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Animal Models: A Non-human Primate and Rodent Animal Model Research Platform, Natural History, and Biomarkers to Predict Clinical Outcome

PMID: 34546212
PMCID: PMC8462056
DOI: 10.1097/HP.0000000000001479

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Animal models in eye research: focus on corneal pathologies.

1. Animal Models in Ocular Research

Species	Zebrafish	Mouse	Rat	Rabbit	Nonhuman Primate	Dog	Cat	Pig	Human
Average eye dimension in volume (cm )	0.0035	0.025	0.1	2.6	4.0	4.5	5.1	6.5	7.2
Average eye dimension (axial length in mm)	2	3.4	6.0	17.1	19.8	20.5	21.4	23.9	24
Corneal horizontal diameter (µm)	2–2.5	3.1	5.1	13.4	10.2–11.4	13–17	15.5–18	14.5–16.5	11.8
Average central corneal thickness (µm)	16–20	90–130	160–200	350–400	420–460	500–600	545–650	600–1100	505–560
Cornea shape	Flat	Flat	Flat	Dome	Dome	Dome	Dome	Dome	Dome
Bowman’s membrane	Yes	No	No	No	Yes	No	No	Less developed or absent	Yes
Average tear volume (µL)	/	0.06–0.2	4.6	5–7.5	/	65	32	/	7–12
Average tear turnover rate (%/min)	/	5	/	6.5	/	12	11	/	15
Time between eye blinks	/	5 min	5 min	6 min	6 s	10–20 s	18 s	20–30 s	5 s
Nictitating membranes	No	Yes (non-functional)	Yes (non-functional)	Yes	No	Yes	Yes	Yes	No
Average aqueous humor volume (µL)	/	6	14	287	100–120	770	820–850	260–300	200–310
Lens size (axial length in mm)	1.0	2.2	3.8	6.4–7.9	3–4	6.7	7.7	7.4–7.8	4
Lens shape	Spheroid	Spheroid	Spheroid	≈Spheroid	Ellipsoid	≈Spheroid	≈Spheroid	≈Spheroid	Ellipsoid
Space taken by lens in eyeball	Very High	Very High	Very High	High	Low	Medium	Medium	Medium	Low
Fovea	No	No	No	No	Yes	No	No	Non-functional	Yes
References	[ , , , , , ]	[ , , , , ]	[ , , , , ]	[ , , , , , , ]	[ , , , , ]	[ , , , , , , , , ]	[ , , , , , , ]	[ , , , , , , ]	[ , , , , , , , , , , ]

1.1. Mouse Models

1.2. rabbit models, 1.3. nonhuman primate models, 1.4. porcine models, 1.5. feline models, 1.6. canine models, 1.7. zebrafish models, 2. focus on the most used animal models in corneal pathologies, 2.1. dry eye diseases, 2.1.1. pathology, 2.1.2. animal models, mouse models, rabbit models, 2.2. ocular herpes (herpetic keratitis), 2.2.1. pathology, 2.2.2. animal models, 2.3. corneal repair and transplantation, 2.3.1. pathology, 2.3.2. animal models, animal models for corneal wounds.

Click here to enlarge figure

Animal Models for Corneal Transplantation

2.4. corneal neovascularization, 2.4.1. pathology, 2.4.2. animal models, 2.5. corneal dystrophy, 2.5.1. pathology, 2.5.2. animal models for fuchs endothelial corneal dystrophy, 2.6. diabetic keratopathy, 2.6.1. pathology, 2.6.2. animal models, induction of diabetes in animal models, animal models for the treatment of diabetic keratopathy, 2.7. keratoconus, 2.7.1. pathology, 2.7.2. animal models, 2.8. development of therapeutic devices requiring animal models, 3. conclusions, author contributions, acknowledgments, conflicts of interest.

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Animal Models	Rodents	Rabbits	Nonhuman Primates	Pigs	Felines	Canines	Zebrafish
Average cost per animal	●●○○○	●●●○○	●●●●●	●●●●○	●●●●○	●●●●○	●○○○○
Space required	●●○○○	●●●○○	●●●●●	●●●●○	●●●●○	●●●●○	●○○○○
Breeding rate	●●●●○	●●●○○	●○○○○	●●○○○	●●○○○	●●○○○	●●●●●
Feasibility of high-throughput screening	●●●●○	●●○○○	●○○○○	●○○○○	●○○○○	●●○○○	●●●●●
Availability of mutant, transgenic, and genetically modified strains	●●●●●	●●●○○	●○○○○	●●○○○	●●○○○	●●○○○	●●●●○
Eye anatomical similarity to humans	●●○○○	●●●○○	●●●●●	●●●●○	●●●●○	●●●●○	●○○○○
Eye genetic similarity to humans	●●○○○	●●●○○	●●●●●	●●●●○	●●●●○	●●●●○	●●○○○
Study of associated corneal pathologies	DED HSV Repair Transplantation Neovascularization FECD DK KC	DED HSV Repair Transplantation Neovascularization FECD DK KC Contact lenses	HSV Repair Transplantation FECD Contact lenses	Repair Transplantation DK	Repair Transplantation FECD DK	DED DK	Repair Corneal dystrophy Drug-related oculotoxicity

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Loiseau, A.; Raîche-Marcoux, G.; Maranda, C.; Bertrand, N.; Boisselier, E. Animal Models in Eye Research: Focus on Corneal Pathologies. Int. J. Mol. Sci. 2023 , 24 , 16661. https://doi.org/10.3390/ijms242316661

Loiseau A, Raîche-Marcoux G, Maranda C, Bertrand N, Boisselier E. Animal Models in Eye Research: Focus on Corneal Pathologies. International Journal of Molecular Sciences . 2023; 24(23):16661. https://doi.org/10.3390/ijms242316661

Loiseau, Alexis, Gabrielle Raîche-Marcoux, Cloé Maranda, Nicolas Bertrand, and Elodie Boisselier. 2023. "Animal Models in Eye Research: Focus on Corneal Pathologies" International Journal of Molecular Sciences 24, no. 23: 16661. https://doi.org/10.3390/ijms242316661

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Published: 07 May 2020

Zebrafish as an alternative animal model in human and animal vaccination research

Ricardo Lacava Bailone 1 , 2 ,
Hirla Costa Silva Fukushima 3 ,
Bianca Helena Ventura Fernandes 4 ,
Luís Kluwe De Aguiar 5 ,
Tatiana Corrêa 6 ,
Helena Janke 6 ,
Princia Grejo Setti 6 ,
Roberto De Oliveira Roça 2 &
Ricardo Carneiro Borra 6

Laboratory Animal Research volume 36 , Article number: 13 ( 2020 ) Cite this article

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Much of medical research relies on animal models to deepen knowledge of the causes of animal and human diseases, as well as to enable the development of innovative therapies. Despite rodents being the most widely used research model worldwide, in recent decades, the use of the zebrafish ( Danio rerio ) model has exponentially been adopted among the scientific community. This is because such a small tropical freshwater teleost fish has crucial genetic, anatomical and physiological homology with mammals. Therefore, zebrafish constitutes an excellent experimental model for behavioral, genetic and toxicological studies which unravels the mechanism of various human diseases. Furthermore, it serves well to test new therapeutic agents, such as the safety of new vaccines. The aim of this review was to provide a systematic literature review on the most recent studies carried out on the topic. It presents numerous advantages of this type of animal model in tests of efficacy and safety of both animal and human vaccines, thus highlighting gains in time and cost reduction of research and analyzes.

Introduction

The role of the immune system is to protect a body against bacterial, viral, or any foreign antigen invasions. In order to improve protection, vaccination is used to boost immunity against diseases caused by microorganisms. It typically contains a less virulent agent that triggers a reaction, thus, stimulating a body’s immune system to recognize it as foreign. In the process, a body’s defense mechanism learns to recognize and destroy a microorganism, its toxins or surface proteins [ 94 ] every time an invasion is identified. The use of vaccination is important because it promotes the stimulation of the body’s defense mechanisms and the development of both individual and collective immunity. Vaccination can act on specific (adaptive) and nonspecific (innate) immune responses unlike immunostimulants which only act on innate response. In addition, it should be noted the role vaccines play in controlling diseases as preventative as well as non-therapeutic measures. Therefore, the body is able to produce antibodies that recognize, signal and neutralize pathogens or particular cellular responses which detect the specific antigens with high efficiency and affinity. As a result, vaccines protect the body against future infections [ 27 ] thus reducing the need for the use of antibiotics and other types of drugs.

Despite the study of immunology in fish being more recent compared to those of humans and in animals, the concepts and techniques used are similar [ 60 ]. The study of the use of vaccines in fish is an area of fast-growing. As aquaculture expands and the need to control pathogens becomes more pressing, the commercial vaccination of different varieties of fish is already a reality in many countries. It aids in the prevention of diseases that could pose health risks to the shoal as well as in avoiding the economic losses due to mortality caused by infection. It reduces the contamination of water bodies by the excessive use of antibiotics, and the reduction of final fish product quality [ 5 , 24 , 42 , 79 , 100 ].

The Zebrafish model has been widely used in both animal and human health research and, more recently, in aquaculture too. In spite of rodents being the most widely used research model in the world, in recent decades the use of the zebrafish ( Danio rerio ) model has exponentially increased among the scientific community. It follows the principle of 3Rs (replacement, reduction, and refinement) as required by a multiplicity of national and international regulatory bodies. Furthermore, the use of zebrafish model results in a reduction of time and use of resources when compared to those more established animals’ models. It also provides a greater informational and predictive capacity when compared to in vitro results [ 53 ]. Thus, using the zebrafish model, it is possible to replace and reduce the use of mammals in research as well as mitigate problems related to the welfare of those animals. Furthermore, zebrafish is used as confirmatory models of the positive previously obtained results, thus, having the ability to refine the findings [ 2 ]. A review of the literature was carried out aiming at presenting the most recent information on vaccination of fish, which brings to light the advantages of this animal model in tests of efficacy and safety of both animal and human vaccines.

Material and methods

The present study was based on a systematic literature review carried out using databases such as Science Direct, Google Scholar and SciELO (Scientific Electronic Library Online). Emphasis was given on identifying publications using search words and terms containing ‘human vaccination’ and ‘animal vaccination’. Particularly, the main key-words searched included ‘Zebrafish model’, ‘vaccine safety’, ‘diseases’, ‘infection’ and ‘toxicology’. Initially, 99 publications were identified which included books, rulings and articles published by international scientific journals of high impact factor. The publications were selected according to relevance and timeliness. 19% of the articles used were published in the last year, 65% in the last 5 years, and 89% published in the last 10 years.

