Deep data mining reveals variable abundance and distribution of microbial reproductive manipulators within and among diverse host species

Abstract

Bacterial symbionts that manipulate the reproduction of their hosts are important factors in invertebrate ecology and evolution, and are being leveraged for host biological control. Infection prevalence restricts which biological control strategies are possible and is thought to be strongly influenced by the density of symbiont infection within hosts, termed titer. Current methods to estimate infection prevalence and symbiont titers are low-throughput, biased towards sampling infected species, and rarely measure titer. Here we develop a data mining approach to estimate symbiont infection frequencies within host species and titers within host tissues. We applied this approach to screen ~32,000 publicly available sequence samples from the most common symbiont host taxa, discovering 2,083 arthropod and 119 nematode infected samples. From these data, we estimated that Wolbachia infects approximately 44% of all arthropod and 34% of all nematode species, while other reproductive manipulators only infect 1-8% of arthropod and nematode species. Although relative titers within hosts were highly variable within and between arthropod species, a combination of arthropod host species and Wolbachia strain explained approximately 36% of variation in Wolbachia titer across the dataset. To explore potential mechanisms for host control of symbiont titer, we leveraged population genomic data from the model system Drosophila melanogaster. In this host, we found a number of SNPs associated with titer in candidate genes potentially relevant to host interactions with Wolbachia. Our study demonstrates that data mining is a powerful tool to detect bacterial infections and quantify infection intensities, thus opening an array of previously inaccessible data for further analysis in host-symbiont evolution.

Fig 1. A schematic of the computational pipeline used to determine the reproductive manipulator infection status and symbiont titer of a sequencing run.

The pipeline (A) takes in a sample’s unique identification number, then downloads two million reads (includes symbiont and host genomic reads). Then, (B) reads are aligned to Wolbachia, Arsenophonus, Spiroplasma, Cardinium, and Rickettsia reference genomes. Also, reads are aligned to a set of 1066 single copy ancestral orthologs obtained from ORTHODB v9 to estimate host coverage without requiring a reference genome. (C) Summary statistics for sample reads aligned to each reference are computed. If a sample had between 0.1 and 0.9 breadth of coverage, the full dataset was downloaded and the workflow repeated to prevent false negative calls. We apply coverage breadth and depth cutoffs to classify infection status as positive, or negative. To estimate symbiont titer, we compare the depth of coverage of host reads mapped to a set of single copy orthologs, to the coverage of symbiont reads mapped to a symbiont reference genome. Please see the methods section for more details on the approach for classification and titer computation.

Fig 2. Phylogeny of Arthropoda orders tested and number of reproductive manipulator positive species within each order.

The frequency of reproductive manipulator-positive species listed in parentheses. No frequency is listed if there was no infection within an arthropod order. We used the Tree of Life taxonomic and phylogenetic package and rotl [74], to group host species by their orders. We labeled arthropod clades containing two or more taxa with subphylum or class.

Fig 3. Titer variation across species infected with Wolbachia and between diverse reproductive manipulator clades.

To increase readability of both plots, categories were randomly downsampled to show 100 samples. The y-axis is log10 scaled. (A) Titer for Wolbachia positive arthropod species with at least three samples were plotted from low to high titer and color coded by taxonomic order. (B) Titer for Wolbachia, Rickettsia, Arsenophonus, Spiroplasma infected samples. We plotted up to three samples for every species infected with Wolbachia to show the range of Wolbachia across tested arthropod species. Titer variation within host species is significant, and this variation is not due to pooled sequencing samples. Our results suggest a symbiont and host genetic contribution to shaping within-host infection densities. Asterisks indicate statistically significant relationships between titer and arthropod species where 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’.