19.1.14: Zebrafish - Biology

19.1.14: Zebrafish - Biology

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The zebrafish, Danio rerio, has become another popular "model" organism with which to study fundamental biological questions.

The zebrfish is a small (1–1.5 inches)(2.5–3.8 cm) freshwater fish that grows easily in aquaria (it is available at many pet stores). Some of its advantages for biologists:

  • It breeds early and often (daily).
  • It is a vertebrate, like us, and thus can provide clues to human biology that invertebrates like Drosophila and Caenorhabditis elegans may not.
  • Its embryos, like those of most fishes, develop outside the body where they can be easily observed (unlike mice).
  • Its embryos are transparent so defects in development can be seen easily.
  • Individual cells in the embryo can be labeled with a fluorescent dye and their fate followed.
  • Embryonic development is quick (they hatch in two days).
  • They can absorb small molecules, such as mutagens, from the aquarium water.
  • Individual cells - or clusters of cells - can be transplanted to other locations in the embryo (as Mangold did with newt embryos).
  • They can be forced to develop by parthenogenesis to produce at will homozygous animals with either a male-derived or female-derived genome.
  • They can be cloned from somatic cells.
  • They can be made transgenic (like mice and Drosophila)
  • Its genome (1.4 x 109 base pairs) has been sequenced revealing 26,606 protein-coding genes.

Forward and Reverse Genetics


Since Mendel's time, most genetics has involved

  • observing an interesting phenotype
  • tracking down the gene responsible for it.

So this "forward" genetics proceeds from phenotype -> genotype.

Some examples:

  • Mendel's work
  • RFLP analysis of large families
  • The one gene - one enzyme theory

These methods have been called "forward" genetics to distinguish them from a more recent approach, which has become an urgent priority with the successes of genome sequencing.


Rapid methods of DNA sequencing has generated a vast amount of data. Thousands of suspected genes have been revealed (e.g., finding open reading frames - ORFs), but the function of many of them is still unknown.

But now with a knowledge of the DNA sequence of a gene of unknown function, one can use methods for suppressing that particular gene ("knockdown") and then observe the effect on the phenotype.

So this "reverse" genetics proceeds from genotype -> phenotype.

Reverse genetics has been applied successfully to

  • plants
  • mice
  • C. elegans
  • zebrafish

For example, the function of a mysterious gene sequence in Danio can be studied by

  • synthesizing a short antisense oligonucleotide complementary to a section of the gene.
  • The oligonucleotide is chemically-modified to make it more stable than a fragment of RNA.
  • Binding to its complementary sequence on the messenger RNA (mRNA) produced by transcription of the animal's gene, blocks ("knocks down") gene expression by
    • preventing translation or
    • disrupting normal splicing of the mRNA.

Because we share so many similar gene sequences (orthologous genes) with Danio, if one can discover the function of the gene in Danio, then we have a better idea of the role of its ortholog in humans.


Carbohydrates are a ubiquitous fuel in biology. They are used as an energy source in most organisms, ranging from bacteria to humans. Increasing evidence suggests that high-carbohydrate diets are a potential risk factor for metabolic syndrome and type 2 diabetes (T2D) independent of energy intake [1,2,3,4,5]. However, the regulation of carbohydrate metabolism is complicated. In addition, unlike vigorous lipid metabolism research, large prospective studies that evaluate the relationship between carbohydrates and associated metabolic diseases have not been performed yet, and a systematic evaluation of high-sugar diets is still lacking. Consequently, there is an urgent need to analyze these areas comprehensively [6].