Zebrafish model and vaccines testing

Vaccination safety.

When devising immunization experiments, challenge trials for vaccine development evaluate the efficacy and safety of the vaccine against different pathogens. These are normally assessed using animal models, mainly mammals, which are often imprecise in reflecting human diseases [ 93 ], not to mention time consuming, and require a large number of animals. Moreover, the mortality and clinical signs as well as laboratory tests are usually analyzed to evaluate the innate (non-specific) or adaptive (specific) immune system response. As in mammals, Zebrafish has a well-maintained adaptive immune system composed of T and B lymphocytes that develop from the thymus and kidneys respectively. However, in relation to the development of memory lymphocytes, fish seem to have memory cells of the type B and T [ 78 ]. Yet, there has not been enough data to confirm that in Zebrafish. Zebrafish also presents the enzyme system involved in the process of genetic rearrangement that originates the B (BCR) and T (TCR) lymphocyte receptors. As in humans, Zebrafish has recombination activator genes that control the rearrangement of gene segments V, D and J to produce the diversity of antibodies and lymphocyte receptors. In addition, the zebrafish’s immune system has only approximately 300,000 antibody-producing B cells, making it three orders of magnitude smaller than mice and five orders simpler than humans [ 48 ].

The efficiency of the humoral response increases due to the increased affinity of the antibodies. Affinity maturation of antibody responses is less efficient in cold-blooded vertebrates compared to mammals. Despite this, in zebrafish, data revealed that specific nucleotides in regions of the BCR receptor were target of directed mutations. Therefore it was suggested that activation-induced deaminase and affinity maturation contributed to the diversification of antibodies also in fish [ 56 ]. Immunization of teleost fish with the TNP-KLH antigen (linked to trinitrophenyl to keyhole limpet hemocyanin), for example, induced the production of specific low affinity antibodies, which were replaced in 5 weeks by antibodies of intermediate affinity, and after 15 weeks, by antibodies with greater affinity for the antigen [ 28 , 97 ].

Among the immunological tests, the most frequent ones are: complete hematological analysis by counting erythrocytes; thrombocytes and leukocytes; differential white cell count; hematocrit; glucose; organ histology, and immunological essays such as serology, specific antibody titration, and agglutination [ 4 , 29 , 57 ]. Furthermore, toxicity tests can be also conducted using zebrafish such as embryotoxicity, hepatotoxicity, neurotoxicity, endocrine toxicity, genotoxicity, among others as proposed by Bailone et al. [ 3 ].

Up to now, these tests have been conducted using rodents, but in recent decades, the Zebrafish model has proved to be an important tool in the studies of infections and immunological responses. This model has the advantage of having OECD-specific guidelines for safety evaluation of chemical compounds (acute toxicity), which is performed within 96 h [ 65 ]. In addition, observations can be made in real-time allowing for the monitoring of embryogenesis (Fig. 1 ) as well as regarding the effects of vaccines in relation to cardiovascular, hepatic, nervous, and endocrine, not to mention, behavioral aspects too [ 18 , 40 ].

Embryos of zebrafish 0, 6, 24 and 48 h’ post-fertilization. Larvae of zebrafish 72 and 96 h post-fertilization

Prior to vaccines being tested on humans, livestock or pets, these should be assessed using animal models to avoid causing them harm, including death, especially in the case of immunosuppressed organisms, children and the elderly [ 26 ]. As for vaccination in humans, for example, about 0.4 to 1.9 people per million who had been vaccinated with BCG against tuberculosis may have developed the disease through vaccine contagion. For hepatitis B, 1 in 600,000 people vaccinated may have presented a severe allergic reaction (anaphylaxis). In the case of vaccine against poliomyelitis, vaccine contagion happened to 1 in every 3.6 million vaccinated. Moreover, to combat yellow fever, the vaccine contagion and seizures happened to 1 in 22 million and internal hemorrhages happened to 1 in 450,000. Thence, the occurrence of side effects is very rare. Side effect reactions in humans may also be observed to be caused by other vaccines such as yellow fever, measles, mumps, rubella, chicken pox, diphtheria and tetanus. The most common symptoms are seizures, severe allergic reactions, meningitis, encephalitis [ 26 ]. Although these risks are irrelevant when compared to damages that could be caused by the non-use of a vaccine, the toxicology, the side effects and immunization at different concentrations ought to be adequately tested.

Thus, the Zebrafish model has the advantage of a researcher to follow in real-time the fish’s development from its embryogenesis to full organ development which is reached about 36 h after fertilization. This allows for a vaccine’s effect on all the major organs precursors to be closely studied [ 53 ] such as using immunohistology (Fig. 2 ).

Histology of adult zebrafish (hematoxylin eosin). a Male. b Female

Zebrafish and mammalian toxicity (Lethal concentration – LC 50 ) profiles are surprisingly similar for a range of substances specified in Table 1 below. Therefore, toxicity studies support the effectiveness of using the zebrafish model for the purpose of testing these substances. Furthermore, they can be extrapolated to the active ingredients present in the vaccine, and enabling quick parallel studies of vaccine reactions in humans and zebrafish.

Advantages of zebrafish model in vaccination tests

Compared to other vertebrates, zebrafish have extra biological advantages including high fecundity, external fertilization, optical transparency and rapid development. Moreover, Zebrafish possess a highly developed immune system that is remarkably similar to the human one. Therefore, it is expected that the majority of the signaling pathways and molecules involved in the immune response of mammals would also exist and behave similarly in fish [ 89 ]. Consequently, the presence in fish of elements of innate and adaptive immunity enables research in infectious processes, being susceptible to infections by gram-negative and gram-positive bacteria, protozoa, viruses, fungi and mycobacteria.

The development of special cloning, mutagenesis and transgenesis techniques allowed the identification of a significant number of mutants. Commercial mutant zebrafish lines and the recently developed CRISPR/Cas9 genome modification system provide the means to create knockout zebrafish for studying individual genes at a whole organism level [ 66 ]. Non-pigmenting mutants such as Casper zebrafish have also helped improve visibility of internal organs [ 92 ]. In addition it is easy to generate transgenic zebrafish with ‘reporter genes’ to facilitate analysis in live fish [ 87 ]. Because the zebrafish genome is conserved in humans, information obtained from zebrafish studies may lead to translational results in humans [ 38 ].

Examples of mutant animals displaying human-like diseases are numerous such as: sapje, which has the gene homologous to that of Duchenne muscular dystrophy; dracula , related to erythropoietic protoporphyria; van Gogh, model of the DiGeorge syndrome; and gridlock , which induces coarctation of the aorta [ 47 ]. Research in tumor suppressor genes p53 and apc ( adenomatous polyposis coli) is another area of great interest . The importance of the p53 gene in human carcinogenesis is well recognized and recent studies have shown zebrafish as an excellent model for assessing the presence (or not) of gene stability. Lymphoid leukemia, melanoma and hepato-carcinoma have already been described in zebrafish thus confirming that the molecular mechanisms involved are similar to those of humans [ 49 ].

Regarding the administration of vaccines, in view of the different routes of applications presented in animals and humans, the zebrafish model still allows the immunization of embryos, facilitated by its transparency, using glass needles (Figs. 3 and 4 ). Interestingly, the fact that the fish’s adaptive immune system does not reach maturity up to 4 weeks after fertilization allows them to be used without the need for immunosuppression in the embryonic stages [ 32 ] in the case, for example, of tumor xenograft experiments.

a Vitelline Yolk Injection (24 HPF), Magnifying Glass Nikon SMZ745, 50X; B) Vitelline Yolk Injection (24 h.p.f.), Magnifying Glass Nikon SMZ745, 50X

a 24 HPF Zebrafish Embryo Brain Injection, Nikon Microscope; b Brain injection of turbo-red substance into a 24 HPF zebrafish embryo; c Luciferin-labeled 4 T1 tumor cell bioluminescence in 3-month-old animals

In zebrafish larvae, a rapid systemic infection can be initiated by direct microinjection of a bacterial suspension into the bloodstream. Alternatively, a more localized infection may be induced by the injection of microbes into the muscle tail or the hindbrain ventricle [ 6 ]. For high transfer rate, the microbes can be readily injected into the yolk for the first few hours after fertilization. However, it is important to keep in mind that the yolk lacks immune cells, and therefore the bacteria are able to grow freely before invading the larval tissues [ 51 ].

Several transgenic zebrafish lines containing fluorescent markers in different cells of the immune system have been developed to visualize host-microbe interactions in the transparent larvae. For example, recruitment of fluorescent neutrophils to the site of bacterial infection (which can also be labeled with fluorescence) could be easily followed and quantified in real time. Yet, so far, researchers have focused primarily on larval infection patterns [ 51 ].

Fish vaccines

In the prevention of disease outbreaks causing mortalities in aquaculture, similarly to any other animal production system, vaccination is essential. Thus, the use of vaccines for that purpose could be improved based on the results from the studies performed in zebrafish [ 89 ]. The development of vaccines for aquaculture has been an important milestone for guaranteeing a continuous safe and high standard animal health production system. In recent years, zebrafish models have been chosen as the preferred model in the production of fish vaccination experiments against several pathogens that cause losses in aquaculture around the world such as bacteriosis and viruses. One of the most important pathogen studies applied to fishing production is attributed to Guo et al. [ 35 ]. They analyzed the protective efficacy of four iron-related recombinant proteins and their single-walled carbon nanotube encapsulated counterparts against the Aeromonas hydrophila infection in zebrafish. They observed that the immune response was increased after vaccination. Guo et al. [ 34 ] also studied Edwardsiella tarda which is an important intracellular pathogenic bacterium that causes the infectious disease Edwardsiellosis in fish. They proved that live E. tarda vaccine enhanced innate immunity by metabolic modulation in zebrafish.

