McConkie-Rosell, Colleen E. McCormack, Alexa T. McCray, Elisabeth McGee, Heather Mefford, J. Lawrence Merritt, Matthew Might, Ghayda Mirzaa, Eva Morava, Paolo M. Moretti, Deborah Mosbrook-Davis, John J. Mulvihill, David R. Murdock, Anna Nagy, Mariko Nakano-Okuno, Avi Nath, Stan F. Nelson, John H. Newman, Sarah K. Nicholas, Deborah Nickerson, Shirley Nieves-Rodriguez, Donna Novacic, Devin Oglesbee, James P. Orengo, Laura Pace, Stephen Pak, J. Carl Pallais, Christina GS. Palmer, Jeanette C. Papp, Neil H. Parker, John A. Phillips III, Jennifer E. Posey, Lorraine Potocki, Bradley Power, Barbara N. Pusey, Aaron Quinlan, Wendy Raskind, Archana N. Raja, Deepak A. Rao, Genecee Renteria, Chloe M. Reuter, Lynette Rives, Amy K. Robertson, Lance H. Rodan, Jill A. Rosenfeld, Natalie Rosenwasser, Francis Rossignol, Maura Ruzhnikov, Ralph Sacco, Jacinda B. Sampson, Susan L. Samson, Mario Saporta, C. Ron Scott, Judy Schaechter, Timothy Schedl, Kelly Schoch, Daryl A. Scott, Vandana Shashi, Jimann Shin, Rebecca Signer, Edwin K. Silverman, Janet S. Sinsheimer, Kathy Sisco, Edward C. Smith, Kevin S. Smith, Emily Solem, Lilianna Solnica-Krezel, Ben Solomon, Rebecca C. Spillmann, Joan M. Stoler, Jennifer A. Sullivan, Kathleen Sullivan, Angela Sun, Shirley Sutton, David A. Sweetser, Virginia Sybert, Holly K. Tabor, Amelia L. M. Tan, Queenie K.-G. Tan, Mustafa Tekin, Fred Telischi, Willa Thorson, Audrey Thurm, Cynthia J. Tifft, Camilo Toro, Alyssa A. Tran, Brianna M. Tucker, Tiina K. Urv, Adeline Vanderver, Matt Velinder, Dave Viskochil, Tiphanie P. Vogel, Colleen E. Wahl, Stephanie Wallace, Nicole M. Walley, Chris A. Walsh, Melissa Walker, Jennifer Wambach, Jijun Wan, Lee-kai Wang, Michael F. Wangler, Patricia A. Ward, Daniel Wegner, Mark Wener, Tara Wenger, Katherine Wesseling Perry, Monte Westerfield, Matthew T. Wheeler, Jordan Whitlock, Lynne A. Wolfe, Jeremy D. Woods, Shinya Yamamoto, John Yang, Muhammad Yousef, Diane B. Zastrow, Wadih Zein, Chunli Zhao, Stephan Zuchner.

Corresponding authors

Correspondence to Dustin Baldridge or Michael F. Wangler .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The Department of Molecular and Human Genetics at Baylor College of Medicine receives revenue from clinical genetic testing completed at Baylor Genetics Laboratories.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Baldridge, D., Wangler, M.F., Bowman, A.N. et al. Model organisms contribute to diagnosis and discovery in the undiagnosed diseases network: current state and a future vision. Orphanet J Rare Dis 16 , 206 (2021). https://doi.org/10.1186/s13023-021-01839-9

Download citation

Received : 27 January 2021

Accepted : 23 April 2021

Published : 07 May 2021

DOI : https://doi.org/10.1186/s13023-021-01839-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Drosophila melanogaster
• Undiagnosed diseases

Orphanet Journal of Rare Diseases

ISSN: 1750-1172

• Submission enquiries: Access here and click Contact Us
• General enquiries: [email protected]

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Review Article
• Open access
• Published: 01 March 2021

Zebrafish as an animal model for biomedical research

• Tae-Young Choi 1 , 2 ,
• Tae-Ik Choi 3 ,
• Yu-Ri Lee 3 ,
• Seong-Kyu Choe   ORCID: orcid.org/0000-0002-2102-973X 2 , 4 , 5 &
• Cheol-Hee Kim 3  

Experimental & Molecular Medicine volume  53 ,  pages 310–317 ( 2021 ) Cite this article

44k Accesses

227 Citations

174 Altmetric

Metrics details

• Disease model

Zebrafish have several advantages compared to other vertebrate models used in modeling human diseases, particularly for large-scale genetic mutant and therapeutic compound screenings, and other biomedical research applications. With the impactful developments of CRISPR and next-generation sequencing technology, disease modeling in zebrafish is accelerating the understanding of the molecular mechanisms of human genetic diseases. These efforts are fundamental for the future of precision medicine because they provide new diagnostic and therapeutic solutions. This review focuses on zebrafish disease models for biomedical research, mainly in developmental disorders, mental disorders, and metabolic diseases.

Similar content being viewed by others

Zebrafish disease models in drug discovery: from preclinical modelling to clinical trials

Integrating non-mammalian model organisms in the diagnosis of rare genetic diseases in humans

Exploring hematopoiesis in zebrafish using forward genetic screening

Introduction.

For use in genetic studies as an animal model, zebrafish was initially introduced by Streisinger and colleagues 1 in the early 1980s. Large-scale N-ethyl-N-nitrosourea (ENU) mutagenesis was conducted in combination with extensive phenotypic screening 2 , 3 . Further phenotypic characterization of these ENU mutations in most of the major organ systems was performed 4 . However, later positional cloning of each ENU mutation after a forward genetic screen was time-consuming and laborious 5 . Since the increase in resolution of the zebrafish genome map, advanced gene-targeting technologies involving ZFNs, TALENs, and CRISPR/Cas9 6 , 7 , 8 , 9 , 10 , 11 , 12 , have overcome challenges in generating specific gene-knockout mutations. CRISPR/Cas9 utilizes an efficient reverse genetic approach to provide knockout animals for zebrafish researchers 13 . Furthermore, the high level of genome structure shared between zebrafish and humans (~70% of human genes have at least one obvious zebrafish ortholog, compared to 80% of human genes with mouse orthologs) 14 , 15 has facilitated the use of zebrafish for understanding human genetic diseases. Recent advancements in next-generation sequencing (NGS) coupled with the demand for personalized medicine has further driven zebrafish uses in identifying causal relationships between the genotype and phenotype of various human diseases.

Additionally, zebrafish possess several advantages over rodent models in the study of vertebrate development and disease. These include hundreds of embryos in a single clutch and optical clarity of the developing embryo, which allows live imaging at the organism level 16 , 17 . In addition, the use of tissue-specific transgenic animals can be easily generated under the control of various selected gene promoters. Recent improvement of the Tol2-based transgenic system in zebrafish 18 has allowed the control of gene expression in a spatiotemporal manner by coupling with regulatory elements such as GAL4/UAS or Cre/LoxP 19 , 20 . These advantages allow live imaging of cells and tracking of cellular dynamics in vivo to study the underlying molecular mechanisms of various developing organs.

The necessity of a model organism to recapitulate metabolic symptoms and associated disease development in humans has led to the exploitation of several animal species, among which rodents have been widely employed. For the past several decades, mice have been the leading experimental animal model in the field of biomedical research due to powerful genetic tools, amenable diagnostic parameters that are comparable to those in humans, and standardized protocols for developing, diagnosing, and treating metabolic syndromes. However, factors inherently different from those in humans, such as dietary requirements, lifestyle, and microbiomes, have called for alternative animal model systems to be utilized in parallel 21 . Zebrafish is a fascinating animal model for understanding the human pathogenesis of metabolic diseases and identifying potential therapeutic options 21 . However, all animal models have unique shortcomings, are the zebrafish model is no exception: first, zebrafish are poikilothermal animals living under water. Nonetheless, zebrafish possess metabolic characteristics similar to humans to complement data obtained from other model organisms, including rodents. This possibility has been clearly shown in recent studies 22 , 23 , 24 , 25 in which drugs that had been approved for alleviating metabolic syndromes in humans were also effective in a zebrafish model.

This review addresses the use of zebrafish as an animal model for biomedical research, mainly in developmental disorders, mental disorders, and communication between the brain and organs. In addition to biomedical research, we also discuss the utility of zebrafish in metabolic control, focusing on cellular metabolic organelles.

Biomedical research I: developmental disorders

During early animal development, an organizer can induce a complete body axis when transplanted to the ventral side of a host embryo. Studies have suggested that head inducers can inhibit Wnt signaling during the early development of anterior brain structures. In zebrafish, for example, it was demonstrated that head defects in the headless mutant were caused by a mutation in T-cell factor 3 5 . Loss of gene function in the headless mutant revealed that headless can repress Wnt target genes. These data provide the first genetic evidence that a component of the Wnt signaling pathway is essential in head/brain formation and patterning in vertebrate animals.

Zebrafish have been a tractable animal model for identifying developing neurons and the in vivo architecture of the brain, from neurogenesis at the early neural plate stage to the adult brain (Fig. 1 ). The zebrafish HuC homolog, which is 89% identical to the human HuC protein, is one of the earliest discovered markers of neuronal precursor cells in zebrafish, which are apparent during neurogenesis as early as the neural plate stage (Fig. 1a ) 26 , 27 . Zebrafish are useful for studying the functional role of novel genes in neuronal development through directed expression studies of the zebrafish nervous system (Fig. 1b ) 28 . In addition, recently, tissue-clearing technology has allowed visualization of neural networks in the whole brain of adult zebrafish (Fig. 1c ). These molecular tools and technologies are useful for investigating phenotypic changes in zebrafish disease models of human developmental disorders.

a Detection of early neuronal precursor cells by whole-mount in situ hybridization with a pan-neuronal marker, huC , at the neural plate stage (10.5 h after fertilization). Unpublished data. b Immunostaining of axonal growth in the spinal cord of one-day-old zebrafish. Double-staining with anti-gicerin antibody and anti-HNK-1 antibody 28 . c Confocal image of myelin structure in an isolated adult zebrafish brain visualized by mbp promoter-driven membrane-tagged GFP, Tg(mbp: mEGFP ) . Arrows indicate the olfactory, optic, and otic nerves. Unpublished data.

The Genome-wide Association Study (GWAS) investigates a genomewide set of variants in human genetic diseases to identify the causative gene variant associated with a particular disease. GWAS data can be used to identify single-nucleotide polymorphisms (SNPs) and other variants in the genome associated with genetic disease 29 . In contrast to the identification of SNPs and variants, phenotypic abnormalities and haploinsufficiency of the various genes are derived from microdeletions or chromosomal translocation of different genomes 30 , 31 . For instance, Potocki-Shaffer syndrome (PSS) is a disorder that affects the development of bones, nerve cells in the brain, and other tissues due to the interstitial deletion of band p11.2 in chromosome 11 32 . Developmental disorders in PSS were investigated using phf21a -knockdown zebrafish, producing developmental abnormalities in the head, face, and jaw, in addition to increased neuronal apoptosis 33 . Another example of a disease studied by zebrafish models is Miles–Carpenter syndrome (MCS), in which syndromic X-linked intellectual disability is characterized by severe intellectual deficit, microcephaly, exotropia, distal muscle wasting, and low digital arches. By whole-exome sequencing of MCS families, ZC4H2 was identified as an MCS gene candidate. ZC4H2 , a zinc-finger protein, is located in Xq11.2, and point mutations in ZC4H2 were found in MCS patients. Homozygous zc4h2 -knockout zebrafish larvae showed motor hyperactivity, abnormal swimming, and continuous jaw movement. Motor hyperactivity was caused by a reduction in V2 GABAergic interneurons, arising from misspecification of neural progenitors in the brain and spinal cord of the zc4h2 -knockout zebrafish 34 . The knockout animals also exhibited contractures of the pectoral fins and abnormal eye positioning, suggestive of exotropia, indicating that zebrafish disease models can be used to study the underlying cellular and molecular mechanisms of human developmental disorders.

Biomedical research II: mental disorders

Mental disorders, also called psychiatric disorders, are characterized by defective behavioral or mental patterns that cause significant distress to the subject. The International Classification of Diseases (ICD) published by the World Health Organization (WHO) is the international standard for classifying medical disease conditions. Over 450 different definitions of mental disorders are represented in the Diagnostic and Statistical Manual of Mental Disorders (DSM), the standard reference for psychiatry published by the American Psychiatric Association. Zebrafish are highly social animals that exhibit shoaling and schooling behaviors and are suitable for social behavioral tests in relation to mental disorders. Using mutagenesis screening, Kim and colleagues recently identified a novel chemokine-like gene family, samdori ( sam ), involved in mental disorders in zebrafish. Among the five sam family members, sam2 is specifically expressed in the habenular nuclei of the brain and is associated with intellectual disability and autism spectrum disorder 35 , 36 . Sam2 -knockout animals (both zebrafish and mouse) showed defects in emotional responses, such as fear and anxiety, that are involved in anxiety-related disorders and/or autism 35 , 36 .

