Data from: Wolbachia impacts microbiome diversity and fitness-associated traits for Drosophila melanogaster in a seasonally fluctuating environment
Data files
Aug 13, 2024 version files 1.17 GB
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dl_1.csv
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dl_2.csv
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dl_3.csv
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dl_4.csv
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dl_5.csv
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dl_6.csv
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dl_7.csv
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dl_8.csv
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field19_total_meta.tsv
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field2019_cleanF.Rds
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fly.001-read-1.fastq
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fly.001-read-3.fastq
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isofemales_lifespan.csv
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otu_matrix.csv
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ph_starv_coxestimates.csv
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README.md
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starv1_clean.csv
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starv2_clean.csv
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starv3_clean.csv
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taxonomy_fullnames.tsv
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tops_bc_modstats.csv
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tree.nwk
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wolbachia_screen.csv
Abstract
The microbiome contributes to many different host traits, but its role in host adaptation remains enigmatic. The fitness benefits of the microbiome often depend on ecological conditions, but theory suggests that fluctuations in both the microbiome and environment modulate these fitness benefits. Moreover, vertically transmitted bacteria might constrain the ability of both the microbiome and host to respond to changing environments. Drosophila melanogaster provides an excellent system to investigate the impacts of interactions between the microbiome and the environment. To address this question, we created field mesocosms of D. melanogaster undergoing seasonal environmental change with and without the vertically transmitted bacteria, Wolbachia pipientis. Sampling temporal patterns in the microbiome revealed that Wolbachia constrained microbial diversity. Furthermore, Wolbachia and a dominant member of the microbiome, Commensalibacter, were associated with differences in two higher-order fitness traits, starvation resistance and lifespan. Our work here suggests that the interplay between the abiotic context and microbe-microbe interactions may shape key host phenotypes that underlie adaptation to changing environments. We conclude by exploring the consequences of complex interactions between Wolbachia and the microbiome for our understanding of eco-evolutionary processes that shape host-microbiome interactions.
README: data for Wolbachia impacts microbiome diversity and fitness-associated traits for Drosophila melanogaster in a seasonally fluctuating environment
https://doi.org/10.5061/dryad.547d7wmg3
Data was generated from a study of Drosophila undergoing seasonal adaptation in NJ during summer-fall 2019.
Description of the data and file structure
Data for temperature data loggers are in files called "dl_N.csv
", with N = 1-8 to note the different cages. Each column for the data loggers is as follows: S/N = arbitrary sorting, Time = time the measurement was collected, Temperature = temperature in Celsius, Humidity%RH = %relative humidity.
The following files are for microbiome data (generated using QIIME2 and exported to analyses/visualization in R):
field19_total_meta.tsv
is the metadata that relates sequencing library to collected metadata. The columns are as follows: flyid = individual fly, BarcodeSequence = is for demultiplexing, for.sort = an arbitrary sorting variable, plate.pos = position within 96 well plate during DNA extraction and library prep, pcr.id = internal note for when PCR took place, cage = cage from where the fly collected, wolbachia = wolbachia status, timepoint = discrete variable for collection time, datecollected = date collected (each timepoint matches a date), protK = protK treatment during DNA extraction, day_since_start = time since the field experiment started, merge.group = variable that merges together the +/- protK from individual flies, seq.run = sequencing run, arb.sort = another arbitrary sorting variable.
otu_matrix.csv
is the ASV matrix (rows are ASVs, columns are individual flies)
taxonomy_fullnames.tsv
links ASV ID to taxonomic assignment. Please note that there are empty cells in this taxonomy file that are used to filter out later. The Rcode will later fill in empties. Rows are ASVs, columns are the different taxonomic ranks.
tree.nwk
is phylogenetic tree
field2019_cleanF.Rds
is the phyloseq object with the cleaned up data (contaminants removed, protK merged, remaining wolbachia removed)
The data for Wolbachia frequency over time:\
wolbachia_screen.csv
For wolbachia_screen.csv, columns are as follows: cage : cage the fly was collected from the field, date = collection date, sample = sample ID from each cage per time point, wolbachia = y (yes, presence of band during PCR check), n (no, no band).
The data for the three starvation timepoints:
starv1_clean.csv for Day 96
starv2_clean.csv for Day 116
starv3_clean.csv for Day 127
For all of the starv files columns are as follows: fly.id = individual fly measured, camera = camera used to film, imageplate = plate used to image flies, imagewell = position in the 24 well plate, hour = hour of death, minute = minute of death, timetodeath = adds hour and minute together, cage = cage the fly was collected from in the field, sex = sex of fly, wolbachia = wolbachia status, full.treat = combines cage + sex + wolbachia status, code = 2 for survminer (all dead)
The data for the three starvation timepoints:
isofemales_lifespan.csv
contains lifespan measurements at the end of the season (Day 127).
The columns are as follows: tube = tube ID that matched an individual fly, death day = date that fly died, start. date = date of collection, days.alive = length fly was alive, cage = cage from where the fly was collected, wolbachia = wolbachia status, code = 2 for survival package (all are 2)
The data from statistical model outputs that were later visualized
ph_starv_coxestimates.csv
contains the estimates from the Cox models to visualize effect size over time. The columns are as follows: timepoint = timepoint from where the flies were collected, coefficient = model coefficient, se = standard error, group = model term from either Wolbachia or Commensalibacter
tops_bc_modstats.csv
contains the variance explained by fixed effects in the analysis of community turnover in the top10 most abundant bacteria. The columns are as follows: model = how many bacteria were included in the statistical model (i.e., top04 = top 4 most abundant bacteria, top05 = top 5 most abundant bacteria, etc), r2 marg = marginal r-squared value from fitted model, r2 con = conditional r-squared value from fitted model, p val season = the p-value for the estimate for time since start of the experiment, p val wolb = the p-value for the estimate for the wolbachia term, p val interact = the p-value for the time * wolbachia interaction term, interact.sig = whether or the not the interaction term was significant (p<0.05).
FASTQ files:
fastq files correspond to the "field19_total_meta.tsv". files with "-read-1.fastq" are forward, while "-read-3.fastq" are reverse.
Sharing/Access information
Sequence is uploaded here.
Code/Software
field2019_qiimecode.txt
contains code run to generate ASV and taxonomy assignments using QIIME2 v2020.6
field2019_v4_share.Rmd
is the R markdown containing all analyses. The following packages are required: phyloseq, ggplot2, tidyverse, RColorBrewer, ggpubr, scales, decontam, vegan, lubridate, btools, lmerTest, lme4, forcats, survival, survminer, coxme, and mgcv