The document discusses the significant economic losses in aquaculture due to diseases and co-infections in farmed fish, highlighting that co-infections often lead to increased mortality and treatment challenges. It explains the dynamics of host-pathogen interactions during concurrent infections and presents various case studies illustrating the effects of specific co-infections on different fish species. The conclusion emphasizes the need for integrated approaches to study co-infections and their implications on fish health and aquaculture productivity.

1. Co-infection Studies in Farmed Fish:
Complexity & Implications
College of Fisheries, Dholi
(Dr Rajendra Prasad Central Agricultural University)
Muzaffarpur, Bihar – 843 121 (India)
By
Rajiv Kumar Brahmachari

2. • Global loss due to disease:
US$ 1.05 - US$ 9.58 billion annually (Shinn et
al., 2015)
• Global loss due to parasites:
U.S. $107.31 million to $134.14 million (Shinn et
al., 2015)
• Loss due to argulosis in Indian carp
aquaculture:
US$ 615.0 ha−1 annually (Sahoo et al., 2013)
• Loss due to sea lice in salmonid aquaculture:
> US$ 300 million annually (Costelo, 2009)
Highlights of some estimated
losses due to diseases in finfish
• Disease – a leading cause of economic losses in
aquaculture industry
− general assumption - infections = single
pathogen
• Co-infections may lead to increased mortality
− mechanics and prevalence - poorly documented
− affect host-pathogen dynamics, disease
severity, duration of infection and host
pathology
• other terms-
concurrent infections, concomitant infections,
mixed infections, multiple infections,
polymicrobial diseases, complicated infections,
dual infections, secondary infections
super infections
Introduction

3. Co-Infections
• “Infection of the host by two or more genetically different pathogens where each pathogen
has pathogenic effects and causes harm to the host” (Cox, 2001).
• Occur frequently in nature
• It can occur from two primary pathogens infecting the host concurrently, or one pathogen
can develop as a secondary infection (Kotob et al., 2016).
• During co-infections, one pathogen can alter the immune response of the host against the
subsequent infections by other pathogens either by suppressing or priming the immune
system (Lello et al., 2004; Telfer et al., 2008).

4. Consequences of Co-infection
Decreased resistance
to other diseases
Increased severity of
infection & mortality rate
Treatment difficulties/
Failure of vaccination
Parasites
Bacteria
Virus Fungi

5. Pathogen-pathogen
interaction
Host-pathogen
interaction
Co-infection
Bacteria Viruses
Scheme of factors that may influence a co-infection
directly between individuals
compete to evade the
immune responses
they elicit
compete for access to the
same host resources
Vorburger & Perlman, 2018

6. Schematic presentation of possible Interactions and Implications of Simultaneous
(Left) and Sequential (Right) Co-infections in a host
Karvonen et al., 2019

7. − the load of one or both pathogens may be
increased
Interactions during Co-Infections
Synergistic Antagonistic
− one or both may be suppressed
or
− one may be increased and the other
suppressed
• Competition for nutrients and places -- limit
the population size or alter the site of
infection
OR
• First pathogen triggers host immune
response and hinders the second pathogen
• First pathogen induces immunosuppression -
hinders the immune response against
subsequent infections - leading to an increase
in the severity of the infections and mortality
rates
Kotob et al. 2016

8. • Bacterial Co-infections
• Viral Co-infections
• Parasite Co-infections
With homologous pathogens With heterologous pathogens
• Parasite & Bacterial Co-infections
• Parasite & Viral Co-infections
• Bacterial & Viral Co-infections
• Fungal & Bacterial Co-infection
Co-infections
Kotob et al., 2016