Existing studies have shown that the control and regulation of glucose homeostasis vary greatly between different organisms, leading to corresponding fluctuations in blood glucose levels across different classes of vertebrates. In these studies, researchers came to the realization that blood glucose concentrations among vertebrate groups are quite heterogeneous [7,8,9]. For example, in mammals, the blood glucose concentration is approximately 7 mM birds have an almost 2-fold higher blood glucose concentration than mammals, and the concentrations in fish and amphibians are definitely lower than those in mammals [10]. In addition, glucose levels demonstrate intraspecies variability in fish, amphibians, reptiles, birds and mammals. While the control and regulation of carbohydrate/glucose homeostasis are currently well known in representative mammalian species (especially in rodents and humans), some of the dogmas created around these species may do not seem extend to other vertebrates. Glucose homeostasis in amphibians and reptiles has not received much attention. Thanks to the importance of fish in aquaculture [11,12,13], a significant number of studies have reported the control of glucose homeostasis in numerous fish species. Teleost fish are generally considered to be glucose intolerant. In fish, particularly in carnivorous species, prolonged postprandial hyperglycaemia is generally observed after feeding a carbohydrate-rich diet [8, 10]. Glucose regulation in fish has been discussed in numerous studies [12, 14,15,16,17]. Existing studies have shown that zebrafish have similar insulin dependence regulatory mechanisms as mammals. The genes and proteins responsible for the regulation of glucose metabolism, have been identified and demonstrated have similar regulation patterns and activity in zebrafish and mammalian [18,19,20]. There are also clear similarities and differences in body temperature, physiological, hormonal regulation, dietary carbohydrate use and so on when fish (at least Oncorhynchus) are compared with mammals. However, exploring divergence phenomena can be difficult. Currently, the physiological and molecular basis of this apparent glucose intolerance in fish is not fully understood. The reasons why basal glycaemia is different between fish and mammals and an understanding of the differences in carbohydrate metabolism amongst species are uncharted territory.

Over the past decade, molecular tools have been used to address some of the downstream components of carbohydrate/glucose metabolism and its regulatory processes, and these results have been used to better understand the roles played by carbohydrates and their regulatory paths. However, most results regarding a single gene or several related genes in single species tend to show strong sample biases and may not extrapolate from one species to another. Only genome-wide comparative approaches, which have the capacity to capture these multi-dimensional signals, can help achieve a systems-level understanding of the molecular underpinnings of carbohydrate metabolism [21, 22]. Nowadays, with the development of high-throughput sequencing technology and the availability of multiple, complete genomes of diverse life forms, comparative analysis can be used to provide a new qualitative perspective on homologous relationships between genes. This analysis, in turn, will enable a deeper understanding of the general trends in the evolution of genomic complexity and lineage-specific adaptations. Therefore, to confirm the complexity and heterogeneity of carbohydrate/glucose homeostasis, we adopted a comparative genomics approach in this study to compare all possible genes in carbohydrate/glucose metabolic pathway as a whole rather than analysing a single gene, pathway or species individually. We first constructed a database of every carbohydrate/glucose gene in Danio rerio (zebrafish), Xenopus tropicalis (frog), Gallus gallus (chicken), Mus musculus (mouse) and Homo sapiens (human) and further annotated the functions of these genes in type 2 diabetes. Afterwards, we systematically compared these genes in the Danio rerio, Xenopus tropicalis, Gallus gallus, Mus musculus and Homo sapiens genomes. This study provides the first systematic genomic insights into carbohydrate/glucose metabolism, which may provide a reference for the prevention and therapy of human type 2 diabetes and may reduce aquaculture industry costs due to glucose intolerance.


Compared to the terrestrial surface, space represents a hostile environment, characterized by the combination of microgravity, and a peculiar radiative environment, which could lead to severe health issues for astronaut crews engaged in long-term missions. Among these factors, exposure to radiation dominated by particle shots and GCR of extremely high energy is of special concern [1, 2]. Efficient shielding of such radiation is very difficult, considering the mass constraints which spaceships need to respect. Therefore, it has been noted that “lack of knowledge about the biological effects of, and responses to, space radiation is the single most important factor limiting the prediction of radiation risk associated with human space exploration” [3, 4]. The observations from the ExoMars Trace Gas Orbiter indicate that a 6-month mission to Mars would imply a radiation dose equal to 60% of the limit which is commonly recommended for the full career of an astronaut [5]. Without major technological leaps in shielding strategies [6], intrinsic or induced biological resilience to space radiation chronic exposure will probably be among the crucial factors to decide about risk acceptability. Individual sensitivity to acute or chronic exposure to radiation is dependent on genetic background [7]. Following recent developments in sequencing technologies, determination of individual genomes and acquisition of multi-omic information on individuals' biological samples has become a relatively low-cost routine. In theory, these resources could allow for the screening of many crew candidates, to identify those possessing particularly sensitive or resistant biological backgrounds. However, our knowledge of the genetic and biological traits associated with sensitivity to space radiation is still very limited [7]. NASA has underscored four risks that may imply important health concerns for astronauts: acute radiation syndrome, carcinogenesis, degenerative tissue alterations, and central nervous system (CNS) loss of performance [3]. Among them, the latter is particularly difficult to understand and predict. Nevertheless, recent reports have started to shed some light on this issue [8, 9].