Additionally, by whole-exome sequencing, FAM50A was identified as the causative gene for Armfield X-linked intellectual disability (XLID) syndrome. XLID refers to forms of mental disorders with intellectual disability that are explicitly associated with X-linked recessive inheritance. Approximately 100 genes have been found to be involved in XLID syndrome 37 . XLID accounts for ~16% of all cases of intellectual disability in males, who are more likely to be affected than females. The biological activity of human FAM50A missense variants was functionally validated by rescue experiments in a zebrafish fam50a -knockout model 38 . Using the zebrafish disease model, it was recently found that Armfield XLID syndrome is a spliceosomopathy associated with aberrant mRNA processing during development 38 .

Biomedical research III: communication between the brain and other organs

Human puberty is a dynamic process that initiates the complex interactions of the hypothalamic-pituitary-gonadal axis (HPG axis), which refers to single endocrine glands as individual entities. The HPG axis plays a critical role in developing and regulating many of the body’s systems, particularly reproduction 39 . Gonadotropin-releasing hormone (GnRH), secreted by the hypothalamus in the brain, circulates through the anterior portion of the pituitary hypophyseal portal system and binds to receptors on the secretory cells of the adenohypophysis 40 . In response to GnRH stimulation, these cells produce luteinizing hormone and follicle-stimulating hormone, which circulate in the bloodstream 41 . Therefore, an adolescent develops into a mature adult with a body capable of sexual reproduction 42 . Kallmann syndrome (KS) is a genetic disorder known to prevent a person from starting or fully completing puberty. In a study showing that the WDR11 gene mutation is involved in KS pathogenicity, the zebrafish wdr11 gene was demonstrated to be expressed in the brain region, indicating a potential role for WDR11-EMX1 protein interaction 43 .

Additionally, acute inflammation is known to initiate regenerative response after traumatic injury in the adult zebrafish brain. The cysteinyl leukotriene receptor 1 ( cysltr1 )–leukotriene C4 ( LTC4 ) pathway is required and sufficient for enhanced proliferation and neurogenesis 44 . LTC4, one of the ligands for CysLT1, binds to its receptor Cysltr1 expressed on radial glial cells in the zebrafish brain 44 . In a study by Kyritsis et al., cysltr1 was increasingly expressed on radial glial cells after traumatic brain injury, suggesting cross talk between components of the inflammatory response and the central nervous system during traumatic brain injury 44 .

The nicotinamide adenine dinucleotide phosphate (NADPH) oxidase (NOX) family is involved in the production of reactive oxygen species in response to various extracellular signals. The NOX family member dual oxidase (DUOX) was identified as thyroid NADPH oxidase. In humans, DUOX2 mutations were identified among children diagnosed with congenital hypothyroidism. Recently, it was demonstrated that, in addition to goitrous thyroid glands and growth retardation, defects in anxiety response and social interaction were found in duox -knockout zebrafish 45 . These results suggest that duox -knockout zebrafish could serve as an effective animal model for studies in thyroid development and related neurological diseases, including intellectual disability and autism.

A large percentage of children with ASD are known to have gastrointestinal problems, such as constipation, diarrhea, and abdominal pain. Recent studies on the brain-gut axis have also shown that interactions with host-associated microbial communities, either directly by microbial metabolites or indirectly via immune, metabolic or endocrine systems, can act as sources of environmental cues. Molecular signals from the gut provide environmental cues for communication between the gut and the brain during episodes related to anxiety, depression, cognition or autism spectrum disorder (ASD) 46 . Moreover, modulation of intrinsic signaling pathways and extrinsic cues in resident intestinal bacteria enhances the stability of β-catenin in intestinal epithelial cells, promoting cell proliferation 47 .

Biomedical research IV: metabolic disorders

Zebrafish an animal models for metabolic research.

A high-calorie diet, a sedentary lifestyle, and a family history of metabolic disorders increase the prevalence of risk factors such as low HDL levels, high triglyceride levels, high blood glucose, high blood pressure, and abdominal obesity 48 . Such metabolic disorders may arise from an imbalance between nutritional intake and energy expenditure, leading to the development of serious illnesses, including diabetes, stroke, and fatty liver disease 49 .

In addition to general similarities with human metabolism, zebrafish metabolism also exhibits unique characteristics. Zebrafish embryos consume yolk for the first five days of development, after which they are fed for further growth to prevent them from undergoing fasting. The feeding-to-fasting transition at 5–6 days post fertilization (dpf) has been utilized to develop mechanistic insights into metabolic homeostasis upon energy deprivation 50 , 51 . Another unique feature of zebrafish is the composition and development of adipose tissue. As a poikilothermal animal, zebrafish do not seem to require brown adipose tissue, on which mammals do depend. Adipose development occurs late in development, with the first adipocyte being detected 8 dpf 50 .

Interestingly, late adipogenesis may also provide an experimental setting by which the role of adipose tissue in the pathogenesis of metabolic disorders can be investigated. Modeling metabolism to recapitulate human disorders can be appropriately established during the larval period. Similarly, metabolic disorders can be modeled in adults to explore phenotype references in the presence of all major metabolic organs. Many metabolic similarities and discrepancies between humans and zebrafish and the modeling of different types of metabolic diseases have been reviewed elsewhere 52 , 53 , 54 .

Zebrafish models for organelle biology research

Body metabolism is regulated by metabolic organelles, such as the endoplasmic reticulum (ER), mitochondria, peroxisomes, lipid droplets, and lysosomes. Whole-body metabolism is the sum of all metabolic activity of individual organs that originates from the metabolic function of individual cells. The function of subcellular organelles is critical for responding to environmental changes and regulating metabolic outputs to maintain metabolic homeostasis.

Zebrafish have served as an excellent model system to assess in vivo toxicity in response to treatment of a chemical of interest, and numerous studies have illustrated metabolic changes related to mitochondrial function upon chemical treatment 55 , 56 , 57 . After an initial study of mitochondrial activity and distribution in zebrafish oocytes reported in 1980, many reports regarding the mitochondrial genome and functional homologs of mitochondrial proteins in zebrafish were published in the late 1990s and early 2000s. More recently, zebrafish models have drawn extensive interest for use in testing a range of bioactive chemicals, including those that induce or disrupt development, improve disease conditions, or induce unfavorable side effects in daily human health or anticancer treatments 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 . In addition, the use of zebrafish as in vivo models for studying gene functions involved in metabolic activities has recently increased. Among the new molecular tools in developmental genetics, CRISPR/Cas9 is the most recent example of a reverse genetics technique, and mechanistic studies of the regulation of biogenesis, degradation and the quality maintenance of an organelle of interest have been conducted using zebrafish models 69 .

CRISPR/Cas9 is the most advanced gene editing system

Recent findings and the development of CRISPR/Cas9, evolutionary gene-editing machinery that originated from the defense system of bacteria that earned its developers the Nobel Prize in Chemistry in 2020. Highly efficient gene targeting made it possible to edit a gene of interest in any genome. Accordingly, studies utilizing CRISPR/Cas9 in zebrafish have rapidly increased. In particular, studies to elucidate the role of mitochondria in neutrophil motility 70 , tRNA biogenesis and the physiology of cardiomyocytes 71 , 72 , neuronal regeneration 73 , neurodegeneration in Parkinson’s disease 74 , 75 and cellular metabolism regulation of mitochondrial abundance 76 , 77 have been reported. Furthermore, studies illustrating the role of the endoplasmic (sarcoplasmic) reticulum included REEP5 -gene knockout, which was used to elucidate the previously unknown regulation of ER/SR membrane protein organization and stress response in cardiac myocytes 78 . In addition, the demonstration of MCTP (multiple C2 domain proteins with two transmembrane regions) gene function acting as a novel ER calcium sensor was also reported 76 .

Moreover, molecular pathogenesis studies based on the analysis of genes, such as ATP13a 79 , NPC1 80 , 81 , and GBA1 82 to understand Niewmann-Pick disease type C1 (NPC1) and other lysosomal storage diseases resulting from defective intracellular trafficking or lysosome function have been reported. Efforts have also been made to elucidate molecules and regulatory mechanisms leading to autophagosome formation, autolysosome formation, and autophagy 83 , 84 , 85 , 86 . Recently, a possible knock-in strategy to edit mitochondrial DNA and genomic DNA has been reported 87 , facilitating research on organelle function in metabolic diseases.

Transgenic approach to track organelle dynamics, abundance, and interaction

Mitochondria have long been foci due to their roles in bioenergetics and apoptosis, leading to a plethora of transgenic zebrafish. Several transgenic zebrafish, such as Mnx1:MITO-Kaede 88 , hspa8:MITO-YC2 89 , and MLS-EGFP 90 , have been generated to mark mitochondria with fluorescent proteins GFP, YFP, Kaede, and yellow cameleon (YC), which are induced explicitly by a pan-expression promoter, an inducible heat shock promoter, a cell-type-specific promoter, or a combination of the GAL4-UAS system and are localized to the mitochondria using a mitochondria-targeting sequence 91 . One of the best examples of live mitochondrial imaging was illustrated in sensory axons of Rohon-Beard neurons, in which mitochondrial shape, dynamics, and transport were analyzed quantitatively 92 . A similar in vivo technique using zebrafish has since become popular to demonstrate the connection between mitochondrial behavior and neuronal health 93 , 94 . In addition to mitochondria, other organelles have been studied to reveal their roles during zebrafish development. For instance, a peroxisomal solute carrier, slc25a17 , is involved in the maintenance of functional peroxisomes by showing substrate specificity towards coenzyme A 95 . To visualize peroxisomes in zebrafish embryos in vivo, the transgenic line Tg(Xla.Eef1a:RFP-SKL) was established and used under different metabolic conditions 96 . The use of double transgenic zebrafish allows simultaneous tracking of the dynamics of mitochondria and peroxisomes in vivo, as shown in Fig. 2a .

a Using transgenic zebrafish lines 5 dpf, mitochondria, Tg(Xla.Eef1a: MLS-EGFP ) , and peroxisomes, Tg(Xla. Eef1a: RFP-SKL ) , in the skin of the developing larva are visualized. b Motile cilia (green) in the hindbrain 4th ventricle are visualized with anti-acetylated tubulin antibody, and nuclei are shown in red. Unpublished data.

Another example is a transgenic line that marks the Golgi apparatus using the Golgi-Venus together with a cis-Golgi marker, GM-130 97 , to elucidate its role in dendrite specification of Purkinje cells. A trans-Golgi marker, GalT-GFP, was also established to reveal the dynamic localization of a connexin variant that influences cellular behavior 98 . A more systematic approach was applied to the study of secretory pathways, where a series of transgenic lines were generated based on different Rab proteins marking different types of endosomal vesicles 99 . A handful of transgenic lines were added to improve the identification of the cellular secretory pathways, and Lamp2-EGFP was used to mark lysosome-related vacuoles in the zebrafish notochord, GFP-CaaX (mem-GFP) was used to visualize the plasma membrane 100 , and NLS-mCherry or NLS-EGFP was used to the identify nucleus 101 , 102 . Another transgenic zebrafish used to mark the apoptotic cell membrane specifically, Annexin-Cy5, was also generated 103 . Moreover, transgenic zebrafish can be used to visualize transient and dynamic structures, with EGFP-LC3 used to monitor phagophore formation during autophagy 104 , Kif17-GFP 105 used to analyze vesicles trafficking towards microtubule plus-ends, and EB1-GFP 106 or EB2-GFP used to view microtubules growing in the plus-end. In combination with vital dyes, these transgenic zebrafish have been utilized extensively to advance our understanding of the dynamics of subcellular structures under physiological conditions and during pathological progression 107 .

Bioimaging tools that enable in vivo analysis

Advanced imaging tools that allow the examination of subcellular structures may facilitate the identification of previously unknown processes. These processes include communication between organelles upon membrane contact 108 , organelle biogenesis (peroxisome biogenesis 109 ), organelle dynamics responding to an environmental cue 110 and organelle trafficking along microtubules 111 . Notably, recent advances in microscopy have greatly enhanced the ability to observe cells in their native state and even monitor in vivo dynamics of organelles as well as ductal structure in the liver in zebrafish 110 , 112 . Motile cilia in the 4th ventricle of the hindbrain and bile duct of the developing liver can be visualized under confocal microscopy after specimens are immunostained with anti-acetylated tubulin (Fig. 2b ) and with anti-cytokeratin 18 antibody (Fig. 3 ), respectively. High-speed, high-resolution, 3-dimensional in vivo imaging has enabled the dissection of dynamic intracellular processes and cellular behavior in response to different environments, which can enable the prediction of physiological conditions at the organism level. In this regard, a drug discovery platform based on organelle biology in zebrafish may play an essential role in the development of precision medicine and next-generation disease therapy.

a , b Using a transgenic zebrafish line, Tg(Tp1:H2BmCherry) , biliary epithelial cell nuclei are labeled red. The bile duct in the developing liver is visualized using the BODIPY FL-C5 dye ( a ) or the anti-cytokeratin 18 antibody ( b ). Unpublished data.

In summary, the zebrafish is a very useful vertebrate animal model in biomedical research and drug discovery. In particular, with the aid of CRISPR-based-knockout technology and big data from next-generation DNA sequencing, functional validation of GWAS candidates in zebrafish is greatly enhancing the ability and accuracy of identifying causative genes and molecular mechanisms underlying the pathogenesis of human genetic diseases. These efforts are fundamental to the establishment of a platform for the future of precision medicine, providing new molecular targets for diagnostic and therapeutic strategies, especially those involving rare diseases.

