Susceptible and infectious states for both vector and host in a dynamic pathogen-vector-host system
Data files
Oct 13, 2023 version files 28.22 KB
Abstract
Deformed wing virus (DWV) is a resurgent insect pathogen of honey bees that is efficiently transmitted by vectors and through host social contact. Continual transmission of DWV between hosts and vectors is required to maintain the pathogen within the population, and this vector-host-pathogen system offers unique disease transmission dynamics for pathogen maintenance between vectors and a social host. In a series of experiments, we measured vector-vector, host-host and host-vector transmission routes and show how these maintain DWV in honey bee populations. We found co-infestations on shared hosts allowed for movement of DWV from mite to mite. Additionally, two social behaviors of the honey bee, trophallaxis and cannibalization of pupae, provide routes for horizontal transmission from bee to bee. Circulation of the virus solely amongst hosts through communicable modes provides a reservoir of DWV for naïve Varroa to acquire and subsequently vector the pathogen. Our findings illustrate the importance of community transmission between hosts and vector transmission. We use these results to highlight the key avenues used by DWV during maintenance and infection and point to similarities with a handful of other infectious diseases of zoonotic and medical importance.
README: Susceptible and infectious states for both vector and host in a dynamic pathogen-vector-host system
https://doi.org/10.5061/dryad.9zw3r22mw
This data set contains the count data, and RTqPCR results for a series of experiments for the manuscript: Susceptible and infectious states for both vector and host in a dynamic pathogen-vector-host system.
Description of the data and file structure
The data is stored in 4 separate Excel files (csv). All of these csv’s can be imported into the accompanying R code.
The data sets are not extensive and can be navigated manually or through the accompanying R code.
The following text provides legends for each excel file.
CanabalSISI
| legend | |
|---|---|
| Type | the cage the sample came from. |
| Cage | describes the exposure given to that group of bees. |
| Group | is a categorical variable that separates experimentally exposed samples from non-exposed samples |
| State | defines the state the mite was in prior to the start of the trial- infectious or naïve. In this case everyone was naïve |
| Primer | primer that was used to detect either wildtype or NanoLuc in qPCR |
| Virus | is the log10 per bee of viral loads calculated from ct values |
| Luc_Mite_Pupae | were provided a pupae that was fed upon by mites that acquired NanoLuc |
| NoVector | were provided nothing- no vector, no mite |
| Mite_Pupae | were provided a pupae that was fed upon by mites without any additional virus |
CoFeed_2022
| legend | |
|---|---|
| Trial | the trial |
| Type | a holding tag used during the experiment |
| Group | the experimental group |
| State | whether the sample was infectious or naïve at beginning of experiment.<br>I used named variables to sort background control samples in R in this column as well |
| Primer | which primer was used for which virus |
| Virus | the log10 genome equivalents of whichever primer |
| Easy | added so that I could properly sort into dataframes in R.<br>I am keeping the names here because I like that level of academic honesty |
SISI_2022
| legend | |
|---|---|
| Trial | the name of the trial |
| Type | a holding tag used during the experiment |
| State | what the sample is: EndPupae is a pupae which was fed on by a mite, Mite is a Varroa, Donor is from the donor group, recipient is from the recipient group. |
| Primer | which primer was used for which virus |
| Virus | is the log10 genome equivalents of whichever primer |
For simplicity, sample numbers, extraction numbers, etc. that were not needed for the R code were excluded from this list when setting up the CSV.
Code/Software
The data was analyzed in R using these packages: library
(ggplot2)(tidyverse)(broom)(AICcmodavg)(dplyr)(plyr)(ggpubr)
Nearly all the code needed for this analysis can be accessed via BaseR. As a note…there maybe more redundancy in my code than someone who has been coding extensively. You will notice I manually make data frames throughout. This was feasible with these small data sets.
Methods
The data was collected from experiments at the USDA-ARS in Beltsville, MD. The RTqPCR results were collected from processing samples on our BioRad machines. Count data was organized in Excel after being transferred from labnotebooks. Then data was analyzed in R.