Vibrio anguillarum , a bacterium that causes vibriosis, was also studied by Ye et al. [ 98 ] who observed the maternal transfer and protection role in zebrafish offspring following vaccination of the brood stock with a live attenuated V. anguillarum vaccine. They proved that the development of immune cells was enhanced and the maternally-derived antibody could protect early embryos and larvae from the attack of specific pathogens via vaccination with a live attenuated vaccine. Furthermore, Liu et al. [ 50 ] analyzed the profiling immune response in zebrafish intestine, skin, spleen and kidney when immersion vaccinated was used with a live attenuated V. anguillarum vaccine. Immersion, or bath vaccination, is a common practice in aquaculture, because of it being convenient as mass vaccination giving sufficient protection. The fish is submerged in water with a sub lethal concentration of the bacteria for a specific time. Liu et al. [ 50 ] observed that antibodies were either produced at antigen-contact tissues or in immune organs. Zhang et al. [ 101 ] studied Th17-like immune response in fish mucosal tissues after administration of live attenuated V. anguillarum via different vaccination routes. When compared to injection vaccination, immersion vaccination elicited intense Th17-like immune responses in the gut tissue of zebrafish. Vibrio vulnificus , that is an aquatic pathogen that can cause primary sepsis and soft tissue infection, was also tested during an experimentation of zebrafish’s reaction to vaccine. It was concluded that CpG oligodeoxynucleotides, a type of essential immunomodulators, protected zebrafish against Vibrio vulnificus induced infection [ 15 ].

Francisella noatunensis is a bacterium that causes granulomatous disease in freshwater and marine fish, and remains an unsolved problem for the aquaculture sector as no efficient vaccines are yet available. Lagos et al. [ 46 ] studied the immunomodulatory properties of Concholepas concholepas hemocyanin against francisellosis in a zebrafish model, proving that his adjuvant was a potential one for aquaculture vaccines. Moreover, Brudal et al. [ 11 ] observed that vaccination with outer membrane vesicles from F. noatunensis reduced the development of francisellosis in a zebrafish model.

Streptococcus sp. has also been studied with the Zebrafish model. Streptococcus parauberis is the major infectious agent of streptococcosis in olive flounder ( Paralichthys olivaceus ). Kim et al. [ 45 ], studying the identification of novel immunogenic proteins against S. parauberis by reverse vaccinology using zebrafish model, identified 41 vaccine candidates against S. parauberis. Furthermore, Streptococcus iniae was studied by Membrebe et al. [ 58 ] testing the protective efficacy of Streptococcus iniae derived enolase against Streptococcal infection in zebrafish model. In that study, enolase protein was evaluated to induce cross-protective immunity against S. iniae and S. parauberis which are major pathogens causing streptococcosis in fish.

Further to the aforementioned examples, many other diseases have been investigated with the Zebrafish model. For example, Rhabdovirus, which is one of the most important diseases in salmonids, is a virus that causes hemorrhagic viral septicemia [ 44 , 64 ]. Listeria monocytogenes [ 19 , 20 ]; Piscirickettsia salmonis which causes salmonid rickettsia sepsis (Tandberg et al. [ 83 ]); and in adjuvant test to improve the efficacy of vaccines [ 44 ], among others [ 82 ].

Animals and human vaccines

The zebrafish model has been used not only in aquaculture, but also in veterinary and human medicine. So far, it has become one of the major model systems used in modern biomedical research [ 51 ]. According to Torraca et al. [ 86 ], zebrafish can be also used as a model for pathogenesis and host defense, modeling many human diseases, such as tuberculosis, Staphylococcus aureus and Shigella infection, among others, as well as model to investigate immune cells, infection and inflammation of different kind of human diseases.

Torraca et al. [ 86 ] posited that zebrafish could also be used as a model for Tuberculosis which is a devastating infectious disease worldwide and with no current prospect of efficient prevention. Tuberculosis is an infectious disease caused by bacilli from the Mycobacterium tuberculosis complex. It is estimated that up to one third of the world’s population is infected with M. tuberculosis and have active tuberculosis, which often develops decades after the primary infection. Annually about two million people perish of tuberculosis and, so far, due to the lack of well-established animal models, such a disease has been difficult to study [ 51 ].

An infection by Mycobacterium marinum in adult zebrafish resembles that of human tuberculosis, as demonstrated by Myllymäki et al. [ 62 ]. Those authors proved that the M. marinum infection model in adult zebrafish was suitable for preclinical screening of tuberculosis immune’s responses and vaccines. It was also a promising new model for tuberculosis vaccine research, including the pre-clinical identification of vaccine antigens [ 16 , 17 , 36 , 41 , 61 , 67 ];). Other species of Mycobacterium have also been studied, such as M. bovis [ 52 , 73 ] and M. abscessos [ 7 ]. M. bovis is most common in cattle, but also affects humans. M. bovis Bacillus Calmette-Guérin vaccine is currently available as a prophylactic tool for preventing the disease. It has been shown to be efficient in preventing disseminated forms of tuberculosis in children; however, its efficiency is limited in areas where individuals have had prior exposure to environmental mycobacteria, and its efficacy decreased with a host’s age [ 55 ].

Moreover, teleost models offer an expanding platform for the understanding of mycobacterial infections and those mechanisms that offer the greatest potential to enhance host protection [ 37 ]. The models make it possible to screen the host and bacterial factors that modify the disease and facilitate the search for new therapeutic agents. It has recently been shown that zebrafish can also be used for the potential screening of DNA-based vaccines and, in particular, for identifying novel antigens protecting against mycobacteria [ 67 ]. Therefore, using the Zebrafish model is expected to accelerate the understanding of the pathogenesis of tuberculosis which would lead to the development of better vaccines. Yet, the usefulness of this model is not limited to tuberculosis, which as seen before it could benefit research for many other important infectious diseases [ 51 ].

Similarly, this model also helps to elucidate bacterial infections in animals and humans by Aeromonas hydrophila [ 91 ], Pseudomonas aeruginosa [ 74 ], Escherichia coli nonpathogenic [ 63 ], E. coli CFT073 [ 95 ], Listeria monocytogenes [ 80 , 81 ], Myroides odoratimimus [ 72 ], Cronobacter turicensis [ 25 ], Streptococcus agalactiae [ 70 , 96 ], Streptococcus iniae and Streptococcus pyogenes [ 59 , 76 , 77 ], among others [ 12 , 85 ].

Shigella is a major cause of dysentery worldwide, accounting for up to 165 million cases of shigellosis each year [ 23 ]. Yet, despite there not existing vaccine available as yet, the human and animal challenge–rechallenge trials with virulent Shigella as well as observational studies in Shigella-endemic areas are promising. The incidence of the disease decreased following Shigella’s infection which pointsto a biological feasibility of a vaccine [ 54 ]. Phalipon et al. [ 71 ] as well as Mani et al. [ 54 ] proposed that adult zebrafish could be used to study the immune response to Shigella, which is crucial to understanding the crosstalk between Shigella and T-lymphocytes [ 75 ] thus this being relevant in the development of vaccine strategies. Studies have also been conducted with Zebrafish model to promote a vaccine against Salmonella, which produces gastroenteritis that causes massive morbidity and mortality in adults and children in developing countries. Howlader et al. [ 39 ] proved that zebrafish was an excellent model for the study of vaccines using successive immersion triple vaccines with the single serotype Salmonella. Typhimurium and Salmonella entereditis induced protective efficacy against a high dose (10 8 CFU/ml) of infection by these pathogens.

Other microorganisms of importance such as fungi which can cause pathologies in humans, such as Candida albicans [ 10 ], Cryptococcus neoformans [ 8 , 84 ] and Mucor circinelloides [ 90 ] have also been the subject of study with teleosts. In addition, viruses such as Herpes simplex [ 13 , 31 ]; human norovirus [ 88 ]; Vesicular stomatitis [ 33 ]; hepatite C [ 21 , 22 ]; Chikungunya [ 1 , 9 , 14 , 68 ]; Sindib [ 69 ] and Influenza A [ 30 ] are some of the human viruses already studied by the zebrafish model in both embryos and larvae.

Conclusions

The use of the Zebrafish model for the production of vaccines with application for both animals and humans, despite already being a reality, is still underused. This model is an important tool for the development of new safe vaccines against diseases which do not yet have preventive treatment, or for which the existing vaccines are not so effective. Thus, previous screening tests with zebrafish have been proven to be effective in preliminary phases prior to testing with mammalians. Despite the evidence from the literature indicating that science in this field is in its infancy, when compared to other animal models used in research, teleost models have proved to be effective in the elucidation of the infection and immunological responses to the diverse animal and human pathogens. In addition, the reduced financial cost and time frame needed for testing are another attractive regarding the use of zebrafish. Thus, it is expected its use would expand in the coming years.

Availability of data and materials

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Bailone, R.L., Fukushima, H.C.S., Ventura Fernandes, B. et al. Zebrafish as an alternative animal model in human and animal vaccination research. Lab Anim Res 36 , 13 (2020). https://doi.org/10.1186/s42826-020-00042-4

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Nonhuman Primate Models and Understanding the Pathogenesis of HIV Infection and AIDS.

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Animal Models for Brain Research

First Online: 30 September 2020

Cite this chapter

Debby Van Dam 6 , 7 &
Peter Paul De Deyn 6 , 7 , 8

1167 Accesses

Animal models are important experimental tools in neuroscience research since they allow appraisal of selected and specific brain pathogenesis-related questions—often not easily accessible in human patients—in a temporal and spatial pattern. Translational research based on valid animal models may aid in alleviating some of the unmet needs in the current pharmaceutical market. Of primary concern to a neuroscience researcher is the selection of the most relevant animal model to achieve pursued research goals. Researchers are challenged to develop models that recapitulate the disorder in question, but are quite often confronted with the choice between models that reproduce cardinal pathological features of the disorders caused by mechanisms that may not necessarily occur in the patients versus models that are based on known aetiological mechanisms that may not reproduce all clinical features. Besides offering some general concepts concerning the relevance, validity and generalisation of animal models for brain disorders, this chapter focuses in detail on animal models of brain disease, in particular schizophrenia models as examples of animal models of psychiatric disorders and Alzheimer’s disease models as examples of animal models of neurological/neurodegenerative disorders.

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The Trouble with Animal Models in Brain Research

Animal models of neurodegenerative diseases

Abbreviations.