Streisinger, G., Walker, C., Dower, N., Knauber, D. & Singer, F. Production of clones of homozygous diploid zebra fish (Brachydanio rerio). Nature 291 , 293–296 (1981).

CAS   PubMed   Google Scholar  

Grunwald, D. J. & Streisinger, G. Induction of recessive lethal and specific locus mutations in the zebrafish with ethyl nitrosourea. Genet. Res. 59 , 103–116 (1992).

Kimmel, C. B. Genetics and early development of zebrafish. Trends Genet. 5 , 283–288 (1989).

Haffter, P. et al. The identification of genes with unique and essential functions in the development of the zebrafish, Danio rerio. Development 123 , 1–36 (1996).

Kim, C. H. et al. Repressor activity of Headless/Tcf3 is essential for vertebrate head formation. Nature 407 , 913–916 (2000).

CAS   PubMed   PubMed Central   Google Scholar  

Doyon, Y. et al. Heritable targeted gene disruption in zebrafish using designed zinc-finger nucleases. Nat. Biotechnol. 26 , 702–708 (2008).

Meng, X., Noyes, M. B., Zhu, L. J., Lawson, N. D. & Wolfe, S. A. Targeted gene inactivation in zebrafish using engineered zinc-finger nucleases. Nat. Biotechnol. 26 , 695–701 (2008).

Bedell, V. M. et al. In vivo genome editing using a high-efficiency TALEN system. Nature 491 , 114–118 (2012).

Hsu, P. D., Lander, E. S. & Zhang, F. Development and applications of CRISPR-Cas9 for genome engineering. Cell 157 , 1262–1278 (2014).

Jinek, M. et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337 , 816–821 (2012).

Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339 , 823–826 (2013).

Varshney, G. K., Sood, R. & Burgess, S. M. Understanding and Editing the Zebrafish Genome. Adv. Genet . 92 , 1–52 (2015).

Sung, Y. H. et al. Highly efficient gene knockout in mice and zebrafish with RNA-guided endonucleases. Genome Res . 24 , 125–131 (2014).

Postlethwait, J. H. et al. Vertebrate genome evolution and the zebrafish gene map. Nat. Genet . 18 , 345–349 (1998).

Howe, K. et al. The zebrafish reference genome sequence and its relationship to the human genome. Nature 496 , 498–503 (2013).

Shin, J. T. & Fishman, M. C. From Zebrafish to human: modular medical models. Annu Rev. Genomics Hum. Genet . 3 , 311–340 (2002).

Lieschke, G. J. & Currie, P. D. Animal models of human disease: zebrafish swim into view. Nat. Rev. Genet 8 , 353–367 (2007).

Kwan, K. M. et al. The Tol2kit: a multisite gateway-based construction kit for Tol2 transposon transgenesis constructs. Dev. Dyn. 236 , 3088–3099 (2007).

Halpern, M. E. et al. Gal4/UAS transgenic tools and their application to zebrafish. Zebrafish 5 , 97–110 (2008).

Langenau, D. M. et al. Cre/lox-regulated transgenic zebrafish model with conditional myc-induced T cell acute lymphoblastic leukemia. Proc. Natl Acad. Sci. USA 102 , 6068–6073 (2005).

Santoro, M. M. Zebrafish as a model to explore cell metabolism. Trends Endocrinol. Metab. 25 , 546–554 (2014).

Nakayama, H. et al. Anti-obesity natural products tested in juvenile zebrafish obesogenic tests and mouse 3T3-L1 adipogenesis assays. Molecules 25 , https://doi.org/10.3390/molecules25245840 (2020).

Misselbeck, K. et al. A network-based approach to identify deregulated pathways and drug effects in metabolic syndrome. Nat. Commun. 10 , 5215 (2019).

PubMed   PubMed Central   Google Scholar  

Asaoka, Y., Terai, S., Sakaida, I. & Nishina, H. The expanding role of fish models in understanding non-alcoholic fatty liver disease. Dis. Model Mech. 6 , 905–914 (2013).

Nakayama, H. et al. Novel anti-obesity properties of palmaria mollis in zebrafish and mouse models. Nutrients 10 , https://doi.org/10.3390/nu10101401 (2018).

Kim, C. H. et al. Zebrafish elav/HuC homologue as a very early neuronal marker. Neurosci. Lett. 216 , 109–112 (1996).

Itoh, M. et al. Mind bomb is a ubiquitin ligase that is essential for efficient activation of Notch signaling by Delta. Dev. Cell 4 , 67–82 (2003).

Kim, C. H. et al. Neuron-specific expression of a chicken gicerin cDNA in transient transgenic zebrafish. Neurochem. Res . 21 , 231–237 (1996).

Manolio, T. A. Genomewide association studies and assessment of the risk of disease. N. Engl. J. Med. 363 , 166–176 (2010).

Koolen, D. A. et al. A new chromosome 17q21.31 microdeletion syndrome associated with a common inversion polymorphism. Nat. Genet. 38 , 999–1001 (2006).

Kalscheuer, V. M. et al. Disruption of the serine/threonine kinase 9 gene causes severe X-linked infantile spasms and mental retardation. Am. J. Hum. Genet. 72 , 1401–1411 (2003).

Potocki, L. & Shaffer, L. G. Interstitial deletion of 11(p11.2p12): a newly described contiguous gene deletion syndrome involving the gene for hereditary multiple exostoses (EXT2). Am. J. Med. Genet. 62 , 319–325 (1996).

Kim, H. G. et al. Translocations disrupting PHF21A in the Potocki-Shaffer-syndrome region are associated with intellectual disability and craniofacial anomalies. Am. J. Hum. Genet. 91 , 56–72 (2012).

May, M. et al. ZC4H2, an XLID gene, is required for the generation of a specific subset of CNS interneurons. Hum. Mol. Genet. 24 , 4848–4861 (2015).

Choi, J. H. et al. Targeted knockout of a chemokine-like gene increases anxiety and fear responses. Proc. Natl Acad. Sci. USA 115 , E1041–E1050 (2018).

Kim, S. et al. Deficiency of a brain-specific chemokine-like molecule, SAM3, induces cardinal phenotypes of autism spectrum disorders in mice. Sci. Rep. 7 , 16503 (2017).

Stevenson, R. E. & Schwartz, C. E. X-linked intellectual disability: unique vulnerability of the male genome. Dev. Disabil. Res Rev. 15 , 361–368 (2009).

PubMed   Google Scholar  

Lee, Y. R. et al. Mutations in FAM50A suggest that Armfield XLID syndrome is a spliceosomopathy. Nat. Commun. 11 , 3698 (2020).

Millar, R. P. et al. Gonadotropin-releasing hormone receptors. Endocr. Rev. 25 , 235–275 (2004).

Charlton, H. Hypothalamic control of anterior pituitary function: a history. J. Neuroendocrinol. 20 , 641–646 (2008).

Vadakkadath Meethal, S. & Atwood, C. S. The role of hypothalamic-pituitary-gonadal hormones in the normal structure and functioning of the brain. Cell Mol. Life Sci. 62 , 257–270 (2005).

Boehm, U. et al. Expert consensus document: European Consensus Statement on congenital hypogonadotropic hypogonadism-pathogenesis, diagnosis and treatment. Nat. Rev. Endocrinol. 11 , 547–564 (2015).

Kim, H. G. et al. WDR11, a WD protein that interacts with transcription factor EMX1, is mutated in idiopathic hypogonadotropic hypogonadism and Kallmann syndrome. Am. J. Hum. Genet. 87 , 465–479 (2010).

Kyritsis, N. et al. Acute inflammation initiates the regenerative response in the adult zebrafish brain. Science 338 , 1353–1356 (2012).

Park, J. S. et al. Targeted knockout of duox causes defects in zebrafish growth, thyroid development, and social interaction. J. Genet Genomics 46 , 101–104 (2019).

Sharon, G., Sampson, T. R., Geschwind, D. H. & Mazmanian, S. K. The central nervous system and the gut microbiome. Cell 167 , 915–932 (2016).

Cheesman, S. E., Neal, J. T., Mittge, E., Seredick, B. M. & Guillemin, K. Epithelial cell proliferation in the developing zebrafish intestine is regulated by the Wnt pathway and microbial signaling via Myd88. Proc. Natl Acad. Sci. USA 108 (Suppl 1), 4570–4577 (2011).

Park, W. Y., Choe, S. K., Park, J. & Um, J. Y. Black raspberry (Rubus coreanus Miquel) promotes browning of preadipocytes and inguinal white adipose tissue in cold-induced mice. Nutrients 11 , https://doi.org/10.3390/nu11092164 (2019).

Jung, U. J. & Choi, M. S. Obesity and its metabolic complications: the role of adipokines and the relationship between obesity, inflammation, insulin resistance, dyslipidemia and nonalcoholic fatty liver disease. Int J. Mol. Sci. 15 , 6184–6223 (2014).

Flynn, E. J. 3rd, Trent, C. M. & Rawls, J. F. Ontogeny and nutritional control of adipogenesis in zebrafish (Danio rerio). J. Lipid Res. 50 , 1641–1652 (2009).

Minchin, J. E. N. & Rawls, J. F. A classification system for zebrafish adipose tissues. Dis. Model Mech. 10 , 797–809 (2017).

Seth, A., Stemple, D. L. & Barroso, I. The emerging use of zebrafish to model metabolic disease. Dis. Model Mech. 6 , 1080–1088 (2013).

Schlegel, A. & Gut, P. Metabolic insights from zebrafish genetics, physiology, and chemical biology. Cell Mol. Life Sci. 72 , 2249–2260 (2015).

Salmi, T. M., Tan, V. W. T. & Cox, A. G. Dissecting metabolism using zebrafish models of disease. Biochem Soc. Trans. 47 , 305–315 (2019).

Oulmi, Y. & Braunbeck, T. Toxicity of 4-chloroaniline in early life-stages of zebrafish (Brachydanio rerio): I. cytopathology of liver and kidney after microinjection. Arch. Environ. Contam Toxicol. 30 , 390–402 (1996).

Braunbeck, T., Gorge, G., Storch, V. & Nagel, R. Hepatic steatosis in zebra fish (Brachydanio rerio) induced by long-term exposure to gamma-hexachlorocyclohexane. Ecotoxicol. Environ. Saf. 19 , 355–374 (1990).

Peterman, E. M. et al. Neutralization of mitochondrial superoxide by superoxide dismutase 2 promotes bacterial clearance and regulates phagocyte numbers in zebrafish. Infect. Immun. 83 , 430–440 (2015).

Hill, A. J., Teraoka, H., Heideman, W. & Peterson, R. E. Zebrafish as a model vertebrate for investigating chemical toxicity. Toxicol. Sci. 86 , 6–19 (2005).

Basu, S. & Sachidanandan, C. Zebrafish: a multifaceted tool for chemical biologists. Chem. Rev. 113 , 7952–7980 (2013).

Kanungo, J., Cuevas, E., Ali, S. F. & Paule, M. G. Zebrafish model in drug safety assessment. Curr. Pharm. Des. 20 , 5416–5429 (2014).

McGrath, P. & Li, C. Q. Zebrafish: a predictive model for assessing drug-induced toxicity. Drug Disco. Today 13 , 394–401 (2008).

CAS   Google Scholar  

Parng, C. In vivo zebrafish assays for toxicity testing. Curr. Opin. Drug Disco. Devel 8 , 100–106 (2005).

McGrath, P. & Seng, W. L. Use of zebrafish apoptosis assays for preclinical drug discovery. Expert Opin. Drug Disco. 8 , 1191–1202 (2013).

Baxendale, S., van Eeden, F. & Wilkinson, R. The Power of Zebrafish in Personalised Medicine. Adv. Exp. Med. Biol. 1007 , 179–197 (2017).

Fernandez, I., Gavaia, P. J., Laize, V. & Cancela, M. L. Fish as a model to assess chemical toxicity in bone. Aquat. Toxicol. 194 , 208–226 (2018).

Green, A. J. & Planchart, A. The neurological toxicity of heavy metals: a fish perspective. Comp. Biochem Physiol. C. Toxicol. Pharm. 208 , 12–19 (2018).

Horzmann, K. A. & Freeman, J. L. Making waves: new developments in toxicology with the zebrafish. Toxicol. Sci. 163 , 5–12 (2018).

Yoganantharjah, P. & Gibert, Y. The use of the zebrafish model to aid in drug discovery and target validation. Curr. Top. Med Chem. 17 , 2041–2055 (2017).

Chen, Z., Berquez, M. & Luciani, A. Mitochondria, mitophagy, and metabolic disease: towards assembling the puzzle. Cell Stress 4 , 147–150 (2020).

Zhou, W. et al . Neutrophil-specific knockout demonstrates a role for mitochondria in regulating neutrophil motility in zebrafish. Dis. Model Mech. 11 , https://doi.org/10.1242/dmm.033027 (2018).

Chen, D. et al. Deletion of Gtpbp3 in zebrafish revealed the hypertrophic cardiomyopathy manifested by aberrant mitochondrial tRNA metabolism. Nucleic Acids Res. 47 , 5341–5355 (2019).