9. Bacterial Co-Infections
Host Co-infecting pathogen Interaction Outcome of sequential coinfection /% mortality Reference
Pangasianodon
hypophthalmus
Edwardsiella ictaluri /
A. hydrophila
Synergistic E. ictaluri recovered only from fish exposed to this
pathogen, whereas A. hydrophila was recovered from all
the moribund fish
E. ictaluri - 80% / A. hydrophila – 10%/ Co-infection
– 95%
Crumlish et al.
2010
E. ictaluri /
Flavobacterium
columnare
Synergistic E. ictaluri – 80% / F. columnare - 3.3% /Co-infection –
96.7%
Dong et al.,
2015
Salmo salar Aliivibrio wodanis /
Moritella viscosa
Antagonistic A. wodanis affected M. viscosa growth
M. viscosa 96%, A. wodanis 8%, Mv & Aw – 94-98%
Aw-then-Mv -84%
Karlsen et al.,
2014
Ictalurus
punctatus
A. veronii/Shewanella
purefaciens/Streptococcus
parauberis
Antagonistic S. parauberis and S. putrefaciens could not be recovered
from fish at the end of the trial
A. veronii 100%/ S. purefaciens 0%/S. parauberis 0% /
Co-infection – 80%
Mohammed
and Peatman,
2018
Rachycentron
canadum
Vibrio harveyi /
Photobacterium damselae
Synergistic P. damselae - 80% / V. harveyi – 80%/ Co-infection –
100%
Ramachandra
et al. 2020

10. Host Co-infecting pathogen Interaction Outcome of sequential coinfection Reference
Japanese
flounder,
Paralichthys
olivaceus
Aquabirnavirus/VHSV Antagonastic
Non-specific protection conferred by the primary ABV
infection against the secondary VHSV infection
commenced at Day 3 and persisted up to Day 14 but
vanished at Day 21 post-ABV challenge.
ABV – 0%; VHSV – 100%; Co-infection ABV than
VHSV – 90%
Pakingking et
al., 2004
in vitro,
grouper fin cell
line GF-1
GNNV / snakehead
retrovirus (SnRV)
Synergystic
SnRV alone does not induce CPEs, the CPE induced by
GNNV in the titration system may be enhanced by
SnRV competition for cell resources
Lee et al.,
2002
Viral Co-Infections

11. Kumar et al., 2018
Immunological outcomes of heterologous viral infections

12. Host Co-infecting pathogen Interaction Outcome of sequential coinfection Reference
Wild brown
trout, Salmo
trutta
Myxozoan
Tetracapsuloides
bryosalmonae / nematode
Raphidascaris acus
Synergistic
T. bryosamonae detected from all sampling sites, with
similar renal pathology. During winter months,
recovery was mainly influenced by the presence or
absence of concurrent infection with R. acus larvae.
Fish without R. acus regenerated completely,
concurrently infected fish showed incomplete
recovery, with chronic renal lesions and incomplete
translocation of T. bryosalmonae from the renal
interstitium into the tubular lumen.
Schmidt-
Posthaus et
al., 2013
Atlantic salmon,
Salmo salar
Neoparamoeba perurans
(AGD) / Lepeophtheirus
salmonis
Synergistic
An association between N. perurans and the salmon
louse Lepeophtheirus salmonis was observed with
increased mortality
Nowak et al.,
2010
Parasitic Co-Infections

13. Heterologous Co-Infections
Host Co-infecting pathogen Interaction Outcome of sequential coinfection Reference
Atlantic
salmon, Salmo
salar
sea louse Caligus
rogercresseyi/bacterial
pathogen Piscirickettsia
salmonis
Synergistic Coinfection with CAL+PS decreased the
survival and growth of vaccinated Atlantic
salmon (against PS) compared to a single
infection with PS
Only 5.2% of coinfected fish survived
compared to 42.7% with PS infection
Figueroa et
al., 2017
Nile tilapia,
Oreochromis
niloticus
Gyrodactylus
cichlidarum/ A.
hydrophila
Synergistic Gyrodactylus caused mechanical injuries
to the epithelium thus creating portals of entry
for A. hydrophila which Induced exaggerated
fish mortalities during summer.
co-infected group – 42.2%, AH – 6.7%; GC – no
mortality
Abdel-Latif
& Khafaga,
2020
Gold fish,
Carassius
auratus
Argulus /Aeromonas
hydrophila
Synergistic high parasite load leads to high mortalities
compared to low parasitic load after goldfish
were exposed to an equal dose of A.
hydrophila.
AH -33%, H A -75%, H A +AH 84.2%
Shameena
et al., 2021