Acetylcholinesterase

Alzheimer’s disease

Amyloid precursor protein

Behavioural and psychological signs and symptoms of dementia

Choline O-acetyltransferase

Disrupted in schizophrenia-1 gene

Diagnostic and Statistical Manual of Mental Disorders

Dysbindin gene

Neuregulin 1 receptor gene

Flinders resistant line

Flinders sensitive line

High-anxiety-related behaviour Wistar rat line

Low-anxiety-related behaviour Wistar rat line

Latent inhibition

Neurofibrillary tangle

Nerve growth factor

N-methyl- d -aspartate

Neuropsychiatric symptoms

Neuregulin 1 gene

Phencyclidine

Platelet-derived growth factor promoter-driven APP

Prepulse inhibition

Research Domain Criteria

Reelin gene

Senescence-accelerated mouse

SAM-prone substrain

Single nucleotide polymorphism

TAR DNA-binding protein 43

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Acknowledgements

This work was supported by the Research Foundation Flanders (FWO), the Belgian Alzheimer Research Foundation—Stichting Alzheimer Onderzoek (SAO-FRA grants #2017/0025 and #2018/0027), the EU Joint Programme—Neurodegenerative Disease Research (JPND) project HEROES (ZonMW project 733051072), agreement between the Institute Born-Bunge and the University of Antwerp, the Medical Research Foundation Antwerp, the Thomas Riellaerts research fund and Neurosearch Antwerp.

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Van Dam, D., De Deyn, P.P. (2021). Animal Models for Brain Research. In: Dierckx, R.A., Otte, A., de Vries, E.F., van Waarde, A., Lammertsma, A.A. (eds) PET and SPECT of Neurobiological Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-53176-8_1

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August 27, 2024

Queen’s Brian May Is a Champion for Badgers and Science

Queen guitarist Brian May has spent a decade studying the science of bovine tuberculosis, which can be carried by badgers, and has identified a new method of spread

By Elizabeth Gibney & Nature magazine

Brian May: “It kind of irks me that we don’t have a scientific paper out there.”

Miikka Skaffari/Getty Images

Brian May has many strings to his guitar. The musician, who is still touring with his rock band Queen, is also an astrophysicist, specializing in 3D stereoscopic images of distant bodies. And to the UK public, he’s also a passionate campaigner for animal rights.

After abandoning his PhD at Imperial College London in 1974 to follow his musical passions, May finally returned to complete his doctorate in 2007. Soon after, the rock star embroiled himself in a polarizing scientific row over whether the European badger ( Meles meles ) was causing mass infection of cattle with bovine tuberculosis (TB). Each year, the problem costs the UK government more than £100 million (US$130 million) and leads to the slaughter of more than 20,000 cows.

Some scientists initially backed the government’s policy of culling badgers — 230,000 have been killed since 2013 — although many now doubt the approach’s effectiveness. The past government had planned to phase out culling in favour of vaccination, but 20 culling licences were issued this year. The new Labour government has said that it plans to end culling, but these licences will continue.

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In a BBC documentary airing in the United Kingdom on 23 August, May describes his decade-long research project to understand what is behind bovine TB. Alongside him on the programme is Anne Brummer, chief executive of their co-founded wildlife charity, the Save Me Trust. “He lives and sleeps this,” says Brummer. “He hates injustice and is a very passionate, compassionate person. He just wants to solve these problems.”

May spoke to Nature about how his scientific skills have been essential to his work, and the “monstrous” findings made by his team, which includes Brummer, farmer Robert Reid and veterinary surgeon Dick Sibley.

Brian May in stable with two farmers and cows.

The Save Me Trust chief executive Anne Brummer ( left ) and Brian May ( second from left ) meeting two farmers in the BBC documentary Brian May: The Badgers, the Farmers and Me.

Athena Films/BBC

What has the influence of your scientific background been on your work with animals? Do you think it gave you more confidence?

Absolutely. The scientific method is something precious, and you do learn it — the hard way — if you’re doing a PhD. Everything comes down to asking the right questions and keeping an open mind, and resisting the terrible inclination that scientists have, because they’re human beings, of finding what you expect to find. We’ve all been told that badgers are how the pathogen is spread, so we look for that pattern. And, sadly, I think that’s why the myth has perpetuated.

Were you convinced from the beginning that badgers were not causing the spread of TB in cattle?

I was always suspicious, but I didn’t have anything to justify my position. But I felt that even if they were responsible, it wasn’t their fault. I remember being at a Zoological Society meeting around 13 years ago, where I had the temerity to stand up and say, “Doesn’t anybody think this is morally wrong?”, and I felt like a child, because everybody looked at me with such scorn. I realized that the only way to get anywhere was to stop shouting, start listening and get into the science. Along the way I think we’ve made breakthroughs that I didn’t even dream of making.

You’ve spent the past 12 years as part of a research team on the Gatcombe farm in Devon, near the south coast of England, studying TB transmission. What did you find?

We developed a view on how the mycobacterium responsible for TB transmits from one animal to another. TB has classically been known as a respiratory disease, but our discovery is that a cow doesn’t contract TB by breathing in something, it contracts it by eating the pathogen from defecation from a neighbouring cow. It’s a monstrous discovery, because once you start understanding your enemy, then you can start to defeat it. Now we know that the thing is passed from cow to cow, because of poor hygiene.

How does testing contribute to the problem?

We also found that the [government-sanctioned] skin test for TB is as little as 50% accurate. That’s a terrible thing to discover, because you might as well toss a coin. We discovered that one cow had been through the skin test 30 times and pronounced healthy, and when it went for a postmortem, it was riddled with TB. So, the skin test is the villain of the piece, and the fact that farmers are relying on this incredibly inaccurate test to remove cows from their precious herds and take them off to slaughter is a scandal.

Do you have any plans to put your findings into a scientific paper?

Absolutely, yes. That’s definitely one of our next steps. It kind of irks me that we don’t have a scientific paper out there, but all in good time.

What makes you so convinced that badgers play no part in transmission?

On Robert Reid’s farm we, for some time, had a healthy herd with an infected population of badgers around it. And all through this period, almost 10 years, there’s never been a single infection from the cows that could graze in the fields, near where the badgers live. All have been in the sheds.

There is also a farmer in Tiverton who built an amazing fence five miles long around his beef herd, to keep the wildlife out. Eventually, he lost half his herd. How did that happen? It’s highly likely that a new bull — shown as healthy by the skin test — is the way this herd became destroyed. It’s likely that this is a pattern we’ve seen in many other places, as well. I would like farmers to see the documentary and think, OK, maybe we’re ready for a change. We need to change so many methods in cattle farming to solve this problem.

In the film, you say that speaking out against badger-culling has become as important to you as your music. Where does astrophysics fit in?

It’s right up there. I’m still doing astrophysics. I’m privileged to be part of a few teams of exploration in NASA, the European Space Agency and the Japanese Aerospace Exploration Agency. I have a great time doing that. What I contribute is stereoscopy and it’s been a lot of fun, because it gives you very human insights into the exploration of these wonderful places they’re visiting.

What does the bovine TB affair teach us about science and policymaking?

All I’d like to say is that it worries me that the peer-review process can embody flaws. If you get the people to peer review who are in the same clique, you’re not going to peruse the material thoroughly enough.

This article is reproduced with permission and was first published on August 22, 2024 .

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v.53(3); 2021 Mar

Zebrafish as an animal model for biomedical research

Tae-young choi.

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

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

Tae-Ik Choi

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

Seong-Kyu Choe

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

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

Cheol-Hee Kim

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.

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. (Fig.1). 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. (Fig.1a 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. (Fig.1b 1b ) 28 . In addition, recently, tissue-clearing technology has allowed visualization of neural networks in the whole brain of adult zebrafish (Fig. (Fig.1c). 1c ). These molecular tools and technologies are useful for investigating phenotypic changes in zebrafish disease models of human developmental disorders.

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Object name is 12276_2021_571_Fig1_HTML.jpg

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. Fig.2a 2a .

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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. (Fig.2b) 2b ) and with anti-cytokeratin 18 antibody (Fig. (Fig.3), 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.

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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: rk.ca.ukw@67ytiohc .

Seong-Kyu Choe, Email: rk.ca.ukw@246uykgnoes .

Cheol-Hee Kim, Email: rk.ca.unc@mikarbez .

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Published: 27 August 2024

Host specificity and cophylogeny in the “animal-gut bacteria-phage” tripartite system

Ye Feng 1 , 2 ,
Ruike Wei 3 ,
Qiuli Chen 2 ,
Tongyao Shang 2 ,
Nihong Zhou 3 ,
Zeyu Wang 2 ,
Yanping Chen 4 ,
Gongwen Chen 3 ,
Guozhi Zhang 3 ,
Kun Dong 5 ,
Yihai Zhong 6 ,
Hongxia Zhao 7 ,
Fuliang Hu 3 &
Huoqing Zheng ORCID: orcid.org/0000-0001-8499-0694 3

npj Biofilms and Microbiomes volume 10 , Article number: 72 ( 2024 ) Cite this article

Metrics details

Metagenomics
Molecular evolution

Cophylogeny has been identified between gut bacteria and their animal host and is highly relevant to host health, but little research has extended to gut bacteriophages. Here we use bee model to investigate host specificity and cophylogeny in the “animal-gut bacteria-phage” tripartite system. Through metagenomic sequencing upon different bee species, the gut phageome revealed a more variable composition than the gut bacteriome. Nevertheless, the bacteriome and the phageome showed a significant association of their dissimilarity matrices, indicating a reciprocal interaction between the two kinds of communities. Most of the gut phages were host generalist at the viral cluster level but host specialist at the viral OTU level. While the dominant gut bacteria Gilliamella and Snodgrassella exhibited matched phylogeny with bee hosts, most of their phages showed a diminished level of cophylogeny. The evolutionary rates of the bee, the gut bacteria and the gut phages showed a remarkably increasing trend, including synonymous and non-synonymous substitution and gene content variation. For all of the three codiversified tripartite members, however, their genes under positive selection and genes involving gain/loss during evolution simultaneously enriched the functions into metabolism of nutrients, therefore highlighting the tripartite coevolution that results in an enhanced ecological fitness for the whole holobiont.

Eco-evolutionary dynamics of gut phageome in wild gibbons ( Hoolock tianxing ) with seasonal diet variations

Community context matters for bacteria-phage ecology and evolution

The enemy from within: a prophage of Roseburia intestinalis systematically turns lytic in the mouse gut, driving bacterial adaptation by CRISPR spacer acquisition

Introduction.