Sasagawa, S. et al. Downregulation of GSTK1 Is a common mechanism underlying hypertrophic cardiomyopathy. Front Pharm. 7 , 162 (2016).

Google Scholar  

Yang, L. Q., Chen, M., Ren, D. L. & Hu, B. Dual oxidase mutant retards mauthner-cell axon regeneration at an early stage via modulating mitochondrial dynamics in zebrafish. Neurosci. Bull. 36 , 1500–1512 (2020).

Soman, S. K., Bazala, M., Keatinge, M., Bandmann, O. & Kuznicki, J. Restriction of mitochondrial calcium overload by mcu inactivation renders a neuroprotective effect in zebrafish models of Parkinson’s disease. Biol. Open 8 , https://doi.org/10.1242/bio.044347 (2019).

Edson, A. J. et al. Dysregulation in the brain protein profile of zebrafish lacking the Parkinson’s disease-related protein DJ-1. Mol. Neurobiol. 56 , 8306–8322 (2019).

Zhang, Q. et al. Deletion of Mtu1 (Trmu) in zebrafish revealed the essential role of tRNA modification in mitochondrial biogenesis and hearing function. Nucleic Acids Res 46 , 10930–10945 (2018).

Munro, B., Horvath, R. & Muller, J. S. Nucleoside supplementation modulates mitochondrial DNA copy number in the dguok -/- zebrafish. Hum. Mol. Genet 28 , 796–803 (2019).

Lee, S. H. et al. REEP5 depletion causes sarco-endoplasmic reticulum vacuolization and cardiac functional defects. Nat. Commun. 11 , 965 (2020).

Nyuzuki, H. et al. Degeneration of dopaminergic neurons and impaired intracellular trafficking in Atp13a2 deficient zebrafish. IBRO Rep. 9 , 1–8 (2020).

Zhang, T. & Peterson, R. T. Modeling lysosomal storage diseases in the zebrafish. Front Mol. Biosci. 7 , 82 (2020).

Tseng, W. C. et al. Modeling Niemann-Pick disease type C1 in zebrafish: a robust platform for in vivo screening of candidate therapeutic compounds. Dis. Model Mech . 11 , https://doi.org/10.1242/dmm.034165 (2018).

Lelieveld, L. T. et al. Role of beta-glucosidase 2 in aberrant glycosphingolipid metabolism: model of glucocerebrosidase deficiency in zebrafish. J. Lipid Res 60 , 1851–1867 (2019).

Jewett, C. E., Appel, B. H. & Prekeris, R. The Rab11 effectors Fip5 and Fip1 regulate zebrafish intestinal development. Biol. Open 9 , https://doi.org/10.1242/bio.055822 (2020).

Meneghetti, G. et al. The epg5 knockout zebrafish line: a model to study Vici syndrome. Autophagy 15 , 1438–1454 (2019).

Fodor, E. et al. Methods to study autophagy in zebrafish. Methods Enzymol. 588 , 467–496 (2017).

Sasaki, T. et al. Autolysosome biogenesis and developmental senescence are regulated by both Spns1 and v-ATPase. Autophagy 13 , 386–403 (2017).

Bian, W. P. et al. Knock-in strategy for editing human and zebrafish mitochondrial DNA using mito-CRISPR/Cas9 system. ACS Synth. Biol. 8 , 621–632 (2019).

Bergamin, G., Cieri, D., Vazza, G., Argenton, F. & Mostacciuolo, M. L. Zebrafish Tg(hb9:MTS-Kaede): a new in vivo tool for studying the axonal movement of mitochondria. Biochim Biophys. Acta 1860 , 1247–1255 (2016).

Mizuno, H., Sassa, T., Higashijima, S., Okamoto, H. & Miyawaki, A. Transgenic zebrafish for ratiometric imaging of cytosolic and mitochondrial Ca2+ response in teleost embryo. Cell Calcium 54 , 236–245 (2013).

Kim, M. J., Kang, K. H., Kim, C. H. & Choi, S. Y. Real-time imaging of mitochondria in transgenic zebrafish expressing mitochondrially targeted GFP. Biotechniques 45 , 331–334 (2008).

Engerer, P. et al. Imaging subcellular structures in the living zebrafish embryo. J Vis Exp , e53456, https://doi.org/10.3791/53456 (2016).

Paquet, D., Plucinska, G. & Misgeld, T. In vivo imaging of mitochondria in intact zebrafish larvae. Methods Enzymol. 547 , 151–164 (2014).

Mandal, A., Pinter, K. & Drerup, C. M. Analyzing neuronal mitochondria in vivo using fluorescent reporters in zebrafish. Front. Cell Dev. Biol. 6 , 144 (2018).

Xu, Y., Chen, M., Hu, B., Huang, R. & Hu, B. In vivo Imaging of mitochondrial transport in single-axon regeneration of zebrafish mauthner cells. Front. Cell Neurosci. 11 , 4 (2017).

Kim, Y. I. et al. Slc25a17 acts as a peroxisomal coenzyme A transporter and regulates multiorgan development in zebrafish. J. Cell Physiol. 235 , 151–165 (2020).

Kim, D. et al. Fis1 depletion in osteoarthritis impairs chondrocyte survival and peroxisomal and lysosomal function. J. Mol. Med (Berl.) 94 , 1373–1384 (2016).

Tanabe, K. et al. Atypical protein kinase C regulates primary dendrite specification of cerebellar Purkinje cells by localizing Golgi apparatus. J. Neurosci. 30 , 16983–16992 (2010).

Gerhart, S. V. et al. The Cx43-like connexin protein Cx40.8 is differentially localized during fin ontogeny and fin regeneration. PLoS ONE 7 , e31364 (2012).

Clark, B. S., Winter, M., Cohen, A. R. & Link, B. A. Generation of Rab-based transgenic lines for in vivo studies of endosome biology in zebrafish. Dev. Dyn. 240 , 2452–2465 (2011).

Ellis, K., Bagwell, J. & Bagnat, M. Notochord vacuoles are lysosome-related organelles that function in axis and spine morphogenesis. J. Cell Biol. 200 , 667–679 (2013).

Yokota, Y. et al. Endothelial Ca 2+ oscillations reflect VEGFR signaling-regulated angiogenic capacity in vivo. Elife 4 , https://doi.org/10.7554/eLife.08817 (2015).

Yeo, S. Y., Kim, M., Kim, H. S., Huh, T. L. & Chitnis, A. B. Fluorescent protein expression driven by her4 regulatory elements reveals the spatiotemporal pattern of Notch signaling in the nervous system of zebrafish embryos. Dev. Biol. 301 , 555–567 (2007).

Peri, F. & Nusslein-Volhard, C. Live imaging of neuronal degradation by microglia reveals a role for v0-ATPase a1 in phagosomal fusion in vivo. Cell 133 , 916–927 (2008).

Cui, J., Sim, T. H., Gong, Z. & Shen, H. M. Generation of transgenic zebrafish with liver-specific expression of EGFP-Lc3: a new in vivo model for investigation of liver autophagy. Biochem Biophys. Res. Commun. 422 , 268–273 (2012).

Bader, J. R., Kusik, B. W. & Besharse, J. C. Analysis of KIF17 distal tip trafficking in zebrafish cone photoreceptors. Vis. Res 75 , 37–43 (2012).

Tran, L. D. et al. Dynamic microtubules at the vegetal cortex predict the embryonic axis in zebrafish. Development 139 , 3644–3652 (2012).

Vacaru, A. M. et al. In vivo cell biology in zebrafish—providing insights into vertebrate development and disease. J. Cell Sci. 127 , 485–495 (2014).

Ng, A. Q. E., Ng, A. Y. E. & Zhang, D. Plasma membrane furrows control plasticity of ER-PM Contacts. Cell Rep. 30 , 1434–1446 e1437 (2020).

Sugiura, A., Mattie, S., Prudent, J. & McBride, H. M. Newly born peroxisomes are a hybrid of mitochondrial and ER-derived pre-peroxisomes. Nature 542 , 251–254 (2017).

Liu, T. L. et al. Observing the cell in its native state: Imaging subcellular dynamics in multicellular organisms. Science 360 , https://doi.org/10.1126/science.aaq1392 (2018).

Yildiz, A. Sorting out microtubule-based transport. Nat. Rev. Mol. Cell Biol. https://doi.org/10.1038/s41580-020-00320-y (2020).

Article   Google Scholar  

Choi, T. Y., Ninov, N., Stainier, D. Y. & Shin, D. Extensive conversion of hepatic biliary epithelial cells to hepatocytes after near total loss of hepatocytes in zebrafish. Gastroenterology 146 , 776–788 (2014).

Download references

Acknowledgements

This work was supported by grants NRF-2020R1I1A3070817 (TYC), NRF-2018M3A9B8021980 (CHK), and MOF-20180430 (SKC). Zebrafish were obtained from the Zebrafish Center for Disease Modeling. Tissue-clearing reagents were kindly provided by Binaree, Inc.

Author information

Authors and affiliations.

Department of Pathology, Digestive Disease Research Institute, Wonkwang University, Iksan, Jeonbuk, 54538, Republic of Korea

Tae-Young Choi

Department of Biomedical Science, Graduate School, Wonkwang University, Iksan, Jeonbuk, 54538, Republic of Korea

Tae-Young Choi & Seong-Kyu Choe

Department of Biology, Chungnam National University, Daejeon, 34134, Republic of Korea

Tae-Ik Choi, Yu-Ri Lee & Cheol-Hee Kim

Department of Microbiology, Wonkwang University, Iksan, Jeonbuk, 54538, Republic of Korea

Seong-Kyu Choe

Institute of Wonkwang Medical Science, Wonkwang University, Iksan, Jeonbuk, 54538, Republic of Korea

You can also search for this author in PubMed   Google Scholar

Corresponding authors

Correspondence to Tae-Young Choi , Seong-Kyu Choe or Cheol-Hee Kim .

Ethics declarations

Conflict of interest.

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Choi, TY., Choi, TI., Lee, YR. et al. Zebrafish as an animal model for biomedical research. Exp Mol Med 53 , 310–317 (2021). https://doi.org/10.1038/s12276-021-00571-5

Download citation

Received : 26 December 2020

Revised : 15 January 2021

Accepted : 18 January 2021

Published : 01 March 2021

Issue Date : March 2021

DOI : https://doi.org/10.1038/s12276-021-00571-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Aligning with the 3rs: alternative models for research into muscle development and inherited myopathies.

• Hashir Mehmood
• Paul R. Kasher
• Gemma L. Walmsley

BMC Veterinary Research (2024)

Speckle statistics as a tool to distinguish collective behaviors of Zebrafish shoals

• Adauto J. F. de Souza
• Antonio. R. de C. Romaguera
• Marcelo L. Lyra

Scientific Reports (2024)

The effect of laboratory diet and feeding on growth parameters in juvenile zebrafish

• Courtney Hillman
• Austin H. Cooper
• Matthew O. Parker

Lab Animal (2024)

Two-photon all-optical neurophysiology for the dissection of larval zebrafish brain functional and effective connectivity

• Lapo Turrini
• Pietro Ricci
• Francesco Saverio Pavone

Communications Biology (2024)

Purification and Characterization of a Novel Anticoagulant Peptide from Protein Hydrolysate of Eupolyphaga sinensis Walker

• Xuefei Zhuang
• Zhishu Tang

International Journal of Peptide Research and Therapeutics (2024)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

An official website of the United States government

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

• Publications
• Account settings
• Advanced Search
• Journal List

Zebrafish as an animal model for biomedical research

Tae-young choi, tae-ik choi, seong-kyu choe, cheol-hee kim.

• Author information
• Article notes
• Copyright and License information

Corresponding author.

Received 2020 Dec 26; Revised 2021 Jan 15; Accepted 2021 Jan 18; Collection date 2021 Mar.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Zebrafish have several advantages compared to other vertebrate models used in modeling human diseases, particularly for large-scale genetic mutant and therapeutic compound screenings, and other biomedical research applications. With the impactful developments of CRISPR and next-generation sequencing technology, disease modeling in zebrafish is accelerating the understanding of the molecular mechanisms of human genetic diseases. These efforts are fundamental for the future of precision medicine because they provide new diagnostic and therapeutic solutions. This review focuses on zebrafish disease models for biomedical research, mainly in developmental disorders, mental disorders, and metabolic diseases.

Subject terms: Disease model, Zebrafish

Animal models: Zebrafish help unlock clues to human disease

With their see-through bodies, low maintenance costs and genetic similarity to humans, zebrafish provide a powerful animal model for studying mental disorders and metabolic diseases in the laboratory. Tae-Young Choi from Wonkwang University, Iksan, South Korea, and coworkers review the many physiological advantages and logistical benefits of rearing these small tropical fish for biomedical research. These include the ease of tissue imaging, the large number of offspring in each generation and the increasing number of genetic techniques available. The researchers highlight the various ways in which zebrafish have contributed to scientists’ understanding of mental disorders and the communication pathways between brain and other organs in the body. They also discuss the potential of zebrafish for tracking metabolism and how it can go awry in various disease settings.