14. Host Co-infecting pathogen Interaction Outcome of sequential coinfection Reference
Red hybrid
tilapia,
Oreochromis
spp.
TiLV / A. hydrophila Antagonestic mortality rate for TiLV-infected tilapia was the most
rapid in TiLV single-infection scenario, whereas it
decreased when tilapia exposed to AH but it
increased transmission rate of TiLV
For mortality rate of TiLV–AH-infected population in
TiLV–high AH scenario, it was two times higher than
that in TiLV–Low AH scenario
Lu et al. 2021
Nile tilapia
(Oreochromis
niloticus)
TiLV / A. veronii (including
34 other bacterial isolates)
Synergistic cumulative effects of viral and bacterial pathogens
could result in the rapid progression of the disease
and high mortalities
Rao et al.
2021

15. Kinetics of influenza viral infection and susceptibility to bacterial co-infection in mice
Metzger & Sun, 2013

16. Co-infection Modulating Oxidative Stress, Immune
Response, and Disease Severity
Devi et al., 2021
Downregulation
Primary infection
Co-infection
ROS
Oxidative Stress
Inflammatory
response
Immune response
Tissue damage
Disease severity
RNS
Upregulation
Argulus /Aeromonas hydrophila co-
infection in gold fish
Immune response
• ↑ NBT, MPO and lysozyme activity
oxidative stress
• ↑ SOD, catalase and GPx activity
Shameena et al., 2021

17. Factors Affecting Co-infection Dynamics
Host resistance factors
Pathogen factors
Environmental factors
Species
Age
Immune state
Ability to infect species
Dose
Infection sequence
Population density
Temperature
Water quality
Other stressors

18. Experimental Models to Study Co-infection
Cell lines
Mathematical
Model
Organoids
Fish
Advantages
• Infinite growth
• Enable high throughput
screening
Disadvantages
• Lack of heterogenity
• Low success rate & no
microenvironment and
immune influence
Advantages
• Cheaper, easier & quicker
Disadvantages
• Give partial description
• Work for restricted range of
values
Advantages
• Heterogenity
• High throughput screening
Disadvantages
• development in infancy stage
• Low success rate & no
microenvironment and
immune influence
Advantages
• Wide availability
• Interaction among pathogen
can be studied
Disadvantages
• Lab condition may not mimic
natural condition for biotic/
abiotic stressor
• Poor reproducibility of results

19. Co-infection Challenge Studies
Case Study

20. Experimental Design
Cumulative mortality of red hybrid tilapia

21. Mathematical Modelling for Coinfection study

22. Lu et al., 2021
 Berkeley Madonna 8.0.1 model was applied to estimate epidemiological
parameters by adopting the single- and coinfection dynamic models to fit
the time course cumulative mortality data of tilapia.
 Briefly, tilapia population was divided into five states of susceptible
(S), bacteria infected (IB), virus-infected (IV), bacteria–virus-infected
(IBV), and mortality (M).
 Tilapia take times τB and τV, respectively, to generate initial bacteria-
infected (kB) and virus-infected populations (kV).
 The transmission rates for susceptible become infected with AH, TiLV,
and AH−TiLV after contacting tilapia in coinfection state (IBV) are βB(1 −
βV), βV(1 − βB), and βBβV, respectively.
In the coinfection model, R0 for TiLV-, AH-, and AH–TiLV-infections
could be calculated, respectively, as βV/αV, βB/αB, and βVβB/αBV.
R0 < 1 disease will disappear over time, whereas R0 > 1 there will be an
epidemic
Result
R0 for TiLV transmission with TiLV followed by high AH = ~11.
Dose-response model reveals that AH exacerbated the mortality risk of
TiLV-infected tilapia under coinfection.