Animal gut harbors a myriad of gut bacteria that play important roles in modulating the immune functions during development, synthesizing vital chemical compounds like hormones, and aiding in the process of digestion while competitively excluding pathogens 1 . In return, gut bacteria depend on their host to maintain a stable ecosystem and access nutrients. The prevalence of these intimate interactions is often cited as evidence supporting the notion that animals and their gut bacteria have mutually evolved into a symbiosis relationship 2 . Typical symbiotic relationships frequently exhibit a fascinating trend of congruent phylogenies 3 . However, because gut bacteria may undergo frequent horizontal transmission between individuals that may disrupt the cophylogenetic signal, strong host-bacteria fidelity over evolutionary timescales was only seen in a few bacterial taxa, e.g., less than 1/3 of gut bacteria in humans 4 , 5 . Given that many of these bacteria are tightly and specifically linked to host development and immune functions, identification of bacteria that have coevolved with their hosts carries essential implications for understanding how gut bacteria impact host physiology and how host selection influences the composition of the gut microbiome.

In comparison to the bacterial components of the gut microbiome, the viral fraction, known as phageome, is characterized by a greater genetic diversity and a more labile composition between individuals 6 . Although a majority of gut bacteriophages remain uncultured and unclassified 7 , dysbiotic gut phageomes have been linked to diseases such as colitis and type II diabetes 8 , 9 . This underscores the potential impact of phageome on the overall health of their superhost, a term referring to the host of bacteria which in turn host the phages. The impact of phageome is primarily believed to occur through the capacity of phages to shape the ecology of their bacterial hosts via top-down pressure. However, it has also been observed that phages can directly modulate the superhost’s metabolism or immune system 10 .

Given that coevolution could occur between animals and their gut bacteria, as well as between gut bacteria and their phages 4 , 11 , these two pairs can theoretically combine to create a coevolving “animal-gut bacteria-phage” system. However, whether and how this tripartite interplay adapts to the external environment and influence the animal’s development, health and even evolution remain largely unclear. When focusing on a narrower point, it is generally believed that host tropism of bacteriophage is constrained by phylogenetic barriers, typically limiting one viral cluster (VC) to infecting one bacterial species 6 . Some gut bacteria with particularly close link to the host’s functions may also exhibit specific host range 5 . Within this context, the question arises as to whether host specificity and cophylogeny are likely to manifest across the entire tripartite system. Notably, Gogarten et al. found that 22.1% of the gut phages showed cophylogeny with their primate superhost, in which, however, the intermediate bacteria were not included for analysis 12 . The answers to the aforementioned questions undoubtedly hold important ecological and evolutionary implications. Moreover, they also have practical relevance in clinical settings, such as in fecal microbiota transplantation and phage therapy against multi-drug resistance bacteria.

In this study, we utilize a bee model to investigate the host specificity and cophylogeny of the “animal-gut bac-phage” tripartite system. Eusocial corbiculate bees include honeybees ( Apis spp.) and bumble bees ( Bombus spp.), and the Apis genus was further split into the Eastern honeybee A. cerana and the Western honeybee A. mellifera due to geographic isolation 13 . These bee species share nearly the same set of gut microbiota that comprise approximately ten phylotypes only 14 , and most of these phylotypes have further diverged into multiple “sequence-discrete populations” (SDPs). Both phylotype and SDP are phylogenetic units that separate an organism from the rest of the community by genetic and genealogical discontinuity. While phylotype is often defined a threshold identity (e.g., ≥97%) of 16 s rRNA genes and therefore approximates genus- or species-level taxon, SDP represents a finer subdivision of phylotype and is characterized by high (e.g., ≥95%) genome-wide average nucleotide identity (gANI) 15 . The simple and conserved composition of the gut microbiota in bees makes it easy to obtain the high-quality contigs for the gut bacteria though metagenomic sequencing. Furthermore, due to the limited number of the bacterial phylotypes, it is simpler to make host prediction for the gut phages and, subsequently, to examine the interaction between phages and bacteria. Last but not least, the divergence of the bacterial phylotypes, such as Gilliamella and Snodgrassella , into SDPs have been known to mirror the evolutionary history of their respective bee hosts 16 , 17 , 18 . During the coevolution, the bacteria of different SDPs have developed elaborate mechanisms to adapt to their specific bee hosts, including the recognition of neuro signals and the formation of biofilm 19 , 20 . These features make bee a particularly convenient animal model for further testing the downstream cophylogeny between gut bacteriome and phageome.

Currently, a few studies have characterized the gut phageome in A. mellifera , which identified a large repertoire of novel phages with remarkable genetic diversity 21 , 22 , 23 . However, knowledge regarding the phageome of A. cerana and Bombus remains unknown. Through comprehensive sequencing of the paired gut bacteriome and phageome of A. cerana , A. mellifera and B. terrestris , we have not only gauged the diversity of the phageome and bacteriome but also assessed the quantitative relationship between them. By conducting comparative genomic and phylogenetic analyses, we assessed the chained cophylogeny in the “animal-gut bacteria-phage” system, and compared the evolutionary rates between the tripartite members. Collectively, this study provides an unprecedented groundwork for future investigations into the ecological, evolutionary and functional host-microbe interactions.

Conserved and codiversified bacteria in the bee gut

We collected A. cerana and A. mellifera from Zhejiang, Yunnan, and Hainan provinces in China, respectively (Fig. 1a ). At each region, A. cerana and A. mellifera colonies were kept in the same apiary, with two colonies being collected for each species. Meanwhile, commercialized B. terrestris was reared in the same apiaries for a duration of two months. By sequencing the genomes of these bee samples, a total of 5053 single-copy orthologous genes were conserved among all samples across the three bee species. A phylogenetic tree based on concatenated core coding sequences was built, in which the bee samples were firstly clustered by species and further by regions (Fig. 1b ). The branch length of the tree was roughly proportional with the divergence time inferred from the fossils that the genus Apis and Bombus split approximately 82 million years ago and the species A. cerana and A. mellifera split approximately 11.8 million years ago (Fig. 1b ) 24 , 25 .

a Locations where bees are sampled. b Maximum likelihood phylogenetic tree comprising the three bee species based on concatenated coding nucleotide sequence (left tree) and the evolutionary timescale inferred by the TimeTree database (right tree). For the left tree, the branches marked by slashes are shortened 10-fold for an enlarged view of the terminal branches. c Taxonomic and functional profile of bee gut bacteriome. The taxonomic profile (left plot) shows the relative abundances of phylotypes; the functional profile (right plot) shows the sequencing depth of genes that are assigned to each category. Only the functions that show significant difference between the bee species are listed. d PCoA plot based on the pairwise Bray–Curtis dissimilarity between samples. The shape and color of the dots that represents individual samples in ( c , d ) are the same as ( a , b ).

We conducted shotgun sequencing of the gut bacteriome, and confirmed that all individual gut communities were dominated by ten core bacterial phylotypes (Fig. 1c ). The principal co-ordinates analysis (PCoA) showed that the composition was distinguishable between bee species (Anosim test, r = 0.319, p = 0.001; Fig. 1d ) but not between geographical regions (Anosim test, r = 0.095, p = 0.166). The functional profile inferred from the entire gut bacteriome showed significantly different abundance in a few categories between bee species, including carbon degradation, oxygen and sulfur metabolism (Kruskal–Wallis test, p < 0.05; Fig. 1c ).

Since Gilliamella and Snodgrassella were the only two phylotypes that accounted for >1% of abundance among all samples, they were chosen for further profiling at SDP level. As revealed in the core gene-based phylogenetic trees for both Gilliamella and Snodgrassella , the metagenome-assembled genomes (MAGs) derived from this study and the isolate genomes downloaded from Genbank database were organized into three distinct and robust major SDPs, which aligned with the respective bee species rather than the geographical origin (Fig. 2a ; Supplementary Fig. 1 ). The strong cophylogenetic signal demonstrated that coevolution existed between the bee host and the two gut bacteria (Parafit test, p = 0.006 for Gilliamella & p = 0.002 for Snodgrassella ).

a Core genome-based maximum-likelihood phylogeny for Gilliamella and Snodgrassella . Gil-Ac means A. cerana -specific Gilliamella SDP (i.e., the Gilliamella SDP that is native to A. cerana ); Am and Bt mean A. mellifera and B. terrestris , respectively; Sno means Snodgrassella . b Principal component analysis (PCA) of gene content of Gilliamella and Snodgrassella SDPs. The gene content is inferred from the MAGs of the native SDP for each sample. c Gut community composition across samples at the SDP level. The shape and color of individual samples in ( a , b , c ) follow the scheme in the bottom box.

The genetic divergence between the SDPs in both Gilliamella and Snodgrassella was remarkable. The average amino acid distance for the conserved genes between SDPs was 12–18%; and the gANI was 78–81% (Supplementary Fig. 2 ). Even within the 16S gene sequences, there were substantial differences between the SDPs, with the maximal distance of up to 3.3% for Gilliamella and 1.8% for Snodgrassella (Supplementary Fig. 2 ). Gene content of the bacterial SDPs were clustered by bee species instead of by region (Fig. 2b ). The core-pan curves showed that both Gilliamella and Snodgrassella possessed a closed pangenome (Supplementary Fig. 2 ). The ratio of pan- and core-genome size for Gilliamella (5.95) was much higher than that for Snodgrassella (3.75), indicating a much more expanded auxiliary gene repertoire for Gilliamella . To evaluate the extent of recombination within bacterial genomes, we not only examined the individual gene-based trees for their topology discordance with the concatenated gene tree, but also detected recombination events within conserved chromosomal regions (Supplementary Fig. 3 ). Both approaches showed a higher frequency of recombination in bacteria compared to the bees, and yet the majority of recombination events in bacteria occurred within SDPs rather than between SDPs (Supplementary Fig. 3 ).

The number of mapped reads showed the dominance of host-specific SDPs in each sample, which indicated that Gilliamella and Snodgrassella were transmitted in bees mainly in a vertical way (Fig. 2c ). However, each host was simultaneously colonized by non-native SDPs, albeit in smaller proportions. Notably, nearly one quarter of both Gilliamella and Snodgrassella in a B. terrestris sample raised in Hainan were assigned to the A. cerana -specific SDP (Fig. 2c ). Furthermore, the non-native SDPs from Hainan were not genetically similar to the A. cerana -specific SDPs from Hainan samples but to that from other regions (Supplementary Fig. 4 ). This indicated dispersal of the same SDP across broad regions, which explained why the phylogenetic tree of bacterial SDPs was not clustered by region.