Introduction

For use in genetic studies as an animal model, zebrafish was initially introduced by Streisinger and colleagues 1 in the early 1980s. Large-scale N-ethyl-N-nitrosourea (ENU) mutagenesis was conducted in combination with extensive phenotypic screening 2 , 3 . Further phenotypic characterization of these ENU mutations in most of the major organ systems was performed 4 . However, later positional cloning of each ENU mutation after a forward genetic screen was time-consuming and laborious 5 . Since the increase in resolution of the zebrafish genome map, advanced gene-targeting technologies involving ZFNs, TALENs, and CRISPR/Cas9 6 – 12 , have overcome challenges in generating specific gene-knockout mutations. CRISPR/Cas9 utilizes an efficient reverse genetic approach to provide knockout animals for zebrafish researchers 13 . Furthermore, the high level of genome structure shared between zebrafish and humans (~70% of human genes have at least one obvious zebrafish ortholog, compared to 80% of human genes with mouse orthologs) 14 , 15 has facilitated the use of zebrafish for understanding human genetic diseases. Recent advancements in next-generation sequencing (NGS) coupled with the demand for personalized medicine has further driven zebrafish uses in identifying causal relationships between the genotype and phenotype of various human diseases.

Additionally, zebrafish possess several advantages over rodent models in the study of vertebrate development and disease. These include hundreds of embryos in a single clutch and optical clarity of the developing embryo, which allows live imaging at the organism level 16 , 17 . In addition, the use of tissue-specific transgenic animals can be easily generated under the control of various selected gene promoters. Recent improvement of the Tol2-based transgenic system in zebrafish 18 has allowed the control of gene expression in a spatiotemporal manner by coupling with regulatory elements such as GAL4/UAS or Cre/LoxP 19 , 20 . These advantages allow live imaging of cells and tracking of cellular dynamics in vivo to study the underlying molecular mechanisms of various developing organs.

The necessity of a model organism to recapitulate metabolic symptoms and associated disease development in humans has led to the exploitation of several animal species, among which rodents have been widely employed. For the past several decades, mice have been the leading experimental animal model in the field of biomedical research due to powerful genetic tools, amenable diagnostic parameters that are comparable to those in humans, and standardized protocols for developing, diagnosing, and treating metabolic syndromes. However, factors inherently different from those in humans, such as dietary requirements, lifestyle, and microbiomes, have called for alternative animal model systems to be utilized in parallel 21 . Zebrafish is a fascinating animal model for understanding the human pathogenesis of metabolic diseases and identifying potential therapeutic options 21 . However, all animal models have unique shortcomings, are the zebrafish model is no exception: first, zebrafish are poikilothermal animals living under water. Nonetheless, zebrafish possess metabolic characteristics similar to humans to complement data obtained from other model organisms, including rodents. This possibility has been clearly shown in recent studies 22 – 25 in which drugs that had been approved for alleviating metabolic syndromes in humans were also effective in a zebrafish model.

This review addresses the use of zebrafish as an animal model for biomedical research, mainly in developmental disorders, mental disorders, and communication between the brain and organs. In addition to biomedical research, we also discuss the utility of zebrafish in metabolic control, focusing on cellular metabolic organelles.

Biomedical research I: developmental disorders

During early animal development, an organizer can induce a complete body axis when transplanted to the ventral side of a host embryo. Studies have suggested that head inducers can inhibit Wnt signaling during the early development of anterior brain structures. In zebrafish, for example, it was demonstrated that head defects in the headless mutant were caused by a mutation in T-cell factor 3 5 . Loss of gene function in the headless mutant revealed that headless can repress Wnt target genes. These data provide the first genetic evidence that a component of the Wnt signaling pathway is essential in head/brain formation and patterning in vertebrate animals.

Zebrafish have been a tractable animal model for identifying developing neurons and the in vivo architecture of the brain, from neurogenesis at the early neural plate stage to the adult brain (Fig. 1 ). The zebrafish HuC homolog, which is 89% identical to the human HuC protein, is one of the earliest discovered markers of neuronal precursor cells in zebrafish, which are apparent during neurogenesis as early as the neural plate stage (Fig. 1a ) 26 , 27 . Zebrafish are useful for studying the functional role of novel genes in neuronal development through directed expression studies of the zebrafish nervous system (Fig. 1b ) 28 . In addition, recently, tissue-clearing technology has allowed visualization of neural networks in the whole brain of adult zebrafish (Fig. 1c ). These molecular tools and technologies are useful for investigating phenotypic changes in zebrafish disease models of human developmental disorders.

Fig. 1. Development of the central nervous system in zebrafish.

a Detection of early neuronal precursor cells by whole-mount in situ hybridization with a pan-neuronal marker, huC , at the neural plate stage (10.5 h after fertilization). Unpublished data. b Immunostaining of axonal growth in the spinal cord of one-day-old zebrafish. Double-staining with anti-gicerin antibody and anti-HNK-1 antibody 28 . c Confocal image of myelin structure in an isolated adult zebrafish brain visualized by mbp promoter-driven membrane-tagged GFP, Tg(mbp: mEGFP ) . Arrows indicate the olfactory, optic, and otic nerves. Unpublished data.

The Genome-wide Association Study (GWAS) investigates a genomewide set of variants in human genetic diseases to identify the causative gene variant associated with a particular disease. GWAS data can be used to identify single-nucleotide polymorphisms (SNPs) and other variants in the genome associated with genetic disease 29 . In contrast to the identification of SNPs and variants, phenotypic abnormalities and haploinsufficiency of the various genes are derived from microdeletions or chromosomal translocation of different genomes 30 , 31 . For instance, Potocki-Shaffer syndrome (PSS) is a disorder that affects the development of bones, nerve cells in the brain, and other tissues due to the interstitial deletion of band p11.2 in chromosome 11 32 . Developmental disorders in PSS were investigated using phf21a -knockdown zebrafish, producing developmental abnormalities in the head, face, and jaw, in addition to increased neuronal apoptosis 33 . Another example of a disease studied by zebrafish models is Miles–Carpenter syndrome (MCS), in which syndromic X-linked intellectual disability is characterized by severe intellectual deficit, microcephaly, exotropia, distal muscle wasting, and low digital arches. By whole-exome sequencing of MCS families, ZC4H2 was identified as an MCS gene candidate. ZC4H2 , a zinc-finger protein, is located in Xq11.2, and point mutations in ZC4H2 were found in MCS patients. Homozygous zc4h2 -knockout zebrafish larvae showed motor hyperactivity, abnormal swimming, and continuous jaw movement. Motor hyperactivity was caused by a reduction in V2 GABAergic interneurons, arising from misspecification of neural progenitors in the brain and spinal cord of the zc4h2 -knockout zebrafish 34 . The knockout animals also exhibited contractures of the pectoral fins and abnormal eye positioning, suggestive of exotropia, indicating that zebrafish disease models can be used to study the underlying cellular and molecular mechanisms of human developmental disorders.

Biomedical research II: mental disorders

Mental disorders, also called psychiatric disorders, are characterized by defective behavioral or mental patterns that cause significant distress to the subject. The International Classification of Diseases (ICD) published by the World Health Organization (WHO) is the international standard for classifying medical disease conditions. Over 450 different definitions of mental disorders are represented in the Diagnostic and Statistical Manual of Mental Disorders (DSM), the standard reference for psychiatry published by the American Psychiatric Association. Zebrafish are highly social animals that exhibit shoaling and schooling behaviors and are suitable for social behavioral tests in relation to mental disorders. Using mutagenesis screening, Kim and colleagues recently identified a novel chemokine-like gene family, samdori ( sam ), involved in mental disorders in zebrafish. Among the five sam family members, sam2 is specifically expressed in the habenular nuclei of the brain and is associated with intellectual disability and autism spectrum disorder 35 , 36 . Sam2 -knockout animals (both zebrafish and mouse) showed defects in emotional responses, such as fear and anxiety, that are involved in anxiety-related disorders and/or autism 35 , 36 .

Additionally, by whole-exome sequencing, FAM50A was identified as the causative gene for Armfield X-linked intellectual disability (XLID) syndrome. XLID refers to forms of mental disorders with intellectual disability that are explicitly associated with X-linked recessive inheritance. Approximately 100 genes have been found to be involved in XLID syndrome 37 . XLID accounts for ~16% of all cases of intellectual disability in males, who are more likely to be affected than females. The biological activity of human FAM50A missense variants was functionally validated by rescue experiments in a zebrafish fam50a -knockout model 38 . Using the zebrafish disease model, it was recently found that Armfield XLID syndrome is a spliceosomopathy associated with aberrant mRNA processing during development 38 .

Biomedical research III: communication between the brain and other organs

Human puberty is a dynamic process that initiates the complex interactions of the hypothalamic-pituitary-gonadal axis (HPG axis), which refers to single endocrine glands as individual entities. The HPG axis plays a critical role in developing and regulating many of the body’s systems, particularly reproduction 39 . Gonadotropin-releasing hormone (GnRH), secreted by the hypothalamus in the brain, circulates through the anterior portion of the pituitary hypophyseal portal system and binds to receptors on the secretory cells of the adenohypophysis 40 . In response to GnRH stimulation, these cells produce luteinizing hormone and follicle-stimulating hormone, which circulate in the bloodstream 41 . Therefore, an adolescent develops into a mature adult with a body capable of sexual reproduction 42 . Kallmann syndrome (KS) is a genetic disorder known to prevent a person from starting or fully completing puberty. In a study showing that the WDR11 gene mutation is involved in KS pathogenicity, the zebrafish wdr11 gene was demonstrated to be expressed in the brain region, indicating a potential role for WDR11-EMX1 protein interaction 43 .

Additionally, acute inflammation is known to initiate regenerative response after traumatic injury in the adult zebrafish brain. The cysteinyl leukotriene receptor 1 ( cysltr1 )–leukotriene C4 ( LTC4 ) pathway is required and sufficient for enhanced proliferation and neurogenesis 44 . LTC4, one of the ligands for CysLT1, binds to its receptor Cysltr1 expressed on radial glial cells in the zebrafish brain 44 . In a study by Kyritsis et al., cysltr1 was increasingly expressed on radial glial cells after traumatic brain injury, suggesting cross talk between components of the inflammatory response and the central nervous system during traumatic brain injury 44 .

The nicotinamide adenine dinucleotide phosphate (NADPH) oxidase (NOX) family is involved in the production of reactive oxygen species in response to various extracellular signals. The NOX family member dual oxidase (DUOX) was identified as thyroid NADPH oxidase. In humans, DUOX2 mutations were identified among children diagnosed with congenital hypothyroidism. Recently, it was demonstrated that, in addition to goitrous thyroid glands and growth retardation, defects in anxiety response and social interaction were found in duox -knockout zebrafish 45 . These results suggest that duox -knockout zebrafish could serve as an effective animal model for studies in thyroid development and related neurological diseases, including intellectual disability and autism.

A large percentage of children with ASD are known to have gastrointestinal problems, such as constipation, diarrhea, and abdominal pain. Recent studies on the brain-gut axis have also shown that interactions with host-associated microbial communities, either directly by microbial metabolites or indirectly via immune, metabolic or endocrine systems, can act as sources of environmental cues. Molecular signals from the gut provide environmental cues for communication between the gut and the brain during episodes related to anxiety, depression, cognition or autism spectrum disorder (ASD) 46 . Moreover, modulation of intrinsic signaling pathways and extrinsic cues in resident intestinal bacteria enhances the stability of β-catenin in intestinal epithelial cells, promoting cell proliferation 47 .

Biomedical research IV: metabolic disorders

Zebrafish an animal models for metabolic research.

A high-calorie diet, a sedentary lifestyle, and a family history of metabolic disorders increase the prevalence of risk factors such as low HDL levels, high triglyceride levels, high blood glucose, high blood pressure, and abdominal obesity 48 . Such metabolic disorders may arise from an imbalance between nutritional intake and energy expenditure, leading to the development of serious illnesses, including diabetes, stroke, and fatty liver disease 49 .

In addition to general similarities with human metabolism, zebrafish metabolism also exhibits unique characteristics. Zebrafish embryos consume yolk for the first five days of development, after which they are fed for further growth to prevent them from undergoing fasting. The feeding-to-fasting transition at 5–6 days post fertilization (dpf) has been utilized to develop mechanistic insights into metabolic homeostasis upon energy deprivation 50 , 51 . Another unique feature of zebrafish is the composition and development of adipose tissue. As a poikilothermal animal, zebrafish do not seem to require brown adipose tissue, on which mammals do depend. Adipose development occurs late in development, with the first adipocyte being detected 8 dpf 50 .

Interestingly, late adipogenesis may also provide an experimental setting by which the role of adipose tissue in the pathogenesis of metabolic disorders can be investigated. Modeling metabolism to recapitulate human disorders can be appropriately established during the larval period. Similarly, metabolic disorders can be modeled in adults to explore phenotype references in the presence of all major metabolic organs. Many metabolic similarities and discrepancies between humans and zebrafish and the modeling of different types of metabolic diseases have been reviewed elsewhere 52 – 54 .

Zebrafish models for organelle biology research

Body metabolism is regulated by metabolic organelles, such as the endoplasmic reticulum (ER), mitochondria, peroxisomes, lipid droplets, and lysosomes. Whole-body metabolism is the sum of all metabolic activity of individual organs that originates from the metabolic function of individual cells. The function of subcellular organelles is critical for responding to environmental changes and regulating metabolic outputs to maintain metabolic homeostasis.