In this review, we will briefly summarize the peculiar features of space radiation and the problems posed by its simulation. We will then highlight established and more recent studies on the impact of ionizing radiations and/or space conditions on CNS structure and function, in humans and experimental models. In particular, we will try to summarize the experiments that, in our opinion, are more informative with regard to functional CNS changes that may derive from the exposure of mammalian brain to mission-relevant doses of HZE particles. For a deeper perspective on these topics, the reader is referred to more extensive surveys [10�]. Afterwards, we will review the studies on genetic factors affecting the general sensitivity to radiation. Finally, we will highlight the experimental models that could provide fundamental insight about genetic and biological factors influencing the response of mature neural networks to space radiation, with particular regard to C. elegans.


Here we discuss how the multi-hit combinations identified above can be used to identify carcinogenic (driver) and non-carcinogenic (passenger) mutations within genes. We also illustrate how these combinations may be used to design a combination therapy targeting the specific genetic mutations responsible for individual instances of cancer.

Distinguishing between driver and passenger mutations

The method used to identify multi-hit combinations uses a mutation frequency based approach to preferentially select driver genes instead of passenger genes, i.e. the selected genes have a significantly higher mutation frequency in tumor samples compared to normal samples. For each gene, the mutation frequency in normal samples is considered to be approximately representative of the background mutation frequency for the gene. However, within these genes not all mutations are carcinogenic.

The combinations found above provide a starting point for examining a smaller subset of genes more closely to identify specific carcinogenic mutations within these genes. In identifying the multi-hit combinations, we did not take into consideration the location of mutations within genes. Clearly there are locations within a gene where certain mutations are unlikely to affect the function of the gene product. Such mutations can result in false positives and contribute to the large number (65%) of tumor samples containing multiple combinations (Fig. 4). Consider for example, the 2-hit combination of mutations in IDH1 and MUC6 in brain lower grade glioma (LGG) tumor samples. Of the 479 LGG tumor samples, 134 (28%) contain mutations in both IDH1 and MUC6, while 5 (1.5%) of 333 normal tissue samples contain a mutation in both these genes (Fig. 6). Comparing the mutations within these genes for normal and tumor samples may reveal which are carcinogenic and which are not. In this example, every one of the tumor samples contains a missense mutation at R132 in IDH1 and no other mutations, while the normal samples do not contain any mutations at this position (Fig. 6). Mutations at R132 in IDH1 have previously been implicated in cancer 33 . On the other hand, the IDH1 mutations seen in the normal samples are unlikely to be carcinogenic. Similarly, mutations at F1989 of MUC6, which occur most frequently in both tumor and normal samples are unlikely to be carcinogenic (Fig. 6). Excluding such non-carcinogenic mutations can reduce the number of false positives and further increase accuracy of our algorithm. In our future work we will develop an automated method to compare and contrast the individual gene loci, so that all of these mutations within genes can be identified. To further improve accuracy of our algorithm, variants that are likely to be carcinogenic can be weighted higher than those that are unlikely to be carcinogenic.

Mutations in normal and lower grade glioma (LGG) tumor samples with mutations in both IDH1 and MUC6. The difference in mutations between normal and tumor samples for the same 2-hit combination can be used to further refine the search algorithm. In the above examples, a missense mutation at R132 in IDH1 is likely to be carcinogenic, whereas mutations at F1989 in MUC6 are unlikely to be carcinogenic. Colored bars represent known functional protein domains. Grey bars represent regions of unknown function. Green dots represent missense mutations, black dots represent truncating mutations and purple dots represent other protein-altering mutations. Figure generated using cBioPortal (Cerami et al. and Gao et al.) 44,45 .