Host specificity and cophylogeny of bacteriophages in the bee gut

We enriched the virus-like particles (VLPs) for each bee gut sample and subsequently sequenced the virome. According to the quality control analysis with the ViromeQC tool, 16 out of 18 samples showed a VLP enrichment score higher than 5, 13 of which had the score even higher than 10 (Supplementary Table 1 ), suggesting that most of these samples did not suffer severe bacterial or eukaryotic contamination. By clustering with vConTACT software, the derived phage sequences together with the prophage sequences predicted from the bacterial isolate genome and the MAGs were assigned to 794 VCs. Only 32 VCs (4.41%) were taxonomically annotated, with the others not overlapping with the RefSeq phages, suggesting the gut phages may have a restricted ecological niche primarily within bee gut.

Few VCs were conserved among all samples or even among all samples from a particular bee species, indicating the absence of core phageome in the bee gut. PCoA based on VC abundance can moderately cluster samples by bee species (Anosim test, r = 0.549, p = 0.001; Fig. 3a ). Compared to the bacteriome, the phageome showed a much more labile composition as revealed by the Shannon index (Fig. 3b ). The Shannon index of bacteriome and phageome were not correlated with each other (Pearson correlation, p = 0.247; Fig. 3c ). According to the Bray-Curtis dissimilarity, nevertheless, the phageome composition was significantly correlated to the bacteriome composition at the phylotype level (Mantel test, r = 0.331, p = 0.001; Supplementary Fig. 5 ), indicating a reciprocal interaction between the two kinds of communities.

a PCoA plot based on the Bray-Curtis dissimilarity of phageome between samples. b Compositional evenness of phageome and bacteriome as measured by Shannon index for each sample. c Pearson correlation of Shannon index between phageome and bacteriome. d The vConTACT-based network diagram, in which each node represents a (pro)phage sequence from either VLP metagenome or bacterial genomes. Contigs that have significantly similar sequences are connected to each other. e Relative abundance of the bacteria (the percentage of the bacteria in the entire bacteriome) and their phages (the percentage of the phages that take the bacteria as the host in the entire phageome). The solid line and the gray area represent the line fit and the 95% confidence interval. The shape and color of individual samples in all panels follow the scheme in the bottom middle box.

In our efforts to predict the host of these phages, we identified 95 VCs and 27 VCs originating from Gilliamella and Snodgrassella , respectively. Notably, most of VCs from Snodgrassella were more or less related to each other in gene content (Fig. 3d ), suggesting they belonged to a few viral families only. Visualization of vConTACT analysis did not distinguish VCs from different bee species or from different geographical regions (Fig. 3d ). Compared to Gilliamella phages, Snodgrassella phages had an extremely low abundance (<1%) in half of the samples; the four samples with abundance > 10% all happened to be from Zhejiang region (Fig. 3e ). Similar to that at the phylotype level, for both Gilliamella and Snodgrassella , the composition of their phages was also moderately correlated with the bacterial composition at the SDP level based on the Bray-Curtis dissimilarity (Mantel test, p < 0.05; Supplementary Fig. 5 ). This suggested that the interaction between the phages and their bacterial host was also present at SDP level. For Gilliamella , in particular, a linear relationship of relative abundance was observed between the phages and the bacterium, with the slope of the linear regression being significantly <1 (Fig. 3e ). Thus, Gilliamella phages did not proliferate as rapidly as their bacterial host did. The same trend was not observed in Snodgrassella .

Moving forward, we checked host specificity of the gut phages. Although 470 (64.8%) VCs showed their members originated from a single bee species, there was a clear trend that VCs with a greater number of members were more likely to be found in multiple hosts (Fig. 4a ). Among these, 63 VCs appeared in all of the three bee species, which belong to host generalist. According to the Parafit test, majority of these VCs codiversified with their bacterial host; but these significant cophylogenetic signals obviously stemmed from the clustering of large number of (pro)phages from the same bee species, even though the phages from other bee species also appeared in the same clade (Supplementary Fig. 6 ). Only two VCs from Gilliamella and one VC from Snodgrassella had their phylogenetic tree perfectly separated into three clades according to their bee and bacterial hosts (Fig. 4b ; Supplementary Fig. 6 ). When we assigned the phages into a finer taxonomic level known as vOTU, which took 90% gANI as threshold, >80% of vOTUs had a single bee superhost, regardless of the number of members the vOTUs possessed (Fig. 4c ). While this finding indicated host specificity at the vOTU level, it also explained the aforementioned significant cophylogenetic signals for the gut phages.

a Histogram showing the number of VCs (left y-axis) derived from a single bee species or from multiple bee species at different VC size (x-axis). The VC size refer to the total number of phage and prophage members contained in the VC. The gray line using the right y-axis shows the percentage of VCs derived from a single bee species. b Phylogeny of the three VCs that shows perfect cophylogeny with the bee and bacterial hosts. VC59 and VC413 are from Gilliamella , and VC413 is from Snodgrassella . The neighbor-joining tree is based on dRep distance, with the x -axis representing gANI. c Histogram showing the number of vOTUs (left y -axis) derived from a single bee species or from multiple bee species. d The genetic distance (by dRep software) for phages within the same bee species and for phages between different bee species. The box-and-whisker plots within the violin plots indicate the median and the quartiles. * p < 0.05 by two-sided Student’s t -test. e The relationship between the genetic distance and the variation of gene content for the gut phages. The solid line and the gray area represent the line fit and the 95% confidence interval.

For phage members of the same vOTU, those originating from the same bee species showed slightly lower genetic distance than that from different bee species (Fig. 4d ). This observation indicated that segregation of superhost blocked the transmission of phage across bee species to a minor extent. The impact of superhost segregation on the gene content variation of phages was also small. Considering the trend that less gene content was shared between phage members with decreasing genetic similarity, the slope of the linear fit was slightly steeper for phages within the same bee species in comparison to those between different bee species (Fig. 4e ).

Genetic disparity and functional collaboration in the tripartite system

The cophylogeny observed among bee, the gut bacteria Gilliamella and Snodgrassella , as well as their respective (pro)phages VC59, VC413 and VC44 strongly suggested a codiversification scenario. The ancestral sequences and gene content state of the three parties were reconstructed at three stages during the bee’s evolution, i.e., genus, species and isolate. At the isolate stage, it became apparent that the synonymous substitution rate (Ks) of Gilliamella , Snodgrassella and the three VCs was higher than that of bee by one or two orders of magnitude (Fig. 5 ). In other word, the bacteria and phages had diverged to the species or even genus level at this timepoint. Given that Ks is almost free from selection pressure, the different Ks values between the three parties may reflect their different generation time. At the genus stage, however, the Ks values for the three parties were somewhat similar, indicating that Ks had approached saturation.

For bee, comparison between ‘Genus’ refers to comparison between the ancestor of Apis and the ancestor of B. terrestris ; comparison between ‘Species’ refers to comparison between the ancestor of A. cerana and the ancestor of A. mellifera . The three dots for the ‘Isolate’ stages represent comparison between A. cerana samples, between A. mellifera samples, and between B. terrestris samples, respectively. The three stages for the gut bacteria and phages in this figure also refer to the bee’s stages instead of their own. For the three phage VCs, the genetic differences at the ‘Genus’ and ‘Species’ stage are too huge to be precisely anticipated and plotted. The values of synonymous substitution rate (Ks), nonsynonymous substitution rate (Ka), and Ka/Ks in this figure are not for individual genes but the average for all conserved orthologous genes. Gene content difference refers to the number of unshared genes divided to the number of total orthologous genes involved in the comparison.

The non-synonymous substitution rate (Ka) increased in similar pace with Ks (Fig. 5 ). Ka/Ks is a common indicator of selective pressures on coding genes. At the isolate stage, Ka/Ks for bee floated around 0.35, but fell down to nearly 0.1 at the species and genus stage. This suggested that bee underwent the purification of missense mutations during isolate divergence and finished this process at the species stage. In contrast, Ka/Ks for gut bacteria differed relatively little at the three stages, indicating their purification process has been rapidly completed at the bee’s isolate stage. In terms of gene content, bee varied with less than 15% between the compared genera, whereas bacteria began to exceed 15% at the bee’s isolate stage. Phages exhibited even higher Ka, Ka/Ks and gene content difference than their bacterial host, which might be explained by the weakness of their DNA repair system, a lack of functional constraint and/or the influence of positive selection pressure. It is noteworthy, however, that the displayed evolutionary pattern of phages came from only three VCs and may not well represent the entire gut phageomes of bees or other animals.

The functional variation at the three evolutionary stages was evaluated by taking two categories of genes for enrichment analysis: genes under positive selection and genes involving gain or loss during evolution. The former genes generated more enriched functions than the latter, indicating that positive selection may represent a more common approach for elevating fitness for the whole holobiont. Many of the enriched functions were related with degradation and biosynthesis of fatty acid, amino acid, and vitamin, several of which were shared by bee and Gilliamella (Fig. 6 ).

The functional enrichment analysis is performed on genes generated on three dimensions: gene categories, including the genes under positive selection and the genes involving gain or loss between clades; organisms, including bee, Gilliamella and Snodgrassella ; stages, including comparison between genus, between species, and between isolates. The functional annotation refers to KEGG. The fold change refers GeneRatio divided by BgRatio, which is calculated by the ClusterProfiler package.

Since only three VCs had their members perfectly clustered according to the bee source, their small number of genes did not permit enrichment analysis. Instead, we opted to analyze the enriched functions of all phage genes while considering the genome of bacterial host as background. In the case of Gilliamella , its (pro)phages enriched the coding potential to influence the bacteria in metabolism of amino acid, the main ingredient of pollen (Supplementary Fig. 7 ). This implies that the (pro)phages may also aid in food digestion and nutrient utilization. On the other hand, much fewer biological processes were enriched from the VCs of Snodgrassella , possibly due to their much smaller gene repertoire.

In this study, we employed bee model to investigate the cophylogeny of “animal-gut bacteria-phage” tripartite system. As consistent with previous studies 20 , our study revealed that the gut bacterial community had a highly stable phylotype composition across bee species and even genus whereas the phylotypes further split into SDPs that aligned with the bee hosts. Taking Gilliamella for example, the 16S rRNA sequence similarity was less than 97% between SDPs, and the gANI values were even below 83%. These values exceeded the common cut-off for species in modern bacterial taxonomy and are even comparable to inter-genus distance, e.g., between Escherichia coli and Salmonella enterica 26 . The substantial genetic divergence between the SDPs explains why recombination occur exclusively within SDPs instead of between SDPs. Consequently, the coexistence of SDPs within an individual bee or colony, which results possibly from the secondary host sympatry of A. cerana and A. mellifera , would not reverse the process of host-bacteria codiversification.