23. Figure highlights the importance of an
integrative approach across multiple
hierarchies to study co-infections, from the
perspective of,
(A)interacting microorganisms (Red),
(B) nature of interactions between them
(Yellow),
(C) mechanisms mediating interactions
(Magenta),
(D) macro level effects of co-infections
(Green),
(E) health interventions affected by co-
infections (Green circles), and
(F) multi-omics approaches to study of co-
infections (box)
Devi et al., 2021
Hierarchical role of co-infections as
cause, modulator and effector of clinical
outcomes

24. • Presence of co-infection are mostly associated with poor outcomes such as
reduced productivity & increased mortality
• It may lead to failure of existing treatment measures
• Co-infections models may help in development of Vaccines/ new drugs or
drug combinations that could be effective in the treatment and prevention
of opportunistic infections
Conclusion & Way forward

25. • Abdel‐Latif, H.M. and Khafaga, A.F., 2020. Natural co‐infection of cultured Nile tilapia Oreochromis niloticus with Aeromonas hydrophila and Gyrodactylus cichlidarum
experiencing high mortality during summer. Aquaculture Research, 51(5), pp.1880-1892.
• Costello, M., 2009. The global economic cost of sea lice to the salmonid farming industry. Journal of fish diseases, 32(1), p.115.
• COX, F.E., 2001. Concomitant infections, parasites and immune responses. Parasitology, 122(S1), pp.S23-S38.
• Crumlish, M., Thanh, P.C., Koesling, J., Tung, V.T. and Gravningen, K., 2010. Experimental challenge studies in Vietnamese catfish, Pangasianodon hypophthalmus
(Sauvage), exposed to Edwardsiella ictaluri and Aeromonas hydrophila. Journal of fish diseases, 33(9), pp.717-722.
• Devi, P., Khan, A., Chattopadhyay, P., Mehta, P., Sahni, S., Sharma, S. and Pandey, R., 2021. Co-infections as Modulators of Disease Outcome: Minor Players or Major
Players?. Frontiers in Microbiology, p.1894.
• Dong, H.T., Nguyen, V.V., Phiwsaiya, K., Gangnonngiw, W., Withyachumnarnkul, B., Rodkhum, C. and Senapin, S., 2015. Concurrent infections of Flavobacterium
columnare and Edwardsiella ictaluri in striped catfish, Pangasianodon hypophthalmus in Thailand. Aquaculture, 448, pp.142-150.
• Figueroa, C., Bustos, P., Torrealba, D., Dixon, B., Soto, C., Conejeros, P. and Gallardo, J.A., 2017. Coinfection takes its toll: Sea lice override the protective effects of
vaccination against a bacterial pathogen in Atlantic salmon. Scientific reports, 7(1), pp.1-8.
• Karvonen, A., Jokela, J. and Laine, A.L., 2019. Importance of sequence and timing in parasite coinfections. Trends in parasitology, 35(2), pp.109-118.
• Kotob, M.H., Menanteau-Ledouble, S., Kumar, G., Abdelzaher, M. and El-Matbouli, M., 2017. The impact of co-infections on fish: a review. Veterinary research, 47(1),
pp.1-12.
• Kumar, N., Sharma, S., Barua, S., Tripathi, B.N. and Rouse, B.T., 2018. Virological and immunological outcomes of coinfections. Clinical microbiology reviews, 31(4),
pp.e00111-17.
• Lee, K.W., Chi, S.C. and Cheng, T.M., 2002. Interference of the life cycle of fish nodavirus with fish retrovirus. Journal of general virology, 83(10), pp.2469-2474.
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• Lu, T.H., Chen, C.Y. and Liao, C.M., 2021. Aeromonas hydrophila as an environmental indicator to detect TiLV-infected tilapia under coinfection threat. Environmental
and Sustainability Indicators, 11, p.100135.
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27. Thank You!