Some of the genes identified by our approach may not be causative (passenger mutations) even though they may be correlated to cancer incidence. Functional analysis can be used to identify genes in the above set of combinations that are unlikely to be driver genes, even though they may be frequently mutated in tumors 11,34,35 . For example, the affect of specific mutations on gene expression levels can be analyzed to determine if the mutation is likely to have a functional effect. In addition we can analyze the pathways affected by the gene combinations (Tables S19–S22). Studies show that combinations of driver gene mutations generally affect mutually exclusive pathways 36 . Therefore, one of the genes in a multi-hit combination affecting the same pathway may include passenger mutations. Although in most cases multiple different pathways are affected by the gene combinations, Tables S19–S22 shows that in some cases (e.g. MUC6 and MUC12 in BRCA) the same pathway is affected by both genes in the combination. Further analysis would be required to determine if the mutations within one of these genes are passenger mutations.

The search algorithm can be run iteratively to incrementally refine the list of multi-hit combinations by excluding these passenger mutations. The input to our algorithm is a list of genes with mutations for each sample. Genes with only passenger mutations can be excluded from this list to minimize the inclusion of passenger mutations in the resulting multi-hit combinations.

A rational basis for combination therapy

The combinations identified above, with further refinement and clinical validation, may represent a more rational basis for targeted combination therapy, instead of the current “marriages of convenience” 27 with limited biological rationale 26 . A more rational strategy may also reduce the risk of expensive failures such as the phase III trial of imfinzi plus tremelimumab. The combination of therapies for a given patient could be designed to target specific carcinogenic combinations of gene mutations found in the patient. Although only 30 of the 256 genes in the combinations identified above were formally identified as “cancer genes” in the catalog of somatic mutations in cancer (COSMIC), many of the other genes were previously implicated in cancer (Table 2). Therapies that target many of the genes in both these categories may be available or under development. For example, the combination of mutations in TP53 and IGHG1 occur in 41% of HNSC tumor samples in TCGA. Several drugs that can restore TP53 function, deplete mutant TP53 or affect downstream targets are currently in pre-clinical development 37 . siRNA targeted silencing of IGHG1 has been shown to inhibit cell viability and promote apoptosis, which might therefore act as a potential target in cancer gene therapy 38,39 . For patients with this combination of mutations, a combination therapy targeting both these genes may be more effective in combination, than separately.

Materials and Methods

Ethics statement

Animal handling and manipulations were conducted in accordance with the guidelines of the University of Otago Animal Ethics Committee under protocol 48–11.

Sample preparation

Adult zebrafish AB strains used for this study were maintained at the Otago Zebrafish Facility, Department of Pathology, University of Otago. Brains were dissected from 12 males and 12 females, and then each brain was halved through the sagittal plane. Six halved male brains were combined into a pooled sample referred to as Male1. Similarly, Male2, Female1 and Female2 were comprised of a pool of six halved brain. Livers (n = 10) were harvested by dissection from wild type male and female fish, and pooled. Genomic DNA was extracted from each sample using the PureLink Genomic DNA Mini Kit (Invitrogen) according to the manufacturer’s instructions.

RRBS library preparation and sequencing

Bisulfite-converted genomic DNA libraries were prepared according to the previously described methods. 37 , 61 Briefly, genomic DNA was digested overnight with MspI (New England Biolabs), followed by end-repair and addition of 3′ A overhangs. Methylated adaptors (Illumina) with a 3′ T overhang were ligated to the A tailed DNA fragments. For reduced representation, 40 to 220 bp (pre-adaptor-ligation size) fragments were excised from 3% Nusieve agarose gels (Lonza) and bisulfite-converted with EZ DNA methylation Gold kit (Zymo Research). Bisulfite converted libraries were amplified by PCR and sequenced on an Illumina HiSeq2000 sequencer with a single-ended, 49 bp run (Beijing Genomics Institute). FASTQ sequence files were obtained containing sequenced reads for each sample (Fig. S1). For zebrafish liver, 9 million single-ended, 100 bp reads were sequenced (New Zealand Genomics Limited).

Sequence quality check and alignment

For sequenced reads obtained for each individual sample, quality checks of the reads, processing and alignments were performed according to our previously published pipeline. 37 The quality of the sequenced reads in all zebrafish libraries was high with a median Phred score of > 30 till the end of last sequencing cycle (see representative FastQC quality plot in Figure S2 FastQC software package is distributed from Babraham Institute). As a result, trimming of the 3′ end of the reads was not necessary for these libraries. However, for the zebrafish liver RRBS library, we hard-trimmed the sequenced reads from 100 bp to 65 bp as the Phred score values dropped significantly after 65 sequencing cycle. Adaptor sequences were removed from the reads using our in-house cleanadaptors program. 37 For the 49 bp sequenced reads, traces of adaptor sequences in the reads were minimal as confirmed by both cleanadaptors and FastQC (from Illumina sequenced reads FastQC searches for known Illumina adaptor sequences). The sequenced reads were aligned against the zebrafish reference genome Zv9 using the bisulfite alignment program Bismark v0.6.4. 62 The alignments were performed on a Mac Pro with 64 bit duo quad core Intel Xeon processors and with 22 Gb RAM running MacOS 10.6.

