DOI: 10.5061/dryad.zs7h44jmn
title: Data from: Host genetics and the skin microbiome independently predict parasite resistance

description: Host responses to parasite infection involve several interacting systems.
 Host genetics determine much of the response, but it is increasingly clear
 that the host-associated microbiome also plays a role. Genetically
 determined systems and the microbiome can also interact; for example, the
 microbiome can modulate the immune response, and vice versa. However, it
 remains unclear how such interactions between the host immune system and
 the microbiome may influence the host’s overall response to parasites. To
 investigate how host genetics and the microbiome interact to shape
 responses to parasites, we imposed truncation selection on Trinidadian
 guppies (Poecilia reticulata) for low and high resistance to the
 specialist ectoparasite Gyrodactylus turnbulli. After 3-6 generations of
 breeding without parasites, we sampled the skin-associated microbiome and
 infected fish from each line. We applied Dirichlet Multinomial Modeling
 (DMM) machine-learning to identify bacterial community types across lines
 and evaluated how selection line and community type explained variations
 in infection severity. Our findings showed that among females, the
 resistant line had significantly lower infection severity, while the
 susceptible line had higher infection severity. Among males, only the
 susceptible line experienced higher infection severity compared to the
 other lines. Line did not explain skin microbial diversity, structure or
 composition. Our DMM analysis revealed three distinct bacterial community
 types, independent of artificial selection lines, which explained just as
 much variation in infection load as selection line. Overall, we found that
 the microbiome and host genetics independently predict infection severity,
 highlighting the microbiome's active role in host-parasite
 interactions.
description: # Data from: Host genetics and the skin microbiome independently predict
 parasite resistance Dataset DOI:
 [10.5061/dryad.zs7h44jmn](10.5061/dryad.zs7h44jmn) ## Description of the
 data and file structure In this study, we wanted to understand how certain
 factors can affect the outcome of parasitic infections. These factors
 include artificial selection, the gender of the fish, and the community of
 microorganisms living on the fish's skin. To set up the artificial
 selection process, we infected 200 *Poecilia reticulata* fish (half male
 and half female) with a parasite called *Gyrodactylus turnbulli*. We
 closely monitored the infection, then divided the fish into two groups:
 one group with the top 30 % of fish having the highest average worm burden
 (susceptible line) and the other group with the bottom 30 % of fish having
 the lowest average worm burden (resistance line). We also had control
 groups that were made up of uninfected fish randomly selected from the
 same population. Data on these fish is found in **ParentalSelection.csv.**
 After this initial selection process, we allowed the fish from each group
 to breed within their respective lines for four to seven generations
 without exposure to the parasite. Before infecting the offspring from the
 artificial selection, we collected swabs from the adult fish to measure
 the communities of microorganisms living on their skin.
 **SelectionData2.csv:** In our analysis, the most important data can be
 found in the file 'SelectionData2.csv,' which contains various
 variables used in our main statistical models: * **fishID:** Identity of
 the focal fish * **line:** The 30 % most intense infection founded the
 susceptible line and the 30 % least intense infections founded the
 resistance lines. The controls lines were founded with the same number of
 parents of randomly selected uninfected fish from the same population. *
 **JulienDays:** Experimental dates when the fish were swabbed and infected
 * **sex:** Sex of the fish * **length, weight:** Length in mm and weight
 (mass) in mg of the fish * **respreL:** The residuals of length of the
 focal fish on sex. Females tend to be larger than males: using the
 residuals of this relationship allows us to test for both size and sex
 differences in behavior. * **PREsmi** The residuals of the scaled mass
 index of fish * **rSMIpre** Body condition - (rescaled so that males and
 females could be compared) scaled mass index * **AUC** the Area Under the
 Curve for worm days / time "infection integral" This data was
 subsetting to run analysis appropriately, all dataframes are explained
 below. #### The following sub-setting of Dataframe's explained: **IO
 Data frame**: Only offspring that were infected with *G.turnbulli*, and
 will be used in GLMM model. **female and male Data frames**: Subset F4-7
 offspring fish that were infected with *G. turnbulli* parasite sub-setting
 to contain only female fish or male fish for post-hoc analysis There is
 evidence that the tiny organisms living in and on hosts, called the
 microbiome, might be passed down from one generation to the next and have
 evolved alongside their hosts (a component of the "holobiont"
 theory). To find out whether the microbiome on the skin of our fish played
 a role in making them more or less resistant to infections, we collected
 samples from 51 fish: Control (n = 15), Resistant (n = 18), Susceptible (n
 = 17), Sham-infected fish (n = 20). We then analyzed the communities of
 microorganisms in these samples using 16s rRNA sequencing. Our goal was to
 see if the fish from different lines had different associated skin
 microbiomes, and if these microbiomes were related to how susceptible or
 resistant they were to infections. In continuation of the above analysis,
 the most important data can be found in the file
 'SelectionData.csv.' This section expands to microbial community
 dataframes: * **weighted_distance-matrix.csv:** Beta-diversity obtained
 from Qiime2 export pipeline- Unweighted and Weighted Unifrac distance
 matrices for PCoA creation and PERMANOVA analysis to which factor explains
 microbial variation on guppies' skin * **Discrete_abundances.csv**:
 Relative frequencies of bacterial taxa data table exported from Qiime2
 after DADA2 processing and SILVA classification **Sub-setting of
 SelectionData.csv explains:** **M1 Data frame**: Subset (n = 51) F4-7
 offspring fish (sham infected included) that were swabbed and sent for 16s
 rRNA 515F--806R sequencing **MicroDat Data frame**: Subset (n = 31) of
 parasite-infected offspring fish that were swabbed and sent for 16s rRNA
 515F--806R sequencing **Kramp et al Microsatellite data.xlsx:** All
 microsatellite data for genotyping fish
 **TaxaDifferencesandabundances.csv:** All taxa and count using DMM
 modeling **Tableofblanks.csv:** All features found in DNA extraction
 blanks **Kramp_et_al_Micosatellite_data.csv:** Contains information on
 Guppy Microsatellite Locus and genotypes.