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. 2019 Jan 24;7(1):10.
doi: 10.1186/s40168-019-0622-9.

A Longitudinal Assessment of Host-Microbe-Parasite Interactions Resolves the Zebrafish Gut Microbiome's Link to Pseudocapillaria Tomentosa Infection and Pathology

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Free PMC article

A Longitudinal Assessment of Host-Microbe-Parasite Interactions Resolves the Zebrafish Gut Microbiome's Link to Pseudocapillaria Tomentosa Infection and Pathology

Christopher A Gaulke et al. Microbiome. .
Free PMC article

Abstract

Background: Helminth parasites represent a significant threat to the health of human and animal populations, and there is a growing need for tools to treat, diagnose, and prevent these infections. Recent work has turned to the gut microbiome as a utilitarian agent in this regard; components of the microbiome may interact with parasites to influence their success in the gut, meaning that the microbiome may encode new anthelmintic drugs. Moreover, parasite infections may restructure the microbiome's composition in consistent ways, implying that the microbiome may be useful for diagnosing infection. The innovation of these utilities requires foundational knowledge about how parasitic infection, as well as its ultimate success in the gut and impact on the host, relates to the gut microbiome. In particular, we currently possess limited insight into how the microbiome, host pathology, and parasite burden covary during infection. Identifying interactions between these parameters may uncover novel putative methods of disrupting parasite success.

Results: To identify interactions between parasite success and the microbiome, we quantified longitudinal associations between an intestinal helminth of zebrafish, Pseudocapillaria tomentosa, and the gut microbiome in 210 4-month-old 5D line zebrafish. Parasite burden and parasite-associated pathology varied in severity throughout the experiment in parasite-exposed fish, with intestinal pathologic changes becoming severe at late time points. Parasite exposure, burden, and intestinal lesions were correlated with gut microbial diversity. Robust generalized linear regression identified several individual taxa whose abundance predicted parasite burden, suggesting that gut microbiota may influence P. tomentosa success. Numerous associations between taxon abundance, burden, and gut pathologic changes were also observed, indicating that the magnitude of microbiome disruption during infection varies with infection severity. Finally, a random forest classifier accurately predicted a fish's exposure to the parasite based on the abundance of gut phylotypes, which underscores the potential for using the gut microbiome to diagnose intestinal parasite infection.

Conclusions: These experiments demonstrate that P. tomentosa infection disrupts zebrafish gut microbiome composition and identifies potential interactions between the gut microbiota and parasite success. The microbiome may also provide a diagnostic that would enable non-destructive passive sampling for P. tomentosa and other intestinal pathogens in zebrafish facilities.

Keywords: Intestine; Microbiome; Nematode; Parasitism; Pseudocapillaria tomentosa; Zebrafish.

Conflict of interest statement

Ethics approval and consent to participate

The use of zebrafish in this study was approved by the Institutional Animal Care and Use Committee at Oregon State University (permit number: 4800).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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

Figures

Fig. 1
Fig. 1
Sampling strategy and physiological manifestations of P. tomentosa exposure. a Exposure and sampling strategy. b The number of worms observed in the intestine of individual fish (wet mount) at each sampling time point. c Weights of unexposed (blue boxes) and exposed fish (red boxes) at each sampling time point
Fig. 2
Fig. 2
Longitudinal histopathological changes during P. tomentosa infection. a Inflammation, b hyperplasia, and c total histopathology score in animals exposed to P. tomentosa across the length of experiment
Fig. 3
Fig. 3
Pathologic changes in fish infected with Pseudocapillaria tomentosa. Hematoxylin and eosin stained sections of parasite exposed and unexposed intestines (a-f). a A representative unexposed, control fish with minimal cellularity in the lamina propria and numerous goblet cells (arrows). b Intestine of a P. tomentosa-exposed fish at 7 dpe exhibiting mild hyperplasia (score 1) and containing numerous larval worms (arrows). c An exposed fish at 59 dpe exhibiting severe hyperplasia (score 3) with increased basilar nuclei (E) extending to near the brush border in some locations, numerous rodlet cells (arrows), and expanded lamina propria (L) due to chronic inflammation. d Chronic inflammation, inflammation score 3, with extensive expansion of the lamina propria (L), largely dysplastic epithelium (E), and loss of epithelial cell polarity (hyperplasia score 3) in an exposed fish at 59 dpe. e Extensive flattening of epithelial folds (E) and moderate expansion of lamina propria (L) due to chronic inflammation (hyperplasia 3, inflammation 2) at 86 dpe. f A carcinoma in an exposed fish at 86 dpe with neoplastic epithelial cells proliferating in the lamina propria (L), invading through tunica muscularis (M), and nests of neoplastic cells (S) present in the serosa. Scale bars = 50 μm
Fig. 4
Fig. 4
Pseudocapillaria tomentosa exposure is associated with altered microbiome composition. a Shannon entropy in exposed (red boxes) and unexposed animals. Nonmetric multidimensional scaling plots of exposed and unexposed microbiomes colored by b days post exposure, c total histopathology score (gray points = N/A), and e total parasite burden (gray points = N/A). Correlations between d total histopathology score and MDS1 and f total parasite burden and MDS1. Blue lines indicate the loess best-fit line and the shaded gray area represents the standard error
Fig. 5
Fig. 5
Parasite burden is associated with microbial abundance. A heat map of coefficients from negative binomial generalized linear models with lowest AIC. The color of each cell represents the direction of the slope (red is negative, blue is positive). An asterisk in a cell indicates q < 0.15
Fig. 6
Fig. 6
Microbial abundance is associated with parasite burden and histopathology. A heat map of coefficients from converged zero-inflated negative binomial generalized linear mixed effects models with lowest AIC. The color of each cell represents the direction of the slope (red is negative, blue is positive). Gray colored cells indicate that a model without an interaction parameter was selected as it had the lowest AIC. An asterisk in a cell indicates q < 0.15

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