Zebrafish have served as an excellent model system to assess in vivo toxicity in response to treatment of a chemical of interest, and numerous studies have illustrated metabolic changes related to mitochondrial function upon chemical treatment 55 – 57 . After an initial study of mitochondrial activity and distribution in zebrafish oocytes reported in 1980, many reports regarding the mitochondrial genome and functional homologs of mitochondrial proteins in zebrafish were published in the late 1990s and early 2000s. More recently, zebrafish models have drawn extensive interest for use in testing a range of bioactive chemicals, including those that induce or disrupt development, improve disease conditions, or induce unfavorable side effects in daily human health or anticancer treatments 58 – 68 . In addition, the use of zebrafish as in vivo models for studying gene functions involved in metabolic activities has recently increased. Among the new molecular tools in developmental genetics, CRISPR/Cas9 is the most recent example of a reverse genetics technique, and mechanistic studies of the regulation of biogenesis, degradation and the quality maintenance of an organelle of interest have been conducted using zebrafish models 69 .

CRISPR/Cas9 is the most advanced gene editing system

Recent findings and the development of CRISPR/Cas9, evolutionary gene-editing machinery that originated from the defense system of bacteria that earned its developers the Nobel Prize in Chemistry in 2020. Highly efficient gene targeting made it possible to edit a gene of interest in any genome. Accordingly, studies utilizing CRISPR/Cas9 in zebrafish have rapidly increased. In particular, studies to elucidate the role of mitochondria in neutrophil motility 70 , tRNA biogenesis and the physiology of cardiomyocytes 71 , 72 , neuronal regeneration 73 , neurodegeneration in Parkinson’s disease 74 , 75 and cellular metabolism regulation of mitochondrial abundance 76 , 77 have been reported. Furthermore, studies illustrating the role of the endoplasmic (sarcoplasmic) reticulum included REEP5 -gene knockout, which was used to elucidate the previously unknown regulation of ER/SR membrane protein organization and stress response in cardiac myocytes 78 . In addition, the demonstration of MCTP (multiple C2 domain proteins with two transmembrane regions) gene function acting as a novel ER calcium sensor was also reported 76 .

Moreover, molecular pathogenesis studies based on the analysis of genes, such as ATP13a 79 , NPC1 80 , 81 , and GBA1 82 to understand Niewmann-Pick disease type C1 (NPC1) and other lysosomal storage diseases resulting from defective intracellular trafficking or lysosome function have been reported. Efforts have also been made to elucidate molecules and regulatory mechanisms leading to autophagosome formation, autolysosome formation, and autophagy 83 – 86 . Recently, a possible knock-in strategy to edit mitochondrial DNA and genomic DNA has been reported 87 , facilitating research on organelle function in metabolic diseases.

Transgenic approach to track organelle dynamics, abundance, and interaction

Mitochondria have long been foci due to their roles in bioenergetics and apoptosis, leading to a plethora of transgenic zebrafish. Several transgenic zebrafish, such as Mnx1:MITO-Kaede 88 , hspa8:MITO-YC2 89 , and MLS-EGFP 90 , have been generated to mark mitochondria with fluorescent proteins GFP, YFP, Kaede, and yellow cameleon (YC), which are induced explicitly by a pan-expression promoter, an inducible heat shock promoter, a cell-type-specific promoter, or a combination of the GAL4-UAS system and are localized to the mitochondria using a mitochondria-targeting sequence 91 . One of the best examples of live mitochondrial imaging was illustrated in sensory axons of Rohon-Beard neurons, in which mitochondrial shape, dynamics, and transport were analyzed quantitatively 92 . A similar in vivo technique using zebrafish has since become popular to demonstrate the connection between mitochondrial behavior and neuronal health 93 , 94 . In addition to mitochondria, other organelles have been studied to reveal their roles during zebrafish development. For instance, a peroxisomal solute carrier, slc25a17 , is involved in the maintenance of functional peroxisomes by showing substrate specificity towards coenzyme A 95 . To visualize peroxisomes in zebrafish embryos in vivo, the transgenic line Tg(Xla.Eef1a:RFP-SKL) was established and used under different metabolic conditions 96 . The use of double transgenic zebrafish allows simultaneous tracking of the dynamics of mitochondria and peroxisomes in vivo, as shown in Fig. 2a .

Fig. 2. Subcellular organelles in the developing zebrafish embryos.

a Using transgenic zebrafish lines 5 dpf, mitochondria, Tg(Xla.Eef1a: MLS-EGFP ) , and peroxisomes, Tg(Xla. Eef1a: RFP-SKL ) , in the skin of the developing larva are visualized. b Motile cilia (green) in the hindbrain 4th ventricle are visualized with anti-acetylated tubulin antibody, and nuclei are shown in red. Unpublished data.

Another example is a transgenic line that marks the Golgi apparatus using the Golgi-Venus together with a cis-Golgi marker, GM-130 97 , to elucidate its role in dendrite specification of Purkinje cells. A trans-Golgi marker, GalT-GFP, was also established to reveal the dynamic localization of a connexin variant that influences cellular behavior 98 . A more systematic approach was applied to the study of secretory pathways, where a series of transgenic lines were generated based on different Rab proteins marking different types of endosomal vesicles 99 . A handful of transgenic lines were added to improve the identification of the cellular secretory pathways, and Lamp2-EGFP was used to mark lysosome-related vacuoles in the zebrafish notochord, GFP-CaaX (mem-GFP) was used to visualize the plasma membrane 100 , and NLS-mCherry or NLS-EGFP was used to the identify nucleus 101 , 102 . Another transgenic zebrafish used to mark the apoptotic cell membrane specifically, Annexin-Cy5, was also generated 103 . Moreover, transgenic zebrafish can be used to visualize transient and dynamic structures, with EGFP-LC3 used to monitor phagophore formation during autophagy 104 , Kif17-GFP 105 used to analyze vesicles trafficking towards microtubule plus-ends, and EB1-GFP 106 or EB2-GFP used to view microtubules growing in the plus-end. In combination with vital dyes, these transgenic zebrafish have been utilized extensively to advance our understanding of the dynamics of subcellular structures under physiological conditions and during pathological progression 107 .

Bioimaging tools that enable in vivo analysis

Advanced imaging tools that allow the examination of subcellular structures may facilitate the identification of previously unknown processes. These processes include communication between organelles upon membrane contact 108 , organelle biogenesis (peroxisome biogenesis 109 ), organelle dynamics responding to an environmental cue 110 and organelle trafficking along microtubules 111 . Notably, recent advances in microscopy have greatly enhanced the ability to observe cells in their native state and even monitor in vivo dynamics of organelles as well as ductal structure in the liver in zebrafish 110 , 112 . Motile cilia in the 4th ventricle of the hindbrain and bile duct of the developing liver can be visualized under confocal microscopy after specimens are immunostained with anti-acetylated tubulin (Fig. 2b ) and with anti-cytokeratin 18 antibody (Fig. 3 ), respectively. High-speed, high-resolution, 3-dimensional in vivo imaging has enabled the dissection of dynamic intracellular processes and cellular behavior in response to different environments, which can enable the prediction of physiological conditions at the organism level. In this regard, a drug discovery platform based on organelle biology in zebrafish may play an essential role in the development of precision medicine and next-generation disease therapy.

Fig. 3. Bile duct formation in the developing zebrafish liver 6 dpf.

a , b Using a transgenic zebrafish line, Tg(Tp1:H2BmCherry) , biliary epithelial cell nuclei are labeled red. The bile duct in the developing liver is visualized using the BODIPY FL-C5 dye ( a ) or the anti-cytokeratin 18 antibody ( b ). Unpublished data.

In summary, the zebrafish is a very useful vertebrate animal model in biomedical research and drug discovery. In particular, with the aid of CRISPR-based-knockout technology and big data from next-generation DNA sequencing, functional validation of GWAS candidates in zebrafish is greatly enhancing the ability and accuracy of identifying causative genes and molecular mechanisms underlying the pathogenesis of human genetic diseases. These efforts are fundamental to the establishment of a platform for the future of precision medicine, providing new molecular targets for diagnostic and therapeutic strategies, especially those involving rare diseases.

Acknowledgements

This work was supported by grants NRF-2020R1I1A3070817 (TYC), NRF-2018M3A9B8021980 (CHK), and MOF-20180430 (SKC). Zebrafish were obtained from the Zebrafish Center for Disease Modeling. Tissue-clearing reagents were kindly provided by Binaree, Inc.

Conflict of interest

The authors declare no competing interests.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Tae-Young Choi, Email: [email protected].

Seong-Kyu Choe, Email: [email protected].

Cheol-Hee Kim, Email: [email protected].