RRBS data analysis

From the zebrafish whole genome assembly (Zv9), an in silico reduced representation (RR) genome based on MspI cleavage sites (C^CGG) and fragment sizes of 40–220 bp was generated by the mkrrgenome program. 37 Custom written UNIX and awk (an interpreted programming language) scripts and commands were used to describe the distribution of fragments in the genome. Methylation analysis was performed using the R package of methylKit. 63 Briefly, after alignment by Bismark, the SAM files containing uniquely aligned reads were numerically sorted and then processed in R studio (version 0.97.312) using the methylKit package. CpG sites covered by at least 10 sequenced reads (termed as CpG10) were retained to generate the reference methylome. Each sequenced and filtered CpG site was assigned a percentage methylation score. Coverage and correlation plots were generated by methylKit using sorted SAM file for the samples. Human and mouse brain whole genome bisulfite sequencing data for control samples were downloaded from MethylomeDB 64 and processed with UNIX and awk scripts (see Table S1).

To investigate CpG10 positions, in relation to the gene and CpG features, the SeqMonk feature table information for Zv9 was used. SeqMonk (freely distributed from Babraham Institute) provide .DAT files containing information on CpG islands and genes in zebrafish. These files were parsed by a purpose-written program (identgeneloc), which then identified proximal genes and CpG islands for the CpG10 sites. The resulting information was further processed with awk scripts to generate the distribution of CpG10 positions.

* In the size range of 40–220 bp. † in silico calculation of the total RR genome. ‡ RRBS data based on 100 bp reads, from Smith et al. 65 § Based on the latest Zv9 build. 45

* As indicated by Bismark, this percentage is the sum of two factors: the actual non-CpG methylation in the genome + possible incomplete bisulfite conversion. However, methylKit analysis showed consistent bisulfite conversion (99%) for all the samples. Male1–2, Female1–2 are from zebrafish brain.

* CpG10 refers to the dinucleotide, which are covered by at least 10 reads after sequencing and alignment.

* The methylation percentages for zebrafish brain (Male1–2 and Female 1–2) and liver RRBS samples were calculated on CpG10 as part of the current study. Percentages for zebrafish sperm, egg, muscle and sphere were derived from recently published whole genome bisulfite sequencing. 20 † Four hour post-fertilization.

* These data are generated from Male1 sample, other samples showed similar trend.

Future directions

The field of biomechanics currently benefits from an integrative approach that incorporates biology, physics, and engineering concepts. Similarly, applying an integrative approach that unites the fields of biomechanics, behavior, and evolution has the potential to contribute form-function insights into the evolution of biomechanical performance through time. Predator–prey interactions, in particular, can serve as a model system for integrative inquiry due to their strong effect on fitness and their dependence on locomotor performance.

Uniting diverse studies of predator–prey interactions from distinct fields is possible when classified by the strategies of predators and prey. Each of the strategies presented above have characteristic behavioral and sensory-motor patterns that favor distinct forms of locomotion both for predation and escape. Identifying underlying mechanisms that mediate such interactions enables comparison of even taxonomically distant animals that share a common strategy to reveal common co-evolutionary patterns between predator and prey. Thus, predator–prey interactions represent a model experimental study system for incorporating locomotor ecology into biomechanical inquiry, which increases the applicability of biomechanical results to evolutionary hypotheses.

Indeed, by espousing this integrative approach, it may be possible to determine whether predation strategies favor certain evolutionary patterns. For example, escape from predation is often cited as a potential driver of the expansion or contraction of niches ( Colwell and Fuentes 1975 Sexton et al. 2009 ). The evolutionary transitions from water to s, such as adaptive radiations following invasion of a novel locomotor matrix.