Cophylogeny can be driven by a variety of ecological and/or evolutionary processes. From the view of functional adaptation, two prerequisites are required for formation or subsequent maintenance of cophylogeny: (1) strong functional dependencies between symbiotic parties and (2) faithful transmission of the functional dependencies across generations (e.g., vertical transmission)” 4 . At both the phylotype and SDP level, the gut bacterial community may encompass vast genes involving metabolism of sugar, amino acid and vitamin, which may compensate for digestion and nutrient availability that bees may not manage on their own. Nevertheless, it is worth noting that reciprocal functional dependencies between bee and the gut bacteria and the resulting competitiveness of native SDP over non-native SDP is not so strong to produce absolute host fidelity. This is supported by that fact that a minor proportion of non-native SDP is occasionally present in the bee gut as found by both field collection and co-inoculation experiments in the lab 17 , 27 . The existence of non-native SDPs suggests their dispersal mediated by social contact among bee colonies, e.g., through shared floral resources or hive robbing by sympatric bee species. While most of the laterally transmitted SDPs would be eliminated from the non-native bee host soon due to their poorer fitness, a few of them are likely fixed and inherited in a long term, leading to imperfect phylogenetic clustering by bee hosts 17 .

This study performs a comprehensive investigation into the host specificity of bee’s gut phages. Most of the bee phages share no sequence similarity with phages found in other animals. Currently, there are only a few literatures working on the gut phageomes of insects, including honey bee, Melipona quadrifasciata (a stingless bee), and mosquito 21 , 22 , 23 , 28 , 29 . These literatures all reported that the gut phages from insects had little overlap with the reference phage sequences. The distinct gene repertoire likely arises from niche specialization of the insect’s gut bacteria. Notably, the level of typing resolution shows critical importance on host specificity of both gut bacteria and phages. For bacteria, phylotypes are stable across bee species while SDPs are host specific. For phages, most of them are host generalist at the VC level but host specialist at the vOTU level. Actually, both phages and bacteria in the gut may repeatedly generate mutations, disperse and infect new host, adapt to and propagate in the new host, gradually evolve into new species, and mutate for host switch again. With a higher mutation rate, phages are better at adapting to new environment, namely they have a much higher frequency of successful host switch into new bacterial hosts (at the SDP level) compared to the bacterial SDPs switching into their non-native bee host, which eventually leads to the phages’ poorer host specificity. Only three VCs were identified to show perfect cophylogeny with host, indicating their potentially important contribution to the fitness of bees or gut bacteria. Meanwhile, their perfect cophylogeny also point to another possibility that these VCs are defective phages that can only replicate in the presence of helper virus. Since long-term stay within the bacterial genome attempts to synchronize the evolutionary pace of the phages and bacteria, the real evolutionary rate of phages at the lytic state is probably faster than that inferred in this study.

We measured the quantitative relationship between bacteriome and phageome in bee. By the Mantel test, we observed that the bacteriome’s composition at both phylotype and SDP level was associated with the phageome’s composition. There are mainly two models describing the virus-host dynamics 30 . The “Kill-the-Winner” represents the classical predator-prey model, in which phages predominantly prey on fast-growing bacteria. In contrast, the “piggyback-the-winner” model suggests that, as bacterial host become more abundant, phages forego rapid replication and opt instead to integrate themselves into their host, thereby reducing their numbers. Our findings indicate that Gilliamella phages replicate more slowly than Gilliamella itself, aligning with the “piggyback-the-winner” model. The enriched pathways for Gilliamella phages involve some metabolic functions, implying that they can modulate the metabolic state rather than hijack the bacterial cell. Unlike Gilliamella that is located on top of the mucus layer, Snodgrassella grows in contact with the ileum epithelium and does not directly interact with food 31 . This may explain why Snodgrassella and its phages possess fewer auxiliary genes with metabolic functions. However, due to the phageome’s greater compositional variability and the absence of core phage members, it is important to note that the impact of phage on bee’s health is probably more indirect and less significant than that of bacteria.

Within the “animal-gut bacteria-phage” tripartite system, the evolutionary rates displayed by the bee, the bacteria and the phages followed basically the same pattern, e.g., Ka, Ks and gene content variation accumulate during evolution while Ka/Ks declines as a result of purification of harmful mutations. Nevertheless, the exact values from the three biological entities differed greatly. Probably, this disparity stems from their inherent biological characteristics such as generation time and error-rate for DNA replication, which depend more on the taxonomic attribute than on the interactions between the three biological entities. In other words, the different evolutionary rates would also be observed between totally unrelated animals, bacteria and phages. To reconcile the different evolutionary rates within a frame of coevolution, selection on the whole holobiont, instead of on the individual parties separately, is required 32 . For the bee model in this study, one of the selections can be food digestion and nutrient utilization since the functional variation for bee, bacteria and phage all center around metabolism of sugar, amino acid and vitamin. However, such functional codependency tends to be not strong enough, and the absolutely faithful transmission is also difficult to achieve in the gut ecosystem. We therefore postulate that rigorous cophylogeny of “animal-gut bacteria-phage” tripartite system cannot widely apply to most of gut microbes. In this context, the very few bacteria and phages that show the cophylogenetic signal are of particular research value due to their potential benefits to host’s fitness.

Overall, this study integrates phage into the analysis of cophylogeny in gut microbiome and therefore represents a significant advancement toward understanding the coevolutionary history of the “animal-gut bacteria-phage” system. While only a few bacteria and phages have been identified as forming a chained cophylogeny with bees, there exist notable differences in their evolutionary rates between the three parities. Nevertheless, their functional variations are simultaneously associated with food digestion and nutrient utilization, highlighting the tripartite coevolution that results in an enhanced ecological fitness for the whole holobiont. To gain a deeper understanding of how gut microbes displaying cophylogenetic signals benefit their bee hosts, further in-depth and mechanistic studies in controlled laboratory conditions are necessary. While our bee model-based study provides a framework for future coevolution studies, it is imperative to expedite the exploration of the “animal-gut bacteria-phage” tripartite system in humans and mice. Despite technical obstacles caused by the complicated gut microbiota in mammals, this will provide insights into the significance of cophylogeny in humans and offer a novel perspective on the relevance of the gut ecosystem to human health.

Sample collection

A. cerana and A. mellifera workers were collected in Zhejiang (30.308 N, 120.092E), Yunnan (25.135 N, 102.756E) and Hainan (19.825 N, 109.695E) provinces in China, respectively. At each region, A. cerana and A. mellifera colonies have been reared in the same apiaries for years. Meanwhile, commercialized B. terrestris colonies (Biobest, China) were reared in the same apiaries for a duration of two months to allow potential transmission of gut bacteria and phages.

Bee genome sequencing and bioinformatic analyses

The genomic DNA was extracted from bee thoraces using Magnetic universal genomic DNA kit (TianGen Biotech, China). Genomic sequencing libraries were generated using NEB Next Ultra DNA Library Prep Kit for Illumina (New England Biolabs, USA), and were then sequenced on the Illumina Nova-seq 6000 platform with 150-bp paired-end reads.

The reads were de novo assembled into contigs by using MegaHit v1.2.9 with default settings 33 . Genes in the bee genomes were predicted by using GeneID v1.4.4 34 . For construction of orthologous gene relationship, a reference gene pool was built. All complete and draft genomes of A. cerana , A. mellifera and B. terrestris were downloaded from NCBI Genbank database. Orthologous gene families of their genes were built using OrthoFinder v2.5.2 35 . The 0/1 matrix indicating gene presence/absence was subject to principal components analysis (PCA) by R software v4.0.3.

Single-copy conserved genes were considered as core gene families and were used for inferring core genome-based phylogeny. The coding sequences were aligned at the protein level with Mafft v7.471 36 , and back-translated to nucleotides. The resulting concatenated alignments was processed by IQtree v2.2.0 for construction of maximum likelihood (ML) tree with GTR + F + I model 37 . The evolutionary time trees comprising the three bee species was meanwhile inferred by Timetree database v5 24 .

Gut bacteriome sequencing and bioinformatic analysis

For A. cerana and A. mellifera , approximately 100 foragers were collected at entrances for each colony. For B. terrestris , 30 workers were collected in the nest. After anesthetization of the bees by CO 2 , guts were pulled out, pooled, and homogenized with a bead-beater. The homogenates were firstly centrifuged at 500 × g for 5 min to remove debris and then centrifuged at 5000 × g for 5 min to pellet the bacterial cells. Bacterial DNA was extracted from the bacterial pellets using a CTAB-based DNA extraction protocol 18 .

Sequencing of the gut bacteriome followed the same protocol as the bee genome sequencing. To infer de novo metagenome assemblies, paired-end reads were firstly mapped against the reference bee genomes using Bowtie v2.4.1 38 . The GenBank accession number of reference genomes for A. cerana , A. mellifera and B. terrestris are GCF_001442555.1 (ACSNU-2.0), GCA_003254395.2 (Amel_HAv3.1) and GCF_000214255.1 (Bter_1.0), respectively. After filtering off the host derived reads, the unmapped reads for each sample were assembled independently using MegaHit. Putative open reading frames (ORFs) were predicted with Prokka v1.14.6 using the metagenome flag 39 . The functional profile of the entire gut bacteriome was inferring by using METABOLIC v4.0 40 , with the assembled contigs and raw reads being the input.

To obtain the taxonomic information at the phylotype level, the ORF sequences from all samples were pooled together and a non-redundant gene catalog was obtained by CD-HIT v4.7 with parameters “-c 0.95, -aS 0.9” 41 . DIAMOND v0.9 was used to search the non-redundant protein sequences against the NCBI microNR database v13 42 . The taxonomic information of each gene was determined using the lowest common ancestor-based algorithm (LCA) implemented in MEGAN v6.20 43 . Then each contig was assigned to a phylotype to which the largest number of ORFs in this contig was assigned. The abundance of each phylotype was measured by mapping reads against contig sequences by Bowtie and summing the read numbers of contigs assigned to the phylotype. Both the calculation of Shannon index and the PCoA analysis, which were based on the phylotype abundance, was calculated using R vegan package v2.6.2 44 .