• 1. Streisinger G, Walker C, Dower N, Knauber D, Singer F. Production of clones of homozygous diploid zebra fish (Brachydanio rerio) Nature. 1981;291:293–296. doi: 10.1038/291293a0. [ DOI ] [ PubMed ] [ Google Scholar ]
• 2. Grunwald DJ, Streisinger G. Induction of recessive lethal and specific locus mutations in the zebrafish with ethyl nitrosourea. Genet. Res. 1992;59:103–116. doi: 10.1017/s0016672300030317. [ DOI ] [ PubMed ] [ Google Scholar ]
• 3. Kimmel CB. Genetics and early development of zebrafish. Trends Genet. 1989;5:283–288. doi: 10.1016/0168-9525(89)90103-0. [ DOI ] [ PubMed ] [ Google Scholar ]
• 4. Haffter P, et al. The identification of genes with unique and essential functions in the development of the zebrafish, Danio rerio. Development. 1996;123:1–36. doi: 10.1242/dev.123.1.1. [ DOI ] [ PubMed ] [ Google Scholar ]
• 5. Kim CH, et al. Repressor activity of Headless/Tcf3 is essential for vertebrate head formation. Nature. 2000;407:913–916. doi: 10.1038/35038097. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 6. Doyon Y, et al. Heritable targeted gene disruption in zebrafish using designed zinc-finger nucleases. Nat. Biotechnol. 2008;26:702–708. doi: 10.1038/nbt1409. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 7. Meng X, Noyes MB, Zhu LJ, Lawson ND, Wolfe SA. Targeted gene inactivation in zebrafish using engineered zinc-finger nucleases. Nat. Biotechnol. 2008;26:695–701. doi: 10.1038/nbt1398. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 8. Bedell VM, et al. In vivo genome editing using a high-efficiency TALEN system. Nature. 2012;491:114–118. doi: 10.1038/nature11537. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 9. Hsu PD, Lander ES, Zhang F. Development and applications of CRISPR-Cas9 for genome engineering. Cell. 2014;157:1262–1278. doi: 10.1016/j.cell.2014.05.010. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 10. Jinek M, et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science. 2012;337:816–821. doi: 10.1126/science.1225829. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 11. Mali P, et al. RNA-guided human genome engineering via Cas9. Science. 2013;339:823–826. doi: 10.1126/science.1232033. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 12. Varshney GK, Sood R, Burgess SM. Understanding and Editing the Zebrafish Genome. Adv. Genet. 2015;92:1–52. doi: 10.1016/bs.adgen.2015.09.002. [ DOI ] [ PubMed ] [ Google Scholar ]
• 13. Sung YH, et al. Highly efficient gene knockout in mice and zebrafish with RNA-guided endonucleases. Genome Res. 2014;24:125–131. doi: 10.1101/gr.163394.113. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 14. Postlethwait JH, et al. Vertebrate genome evolution and the zebrafish gene map. Nat. Genet. 1998;18:345–349. doi: 10.1038/ng0498-345. [ DOI ] [ PubMed ] [ Google Scholar ]
• 15. Howe K, et al. The zebrafish reference genome sequence and its relationship to the human genome. Nature. 2013;496:498–503. doi: 10.1038/nature12111. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 16. Shin JT, Fishman MC. From Zebrafish to human: modular medical models. Annu Rev. Genomics Hum. Genet. 2002;3:311–340. doi: 10.1146/annurev.genom.3.031402.131506. [ DOI ] [ PubMed ] [ Google Scholar ]
• 17. Lieschke GJ, Currie PD. Animal models of human disease: zebrafish swim into view. Nat. Rev. Genet. 2007;8:353–367. doi: 10.1038/nrg2091. [ DOI ] [ PubMed ] [ Google Scholar ]
• 18. Kwan KM, et al. The Tol2kit: a multisite gateway-based construction kit for Tol2 transposon transgenesis constructs. Dev. Dyn. 2007;236:3088–3099. doi: 10.1002/dvdy.21343. [ DOI ] [ PubMed ] [ Google Scholar ]
• 19. Halpern ME, et al. Gal4/UAS transgenic tools and their application to zebrafish. Zebrafish. 2008;5:97–110. doi: 10.1089/zeb.2008.0530. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 20. Langenau DM, et al. Cre/lox-regulated transgenic zebrafish model with conditional myc-induced T cell acute lymphoblastic leukemia. Proc. Natl Acad. Sci. USA. 2005;102:6068–6073. doi: 10.1073/pnas.0408708102. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 21. Santoro MM. Zebrafish as a model to explore cell metabolism. Trends Endocrinol. Metab. 2014;25:546–554. doi: 10.1016/j.tem.2014.06.003. [ DOI ] [ PubMed ] [ Google Scholar ]
• 22. Nakayama, H. et al. Anti-obesity natural products tested in juvenile zebrafish obesogenic tests and mouse 3T3-L1 adipogenesis assays. Molecules 25 , 10.3390/molecules25245840 (2020). [ DOI ] [ PMC free article ] [ PubMed ]
• 23. Misselbeck K, et al. A network-based approach to identify deregulated pathways and drug effects in metabolic syndrome. Nat. Commun. 2019;10:5215. doi: 10.1038/s41467-019-13208-z. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 24. Asaoka Y, Terai S, Sakaida I, Nishina H. The expanding role of fish models in understanding non-alcoholic fatty liver disease. Dis. Model Mech. 2013;6:905–914. doi: 10.1242/dmm.011981. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 25. Nakayama, H. et al. Novel anti-obesity properties of palmaria mollis in zebrafish and mouse models. Nutrients 10 , 10.3390/nu10101401 (2018). [ DOI ] [ PMC free article ] [ PubMed ]
• 26. Kim CH, et al. Zebrafish elav/HuC homologue as a very early neuronal marker. Neurosci. Lett. 1996;216:109–112. doi: 10.1016/0304-3940(96)13021-4. [ DOI ] [ PubMed ] [ Google Scholar ]
• 27. Itoh M, et al. Mind bomb is a ubiquitin ligase that is essential for efficient activation of Notch signaling by Delta. Dev. Cell. 2003;4:67–82. doi: 10.1016/s1534-5807(02)00409-4. [ DOI ] [ PubMed ] [ Google Scholar ]
• 28. Kim CH, et al. Neuron-specific expression of a chicken gicerin cDNA in transient transgenic zebrafish. Neurochem. Res. 1996;21:231–237. doi: 10.1007/BF02529139. [ DOI ] [ PubMed ] [ Google Scholar ]
• 29. Manolio TA. Genomewide association studies and assessment of the risk of disease. N. Engl. J. Med. 2010;363:166–176. doi: 10.1056/NEJMra0905980. [ DOI ] [ PubMed ] [ Google Scholar ]
• 30. Koolen DA, et al. A new chromosome 17q21.31 microdeletion syndrome associated with a common inversion polymorphism. Nat. Genet. 2006;38:999–1001. doi: 10.1038/ng1853. [ DOI ] [ PubMed ] [ Google Scholar ]
• 31. Kalscheuer VM, et al. Disruption of the serine/threonine kinase 9 gene causes severe X-linked infantile spasms and mental retardation. Am. J. Hum. Genet. 2003;72:1401–1411. doi: 10.1086/375538. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 32. Potocki L, Shaffer LG. Interstitial deletion of 11(p11.2p12): a newly described contiguous gene deletion syndrome involving the gene for hereditary multiple exostoses (EXT2) Am. J. Med. Genet. 1996;62:319–325. doi: 10.1002/(SICI)1096-8628(19960329)62:3<319::AID-AJMG22>3.0.CO;2-M. [ DOI ] [ PubMed ] [ Google Scholar ]
• 33. Kim HG, et al. Translocations disrupting PHF21A in the Potocki-Shaffer-syndrome region are associated with intellectual disability and craniofacial anomalies. Am. J. Hum. Genet. 2012;91:56–72. doi: 10.1016/j.ajhg.2012.05.005. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 34. May M, et al. ZC4H2, an XLID gene, is required for the generation of a specific subset of CNS interneurons. Hum. Mol. Genet. 2015;24:4848–4861. doi: 10.1093/hmg/ddv208. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 35. Choi JH, et al. Targeted knockout of a chemokine-like gene increases anxiety and fear responses. Proc. Natl Acad. Sci. USA. 2018;115:E1041–E1050. doi: 10.1073/pnas.1707663115. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 36. Kim S, et al. Deficiency of a brain-specific chemokine-like molecule, SAM3, induces cardinal phenotypes of autism spectrum disorders in mice. Sci. Rep. 2017;7:16503. doi: 10.1038/s41598-017-16769-5. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 37. Stevenson RE, Schwartz CE. X-linked intellectual disability: unique vulnerability of the male genome. Dev. Disabil. Res Rev. 2009;15:361–368. doi: 10.1002/ddrr.81. [ DOI ] [ PubMed ] [ Google Scholar ]
• 38. Lee YR, et al. Mutations in FAM50A suggest that Armfield XLID syndrome is a spliceosomopathy. Nat. Commun. 2020;11:3698. doi: 10.1038/s41467-020-17452-6. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 39. Millar RP, et al. Gonadotropin-releasing hormone receptors. Endocr. Rev. 2004;25:235–275. doi: 10.1210/er.2003-0002. [ DOI ] [ PubMed ] [ Google Scholar ]
• 40. Charlton H. Hypothalamic control of anterior pituitary function: a history. J. Neuroendocrinol. 2008;20:641–646. doi: 10.1111/j.1365-2826.2008.01718.x. [ DOI ] [ PubMed ] [ Google Scholar ]
• 41. Vadakkadath Meethal S, Atwood CS. The role of hypothalamic-pituitary-gonadal hormones in the normal structure and functioning of the brain. Cell Mol. Life Sci. 2005;62:257–270. doi: 10.1007/s00018-004-4381-3. [ DOI ] [ PubMed ] [ Google Scholar ]
• 42. Boehm U, et al. Expert consensus document: European Consensus Statement on congenital hypogonadotropic hypogonadism-pathogenesis, diagnosis and treatment. Nat. Rev. Endocrinol. 2015;11:547–564. doi: 10.1038/nrendo.2015.112. [ DOI ] [ PubMed ] [ Google Scholar ]
• 43. Kim HG, et al. WDR11, a WD protein that interacts with transcription factor EMX1, is mutated in idiopathic hypogonadotropic hypogonadism and Kallmann syndrome. Am. J. Hum. Genet. 2010;87:465–479. doi: 10.1016/j.ajhg.2010.08.018. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 44. Kyritsis N, et al. Acute inflammation initiates the regenerative response in the adult zebrafish brain. Science. 2012;338:1353–1356. doi: 10.1126/science.1228773. [ DOI ] [ PubMed ] [ Google Scholar ]
• 45. Park JS, et al. Targeted knockout of duox causes defects in zebrafish growth, thyroid development, and social interaction. J. Genet Genomics. 2019;46:101–104. doi: 10.1016/j.jgg.2019.01.004. [ DOI ] [ PubMed ] [ Google Scholar ]
• 46. Sharon G, Sampson TR, Geschwind DH, Mazmanian SK. The central nervous system and the gut microbiome. Cell. 2016;167:915–932. doi: 10.1016/j.cell.2016.10.027. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 47. Cheesman SE, Neal JT, Mittge E, Seredick BM, Guillemin K. Epithelial cell proliferation in the developing zebrafish intestine is regulated by the Wnt pathway and microbial signaling via Myd88. Proc. Natl Acad. Sci. USA. 2011;108(Suppl 1):4570–4577. doi: 10.1073/pnas.1000072107. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 48. Park, W. Y., Choe, S. K., Park, J. & Um, J. Y. Black raspberry (Rubus coreanus Miquel) promotes browning of preadipocytes and inguinal white adipose tissue in cold-induced mice. Nutrients 11 , 10.3390/nu11092164 (2019). [ DOI ] [ PMC free article ] [ PubMed ]
• 49. Jung UJ, Choi MS. Obesity and its metabolic complications: the role of adipokines and the relationship between obesity, inflammation, insulin resistance, dyslipidemia and nonalcoholic fatty liver disease. Int J. Mol. Sci. 2014;15:6184–6223. doi: 10.3390/ijms15046184. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 50. Flynn EJ, 3rd, Trent CM, Rawls JF. Ontogeny and nutritional control of adipogenesis in zebrafish (Danio rerio) J. Lipid Res. 2009;50:1641–1652. doi: 10.1194/jlr.M800590-JLR200. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 51. Minchin JEN, Rawls JF. A classification system for zebrafish adipose tissues. Dis. Model Mech. 2017;10:797–809. doi: 10.1242/dmm.025759. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 52. Seth A, Stemple DL, Barroso I. The emerging use of zebrafish to model metabolic disease. Dis. Model Mech. 2013;6:1080–1088. doi: 10.1242/dmm.011346. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 53. Schlegel A, Gut P. Metabolic insights from zebrafish genetics, physiology, and chemical biology. Cell Mol. Life Sci. 2015;72:2249–2260. doi: 10.1007/s00018-014-1816-8. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 54. Salmi TM, Tan VWT, Cox AG. Dissecting metabolism using zebrafish models of disease. Biochem Soc. Trans. 2019;47:305–315. doi: 10.1042/BST20180335. [ DOI ] [ PubMed ] [ Google Scholar ]
• 55. Oulmi Y, Braunbeck T. Toxicity of 4-chloroaniline in early life-stages of zebrafish (Brachydanio rerio): I. cytopathology of liver and kidney after microinjection. Arch. Environ. Contam Toxicol. 1996;30:390–402. doi: 10.1007/BF00212299. [ DOI ] [ PubMed ] [ Google Scholar ]
• 56. Braunbeck T, Gorge G, Storch V, Nagel R. Hepatic steatosis in zebra fish (Brachydanio rerio) induced by long-term exposure to gamma-hexachlorocyclohexane. Ecotoxicol. Environ. Saf. 1990;19:355–374. doi: 10.1016/0147-6513(90)90036-5. [ DOI ] [ PubMed ] [ Google Scholar ]
• 57. Peterman EM, et al. Neutralization of mitochondrial superoxide by superoxide dismutase 2 promotes bacterial clearance and regulates phagocyte numbers in zebrafish. Infect. Immun. 2015;83:430–440. doi: 10.1128/IAI.02245-14. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 58. Hill AJ, Teraoka H, Heideman W, Peterson RE. Zebrafish as a model vertebrate for investigating chemical toxicity. Toxicol. Sci. 2005;86:6–19. doi: 10.1093/toxsci/kfi110. [ DOI ] [ PubMed ] [ Google Scholar ]
• 59. Basu S, Sachidanandan C. Zebrafish: a multifaceted tool for chemical biologists. Chem. Rev. 2013;113:7952–7980. doi: 10.1021/cr4000013. [ DOI ] [ PubMed ] [ Google Scholar ]
• 60. Kanungo J, Cuevas E, Ali SF, Paule MG. Zebrafish model in drug safety assessment. Curr. Pharm. Des. 2014;20:5416–5429. doi: 10.2174/1381612820666140205145658. [ DOI ] [ PubMed ] [ Google Scholar ]
• 61. McGrath P, Li CQ. Zebrafish: a predictive model for assessing drug-induced toxicity. Drug Disco. Today. 2008;13:394–401. doi: 10.1016/j.drudis.2008.03.002. [ DOI ] [ PubMed ] [ Google Scholar ]
• 62. Parng C. In vivo zebrafish assays for toxicity testing. Curr. Opin. Drug Disco. Devel. 2005;8:100–106. [ PubMed ] [ Google Scholar ]
• 63. McGrath P, Seng WL. Use of zebrafish apoptosis assays for preclinical drug discovery. Expert Opin. Drug Disco. 2013;8:1191–1202. doi: 10.1517/17460441.2013.825244. [ DOI ] [ PubMed ] [ Google Scholar ]
• 64. Baxendale S, van Eeden F, Wilkinson R. The Power of Zebrafish in Personalised Medicine. Adv. Exp. Med. Biol. 2017;1007:179–197. doi: 10.1007/978-3-319-60733-7_10. [ DOI ] [ PubMed ] [ Google Scholar ]
• 65. Fernandez I, Gavaia PJ, Laize V, Cancela ML. Fish as a model to assess chemical toxicity in bone. Aquat. Toxicol. 2018;194:208–226. doi: 10.1016/j.aquatox.2017.11.015. [ DOI ] [ PubMed ] [ Google Scholar ]
• 66. Green AJ, Planchart A. The neurological toxicity of heavy metals: a fish perspective. Comp. Biochem Physiol. C. Toxicol. Pharm. 2018;208:12–19. doi: 10.1016/j.cbpc.2017.11.008. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 67. Horzmann KA, Freeman JL. Making waves: new developments in toxicology with the zebrafish. Toxicol. Sci. 2018;163:5–12. doi: 10.1093/toxsci/kfy044. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 68. Yoganantharjah P, Gibert Y. The use of the zebrafish model to aid in drug discovery and target validation. Curr. Top. Med Chem. 2017;17:2041–2055. doi: 10.2174/1568026617666170130112109. [ DOI ] [ PubMed ] [ Google Scholar ]
• 69. Chen Z, Berquez M, Luciani A. Mitochondria, mitophagy, and metabolic disease: towards assembling the puzzle. Cell Stress. 2020;4:147–150. doi: 10.15698/cst2020.06.222. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 70. Zhou, W. et al . Neutrophil-specific knockout demonstrates a role for mitochondria in regulating neutrophil motility in zebrafish. Dis. Model Mech. 11 , 10.1242/dmm.033027 (2018). [ DOI ] [ PMC free article ] [ PubMed ]
• 71. Chen D, et al. Deletion of Gtpbp3 in zebrafish revealed the hypertrophic cardiomyopathy manifested by aberrant mitochondrial tRNA metabolism. Nucleic Acids Res. 2019;47:5341–5355. doi: 10.1093/nar/gkz218. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 72. Sasagawa S, et al. Downregulation of GSTK1 Is a common mechanism underlying hypertrophic cardiomyopathy. Front Pharm. 2016;7:162. doi: 10.3389/fphar.2016.00162. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 73. Yang LQ, Chen M, Ren DL, Hu B. Dual oxidase mutant retards mauthner-cell axon regeneration at an early stage via modulating mitochondrial dynamics in zebrafish. Neurosci. Bull. 2020;36:1500–1512. doi: 10.1007/s12264-020-00600-9. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 74. Soman, S. K., Bazala, M., Keatinge, M., Bandmann, O. & Kuznicki, J. Restriction of mitochondrial calcium overload by mcu inactivation renders a neuroprotective effect in zebrafish models of Parkinson’s disease. Biol. Open 8 , 10.1242/bio.044347 (2019). [ DOI ] [ PMC free article ] [ PubMed ]
• 75. Edson AJ, et al. Dysregulation in the brain protein profile of zebrafish lacking the Parkinson’s disease-related protein DJ-1. Mol. Neurobiol. 2019;56:8306–8322. doi: 10.1007/s12035-019-01667-w. [ DOI ] [ PubMed ] [ Google Scholar ]
• 76. Zhang Q, et al. Deletion of Mtu1 (Trmu) in zebrafish revealed the essential role of tRNA modification in mitochondrial biogenesis and hearing function. Nucleic Acids Res. 2018;46:10930–10945. doi: 10.1093/nar/gky758. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 77. Munro B, Horvath R, Muller JS. Nucleoside supplementation modulates mitochondrial DNA copy number in the dguok -/- zebrafish. Hum. Mol. Genet. 2019;28:796–803. doi: 10.1093/hmg/ddy389. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 78. Lee SH, et al. REEP5 depletion causes sarco-endoplasmic reticulum vacuolization and cardiac functional defects. Nat. Commun. 2020;11:965. doi: 10.1038/s41467-019-14143-9. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 79. Nyuzuki H, et al. Degeneration of dopaminergic neurons and impaired intracellular trafficking in Atp13a2 deficient zebrafish. IBRO Rep. 2020;9:1–8. doi: 10.1016/j.ibror.2020.05.002. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 80. Zhang T, Peterson RT. Modeling lysosomal storage diseases in the zebrafish. Front Mol. Biosci. 2020;7:82. doi: 10.3389/fmolb.2020.00082. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 81. Tseng, W. C. et al. Modeling Niemann-Pick disease type C1 in zebrafish: a robust platform for in vivo screening of candidate therapeutic compounds. Dis. Model Mech . 11 , 10.1242/dmm.034165 (2018). [ DOI ] [ PMC free article ] [ PubMed ]
• 82. Lelieveld LT, et al. Role of beta-glucosidase 2 in aberrant glycosphingolipid metabolism: model of glucocerebrosidase deficiency in zebrafish. J. Lipid Res. 2019;60:1851–1867. doi: 10.1194/jlr.RA119000154. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 83. Jewett, C. E., Appel, B. H. & Prekeris, R. The Rab11 effectors Fip5 and Fip1 regulate zebrafish intestinal development. Biol. Open 9 , 10.1242/bio.055822 (2020). [ DOI ] [ PMC free article ] [ PubMed ]
• 84. Meneghetti G, et al. The epg5 knockout zebrafish line: a model to study Vici syndrome. Autophagy. 2019;15:1438–1454. doi: 10.1080/15548627.2019.1586247. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 85. Fodor E, et al. Methods to study autophagy in zebrafish. Methods Enzymol. 2017;588:467–496. doi: 10.1016/bs.mie.2016.10.028. [ DOI ] [ PubMed ] [ Google Scholar ]
• 86. Sasaki T, et al. Autolysosome biogenesis and developmental senescence are regulated by both Spns1 and v-ATPase. Autophagy. 2017;13:386–403. doi: 10.1080/15548627.2016.1256934. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 87. Bian WP, et al. Knock-in strategy for editing human and zebrafish mitochondrial DNA using mito-CRISPR/Cas9 system. ACS Synth. Biol. 2019;8:621–632. doi: 10.1021/acssynbio.8b00411. [ DOI ] [ PubMed ] [ Google Scholar ]
• 88. Bergamin G, Cieri D, Vazza G, Argenton F, Mostacciuolo ML. Zebrafish Tg(hb9:MTS-Kaede): a new in vivo tool for studying the axonal movement of mitochondria. Biochim Biophys. Acta. 2016;1860:1247–1255. doi: 10.1016/j.bbagen.2016.03.007. [ DOI ] [ PubMed ] [ Google Scholar ]
• 89. Mizuno H, Sassa T, Higashijima S, Okamoto H, Miyawaki A. Transgenic zebrafish for ratiometric imaging of cytosolic and mitochondrial Ca2+ response in teleost embryo. Cell Calcium. 2013;54:236–245. doi: 10.1016/j.ceca.2013.06.007. [ DOI ] [ PubMed ] [ Google Scholar ]
• 90. Kim MJ, Kang KH, Kim CH, Choi SY. Real-time imaging of mitochondria in transgenic zebrafish expressing mitochondrially targeted GFP. Biotechniques. 2008;45:331–334. doi: 10.2144/000112909. [ DOI ] [ PubMed ] [ Google Scholar ]
• 91. Engerer, P. et al. Imaging subcellular structures in the living zebrafish embryo. J Vis Exp , e53456, 10.3791/53456 (2016). [ DOI ] [ PMC free article ] [ PubMed ]
• 92. Paquet D, Plucinska G, Misgeld T. In vivo imaging of mitochondria in intact zebrafish larvae. Methods Enzymol. 2014;547:151–164. doi: 10.1016/B978-0-12-801415-8.00009-6. [ DOI ] [ PubMed ] [ Google Scholar ]
• 93. Mandal A, Pinter K, Drerup CM. Analyzing neuronal mitochondria in vivo using fluorescent reporters in zebrafish. Front. Cell Dev. Biol. 2018;6:144. doi: 10.3389/fcell.2018.00144. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 94. Xu Y, Chen M, Hu B, Huang R, Hu B. In vivo Imaging of mitochondrial transport in single-axon regeneration of zebrafish mauthner cells. Front. Cell Neurosci. 2017;11:4. doi: 10.3389/fncel.2017.00004. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 95. Kim YI, et al. Slc25a17 acts as a peroxisomal coenzyme A transporter and regulates multiorgan development in zebrafish. J. Cell Physiol. 2020;235:151–165. doi: 10.1002/jcp.28954. [ DOI ] [ PubMed ] [ Google Scholar ]
• 96. Kim D, et al. Fis1 depletion in osteoarthritis impairs chondrocyte survival and peroxisomal and lysosomal function. J. Mol. Med (Berl.) 2016;94:1373–1384. doi: 10.1007/s00109-016-1445-9. [ DOI ] [ PubMed ] [ Google Scholar ]
• 97. Tanabe K, et al. Atypical protein kinase C regulates primary dendrite specification of cerebellar Purkinje cells by localizing Golgi apparatus. J. Neurosci. 2010;30:16983–16992. doi: 10.1523/JNEUROSCI.3352-10.2010. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 98. Gerhart SV, et al. The Cx43-like connexin protein Cx40.8 is differentially localized during fin ontogeny and fin regeneration. PLoS ONE. 2012;7:e31364. doi: 10.1371/journal.pone.0031364. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 99. Clark BS, Winter M, Cohen AR, Link BA. Generation of Rab-based transgenic lines for in vivo studies of endosome biology in zebrafish. Dev. Dyn. 2011;240:2452–2465. doi: 10.1002/dvdy.22758. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 100. Ellis K, Bagwell J, Bagnat M. Notochord vacuoles are lysosome-related organelles that function in axis and spine morphogenesis. J. Cell Biol. 2013;200:667–679. doi: 10.1083/jcb.201212095. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 101. Yokota, Y. et al. Endothelial Ca 2+ oscillations reflect VEGFR signaling-regulated angiogenic capacity in vivo. Elife 4 , 10.7554/eLife.08817 (2015). [ DOI ] [ PMC free article ] [ PubMed ]
• 102. Yeo SY, Kim M, Kim HS, Huh TL, Chitnis AB. Fluorescent protein expression driven by her4 regulatory elements reveals the spatiotemporal pattern of Notch signaling in the nervous system of zebrafish embryos. Dev. Biol. 2007;301:555–567. doi: 10.1016/j.ydbio.2006.10.020. [ DOI ] [ PubMed ] [ Google Scholar ]
• 103. Peri F, Nusslein-Volhard C. Live imaging of neuronal degradation by microglia reveals a role for v0-ATPase a1 in phagosomal fusion in vivo. Cell. 2008;133:916–927. doi: 10.1016/j.cell.2008.04.037. [ DOI ] [ PubMed ] [ Google Scholar ]
• 104. Cui J, Sim TH, Gong Z, Shen HM. Generation of transgenic zebrafish with liver-specific expression of EGFP-Lc3: a new in vivo model for investigation of liver autophagy. Biochem Biophys. Res. Commun. 2012;422:268–273. doi: 10.1016/j.bbrc.2012.04.145. [ DOI ] [ PubMed ] [ Google Scholar ]
• 105. Bader JR, Kusik BW, Besharse JC. Analysis of KIF17 distal tip trafficking in zebrafish cone photoreceptors. Vis. Res. 2012;75:37–43. doi: 10.1016/j.visres.2012.10.009. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 106. Tran LD, et al. Dynamic microtubules at the vegetal cortex predict the embryonic axis in zebrafish. Development. 2012;139:3644–3652. doi: 10.1242/dev.082362. [ DOI ] [ PubMed ] [ Google Scholar ]
• 107. Vacaru AM, et al. In vivo cell biology in zebrafish—providing insights into vertebrate development and disease. J. Cell Sci. 2014;127:485–495. doi: 10.1242/jcs.140194. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 108. Ng AQE, Ng AYE, Zhang D. Plasma membrane furrows control plasticity of ER-PM Contacts. Cell Rep. 2020;30:1434–1446 e1437. doi: 10.1016/j.celrep.2019.12.098. [ DOI ] [ PubMed ] [ Google Scholar ]
• 109. Sugiura A, Mattie S, Prudent J, McBride HM. Newly born peroxisomes are a hybrid of mitochondrial and ER-derived pre-peroxisomes. Nature. 2017;542:251–254. doi: 10.1038/nature21375. [ DOI ] [ PubMed ] [ Google Scholar ]
• 110. Liu, T. L. et al. Observing the cell in its native state: Imaging subcellular dynamics in multicellular organisms. Science 360 , 10.1126/science.aaq1392 (2018). [ DOI ] [ PMC free article ] [ PubMed ]
• 111. Yildiz A. Sorting out microtubule-based transport. Nat. Rev. Mol. Cell Biol. 2020 doi: 10.1038/s41580-020-00320-y. [ DOI ] [ PubMed ] [ Google Scholar ]
• 112. Choi TY, Ninov N, Stainier DY, Shin D. Extensive conversion of hepatic biliary epithelial cells to hepatocytes after near total loss of hepatocytes in zebrafish. Gastroenterology. 2014;146:776–788. doi: 10.1053/j.gastro.2013.10.019. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• View on publisher site
• PDF (1.6 MB)
• Collections