The transition of a society from traditional to market-based diets (termed the nutrition transition) has been associated with profound changes in culture and health ( 1– 4). Many indigenous circumpolar populations are undergoing this transition ( 5– 7), which is associated with increased rates of chronic disease ( 6, 8). Dietary change can be difficult to monitor due in part to the lack of baseline data and in part to the limitations of existing methods for dietary assessment. Self-report methods that are feasible to collect from large populations (e.g., FFQ) are subject to large error and bias ( 9, 10), whereas more reliable methods (e.g., repeated 24-h recall) can be prohibitively expensive ( 11, 12). Dietary biomarkers provide a promising alternative to self-report methods, because they are unbiased, more reliable, and can be measured from archived samples ( 13– 16). We are developing biomarkers of traditional and market intake for the Yup'ik Eskimo population of Southwest Alaska based on the relative abundances of naturally occurring carbon and nitrogen stable isotopes ( 17, 18). Isotopic markers have been widely used as markers of diet in ecological and anthropological studies ( 19– 22). Furthermore, they are inexpensive to measure, precise, and can be measured in multiple tissue types, including serum, RBC, and hair ( 16– 18, 23, 24).

Stable isotope biomarkers are informative in the Yup'ik population, because many commonly consumed traditional and market foods are isotopically distinct ( 25– 29). The nitrogen stable isotope ratio (δ 15 N) indicates consumption of marine mammals and fish ( 25, 28), which are a large component of the traditional Yup'ik diet ( 30– 32). This biomarker has been recently validated for Yup'ik Eskimos based on comparisons with the marine fatty acids EPA and DHA ( 17, 18). Thus, we propose that δ 15 N will indicate consumption of traditional marine foods in this population. The carbon isotope ratio (δ 13 C) is elevated in plants using the C4 (Hatch-Slack) photosynthetic pathway relative to those using the more common C3 (Calvin) photosynthetic pathway ( 33). The most common representatives of these plants in the U.S. agricultural system are corn and sugar cane, which are widely present in the market diet as sweeteners ( 29), as ingredients in processed foods, and indirectly via domestic animals raised on corn ( 34, 35). The carbon isotope ratio has shown moderate associations with reported C4-based sweetener and sweetened beverage intake in the U.S. population ( 23, 24, 36). Here, we propose that δ 13 C will provide an independent biomarker of market food intake for the Yup'ik Eskimo population.

The overall objective of this study was to evaluate isotopic biomarkers of market and traditional food intake in a Yup'ik Eskimo study population. Developing reliable and accurate markers of dietary change for this population could help to predict increases in disease incidence and develop appropriate dietary interventions. The specific aims of this study were 3-fold. First, we determined expected relationships between dietary intake and RBC isotope ratios by completing a comprehensive analysis of δ 15 N and δ 13 C values in traditional and market foods important to the Yup'ik population. Second, we evaluated the association between RBC δ 15 N and reported fish and marine mammal intake, and RBC δ 13 C and reported market food intake based on 4 d of diet records from 230 Yup'ik Eskimos. Finally, we evaluated whether variations in dietary intake by age, community location, and cultural identity that were previously reported for this population based on self-report were also seen using isotopic biomarkers ( 30, 37– 39). The extensive nature of previous dietary assessment in this population provides an ideal framework with which to evaluate the efficacy of these proposed biomarkers.


O constante desenvolvimento de novas tecnologias pode ajudar a elucidar os mecanismos dessa doença complexa e intrigante. As tecnologias de sequenciamento de próxima geração nos fornecerão grandes quantidades de dados e, em breve, seremos capazes de sequenciar todo o genoma do paciente, o que poderia nos ajudar a entender como a mutação interage com os genes modificadores para estabelecer o fenótipo. Essa tecnologia também pode ser aplicada ao sequenciamento de RNA e poderemos ter todo o perfil do transcriptoma do paciente, ajudando-nos a visualizar o impacto da mutação na cascata de sinalização.

Uma rede de expressão comparativa de genes relacionados com cardiomiopatias pode nos dar uma ideia de como esses genes interagem uns com os outros. Por enquanto, podemos dizer que há ainda muito trabalho a fazer até que tenhamos algumas respostas definitivas sobre CMH, mas a triagem genética dos pacientes traz vários benefícios, para o paciente e para os médicos, e deve ser usada na prática médica.

Watch the video: A Pair Of Zebrafish (October 2022).