The taxonomic assignment at the SDP level following the methods from Ellegaard and Engel 16 with small modifications. All available published genomes of Gilliamella and Snodgrassella strains were downloaded from NCBI Genbank database (Supplementary Table 2 ). The orthologous relationship was constructed by using OrthoFinder, and the ML phylogenetic tree based on concatenated conserved genes was made following the same methods for bee as above. Based on the tree phylogeny, the isolates were clearly assigned into A. cerana , A. mellifera and Bombus clades. When all genes from the isolate genomes made a reference gene database, query ORFs from samples were searched against the database with NCBI Blastn v2.11.0 with a minimum percentage identity of 80% and a minimum alignment length of 150 bp. According to the best hit, each ORF was assigned to one of A. cerana , A. mellifera and Bombus clade. Then each contig was assigned to a host-specific SDP to which the largest number of ORFs in this contig was assigned; and all ORFs in the contig were re-assigned to the same SDP as their contig was. For each sample, therefore, all of the contigs corresponding to the sample’s bee source constituted the MAGs of its native SDP, whereas the contigs not corresponding to the sample’s bee source constituted the MAGs of non-native SDP. Last, reads for each sample were mapped to the MAGs for abundance estimation of native and non-native SDPs. For each sample’s native SDP, its 0/1 gene matrix indicating gene presence/absence in the MAG was subject to PCA by R. This 0/1 matrix was also used for core-pan genome analysis by using PanGP v1.0.1 45 . The genes conserved among all samples’ native SDP were extracted for core genome phylogeny following the same method for bee as above. Pairwise ANI values between the MAGs of all samples’ native SDP were calculated using FastANI v1.33 26 .

Three approaches were adopted for assessing the recombination in bacterial genomes. First, for each of the single-copy conserved genes, the gene’s ML tree was constructed by IQtree and then was compared with the aforementioned core genome-based ML tree by using the treedist program in Philip v3.69 46 , with the branch score distance method. The larger the distance, the more likely the gene was involved in recombination. Second, still for each of the conserved genes, the PhiPack software was run to produce a p -value of recombination 47 , with p-value less than 0.05 being considered to involve recombination. Third, recombinant segments within the concatenated alignment of core genes were detected by using FastGear 48 . The extent of recombination in bee genomes were also assessed using the first and the second approach.

Gut phageome sequencing and bioinformatic analyses

Extraction of VLPs followed the method by Bonilla-Rosso et al. 23 and He et al. 49 . Briefly, the supernatant obtained from centrifuging the homogenates of gut tissues (as aforementioned in the section “Gut bacteriome sequencing and bioinformatic analysis”) was filtered through 0.22-μm-pore-sized filters (BIOFIL, China), followed by PEG6000 (GENERAYBIO, China) flocculation and centrifugation at 25,000 × g for 2 h to concentrate VLP. The VLP solution was further incubated with DNase I (Takara, China) to remove free DNA. Next, the VLPs were extracted with TE buffer, lysed by SDS and proteinase K (Vazyme, China), and incubated with CTAB/NaCl. Finally, the viral DNA was extracted with phenol/chloroform/isoamyl alcohol protocol.

To assess the quality of enrichment of the viromes, the raw fastq data were processed with the ViromeQC v1.0 50 . Genome sequencing and de novo assembly followed the same method as the bacteriome sequencing. The phage sequences were identified using VIRSorter v2.2.2 from the derived contigs 51 . Meanwhile, putative prophage sequences were identified from the isolate genomes of Gilliamella and Snodgrassella downloaded from the public database as well as the MAGs of bacteriome derived from this study. The phage and prophage sequences were pooled and processed with vContACT v2, which generated probabilistic VCs with high similarity 52 . The reference viral sequences organized by the ICTV bacterial viruses subcommittee were also added for the vContACT clustering analysis 53 . Taxonomic annotation of VC was determined based on the vContACT results: if a viral sequence was within the same VC as a reference genome, then the viral sequence was considered to belong to the same genus as the reference genome does. The vContACT clustering results were visualized by Cytoscape v3.7.1 54 .

The abundance of phage members in each sample was measured by the number of reads mapped to the phage sequences by Bowtie. The abundance of the phage members of the same VC were summed to obtain the VC abundance in each sample. To assess the quantitative relationship between baceriome and phageome, the Mantel test was used to compare the Bray-Curtis dissimilarity for the bacteriome and for the phageome by R ape package v5.6 55 , for which the bacteriome referred to the phylotype level and the phageome referred to the VC level. Linear fit of Bray-Curtis dissimilarity between bacteriome and phageome was run by using the lm function in R. The hypothesis of cophylogeny between bee and bacteria and between bacteria and phages was tested using the Parafit function in R ape package 56 .

Host prediction for phages was based on two strategies. First, CRISPR spacers were predicted from the isolate genomes and MAGs using CRISPR Recognition Tool v1.2 57 . This CRISPR-spacer collection was subsequently searched against the phage sequences using the blastn software with the additional settings -ungapped and -perc_identity 100. Blast hits indicated the host relationship. Second, phage members within the same VC by vCONTACT2 analysis were considered to have the same host phylotype. The quantitative relationship between bacteriome at the SDP level and phageome was also determined: the Bray–Curtis dissimilarity for the bacteriome was calculated based on the SDP abundance, and the Bray-Curtis dissimilarity for the phageome was calculated for the phylotype-specific VCs.

At a finer level of taxonomy, the phages and prophages were also assigned to vOTU by using dRep v3.4.1 58 , with the default 90% of ANI as the threshold. The genetic difference between phage members was defined as 100% minus ANI calculated by dRep. The gene content difference between phage members was defined as the number of unshared protein clusters divided by total non-redundant protein clusters of the two compared phages. The information of protein clusters was generated from the vContACT analysis as described above.

Tripartite system analysis

The sequences of nodes that represented ancestral A. cerana , A. mellifera and Apis were inferred by IQtree, with the aligned conserved genes and the corresponding ML tree as the input file and -asr as the input parameter. The ancestral states of gene presence/absence that reflected gene gain and loss during divergence were determined by Dollo parsimony algorithm in Count v9.1106 59 .

Ka, Ks and Ka/Ks were calculated by the codeml program in PAML v4.9 60 . In detail, NSsites was specified to be ‘0 1 2 7 8’. In these models, Model 0 was used to calculate one Ka/Ks ratio for all branches; comparison between M1a and M2a and between M7 and M8 was performed to identify positively selected sites through Bayes Empirical Bayes analysis. Genes with Ka/Ks >1 or containing positively selected sites were regarded as genes under positive selection.

Function annotation was performed by using the eggNOG-mapper server 61 , which included annotation by Gene Ontology (GO) and KEGG database. Functional enrichment analysis was performed with the clusterProfiler package v4.0 in R. The enriched GO terms were visualized by REVIGO online service in the Treemap form 62 .

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

Raw reads were deposited in NCBI’s Sequence Read Archive (SRA) database under project accession no. PRJNA1026499, PRJNA1027301, PRJNA1027315, PRJNA1027983, PRJNA1027985, PRJNA1028003, PRJNA1029451, PRJNA1029457, PRJNA1029459. The detailed accession numbers for each dataset are listed in Supplementary Table 3 . The source data in this study are available at https://doi.org/10.5281/zenodo.12673332 .

Code availability

The scripts used in this study are available at https://doi.org/10.5281/zenodo.12673332 .

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant number 32072798), Science and Technology Department of Zhejiang Province, China (grant number 2021C02068-8); China Agriculture Research System of MOF and MARA (grant number CARS-44).

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Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China

Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, China

Ye Feng, Qiuli Chen, Tongyao Shang & Zeyu Wang

College of Animal Sciences, Zhejiang University, Hangzhou, China

Ruike Wei, Nihong Zhou, Gongwen Chen, Guozhi Zhang, Fuliang Hu & Huoqing Zheng

USDA-ARS Bee Research Laboratory, Beltsville, MD, USA

Yanping Chen

Eastern Bee Research Institute, College of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China

Environment and Plant Protection Institute, Chinese Academy of Tropical Agriculture Sciences, Haikou, Hainan, China

Yihai Zhong

Guangdong Key Laboratory of Animal Conservation and Resource Utilization, Guangdong Public Laboratory of Wild Animal Conservation and Utilization, Institute of Zoology, Guangdong Academy of Sciences, Guangzhou, China

Hongxia Zhao

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Y.F. and H. Zheng conceived the study. N.Z., G.C., G.Z., K.D., Y.Z., and H. Zhao collected samples and generated metagenome data. Y.F., R.W., Q.C., T.S., and Z.W. performed data analysis. Y.F. drafted the manuscript. Y.F., Y.C., F.H., and H. Zheng revised the manuscript.

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Correspondence to Ye Feng or Huoqing Zheng .

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Feng, Y., Wei, R., Chen, Q. et al. Host specificity and cophylogeny in the “animal-gut bacteria-phage” tripartite system. npj Biofilms Microbiomes 10 , 72 (2024). https://doi.org/10.1038/s41522-024-00557-x

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DOI : https://doi.org/10.1038/s41522-024-00557-x

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Title: raw-adapter: adapting pre-trained visual model to camera raw images.

Abstract: sRGB images are now the predominant choice for pre-training visual models in computer vision research, owing to their ease of acquisition and efficient storage. Meanwhile, the advantage of RAW images lies in their rich physical information under variable real-world challenging lighting conditions. For computer vision tasks directly based on camera RAW data, most existing studies adopt methods of integrating image signal processor (ISP) with backend networks, yet often overlook the interaction capabilities between the ISP stages and subsequent networks. Drawing inspiration from ongoing adapter research in NLP and CV areas, we introduce RAW-Adapter, a novel approach aimed at adapting sRGB pre-trained models to camera RAW data. RAW-Adapter comprises input-level adapters that employ learnable ISP stages to adjust RAW inputs, as well as model-level adapters to build connections between ISP stages and subsequent high-level networks. Additionally, RAW-Adapter is a general framework that could be used in various computer vision frameworks. Abundant experiments under different lighting conditions have shown our algorithm's state-of-the-art (SOTA) performance, demonstrating its effectiveness and efficiency across a range of real-world and synthetic datasets.

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