Similar articles

Cited by other articles, links to ncbi databases.

• Download .nbib .nbib
• Format: AMA APA MLA NLM

Add to Collections

A literature review of flavonoids and lifespan in model organisms

Affiliation.

• 1 Institute of Human Nutrition and Food Science, University of Kiel,Germany.
• PMID: 27609098
• DOI: 10.1017/S0029665116000720

Epidemiological data on consumption of flavonoid-containing food points to the notion that some of these secondary plant metabolites may favour healthy ageing. The aim of the present paper was to review the literature on lifespan extension by flavonoids in worms, flies and mice. In most studies, worms and flies experienced lifespan extension when supplemented with flavonoids either as extracts or single compounds. Studies with mutant worms and flies give hints as to which gene products may be regulated by flavonoids and consequently enhance longevity. We discuss the data considering putative mechanisms that may underlie flavonoid action such as energy-restriction-like effects, inhibition of insulin-like-growth-factor signalling, induction of antioxidant defence mechanisms, hormesis as well as antimicrobial properties. However, it remains uncertain whether human lifespan could be prolonged by increased flavonoid intake.

Keywords: BE blueberry extract; BTE black tea extract; EGCG epigallocatechin gallate; ER energy restriction; FOXO forkhead box O; GTE green tea extract; IGF insulin-like growth factor; PG pomegranate powder; SOD superoxide dismutases; Catechin; Longevity; Polyphenols; Quercetin.

Publication types

• Anti-Infective Agents / pharmacology
• Antioxidants / pharmacology*
• Caenorhabditis elegans / drug effects
• Dietary Supplements
• Drosophila melanogaster / drug effects
• Flavonoids / pharmacology*
• Longevity / drug effects*
• Plant Extracts / pharmacology
• Anti-Infective Agents
• Antioxidants
• Plant Extracts