SUPPLEMENTARY METHODS
DGGE PCR conditions
PCR amplification of 16S rRNA genes for DGGE surveys were performed using
primers 356F and 517R (Table S2). Reactions were performed using APEX HotStart Taq
DNA Polymerase (Genesee Scientific, San Diego CA) in a total volume of 20 μl using
10X buffer with 1.5 mM MgCl2, 200 μM dNTPs and 0.5 μM each primer. PCR mixes
were then subjected to the following conditions: 95°C for 15 min. to activate the enzyme,
then 20 cycles of 94°C for 1 min., a 65 – 55°C touchdown for 1 min. (-0.5°C/cycle) and
72°C for 1 min., followed by 25 cycles of 94°C for 1 min., 55°C for 1 min. and 72°C for
1 min., and, finally, 8 min. at 72° C followed by a 4°C hold.
To identify the origins of DGGE bands, after electrophoresis, those of interest
were excised from the gel, eluted in 50 μl of PCR-grade H2O, then incubated at 37°C for
30 min. before PCR amplification (as above) with primers 356F and 517R. This PCR
product was purified using Fermentas (Glen Burnie, MD) Exo I and FastAP
Thermosensitive Alkaline Phosphatase or an Omega Bio-Tek (Norcross, GA) E.Z.N.A.
Cycle Pure Kit (per manufacturer’s instructions). The resulting DNA extraction was then
re-amplified as per the DGGE PCR reaction above, and run out again on a 1% agarose
gel to confirm that only a single band remained. Purified products were then sequenced in
the forward and/or reverse direction(s) by Eurofins MWG Operon (Huntsville, AL).
Generating expected frequencies for particular infection types
To illustrate how we generated expected symbiont frequencies and numbers,
we provide the following example. The expected frequency of H. defensa-S.
symbiotica-Rickettsiella superinfections in UT alfalfa was estimated with the
following equation: Expected Frequency H. defensa-S. symbiotica-Rickettsiella-superinfection-UT =
Freq H. defensa-UT * Freq S. symbiotica-UT * Freq Rickettsiella-UT * (1- Freq R. insecticola-UT) * (1- Freq
Rickettsia-UT)
* (1- Freq Spiroplasma-UT) * (1- Freq X-type-UT). This value was multiplied by the
number of aphids sampled from this population to give the total number of expected
UT aphids with the given superinfection. Similar calculations were performed for
the other four populations. These values were then summed to yield the total
number of expected aphids with the given superinfection across all populations. The
number of observed aphids with this infection was also summed, and comparisons
were made using 2-tailed Fisher’s Exact Tests.
The expected frequency of superinfected aphids (i.e. those with two or more
secondary symbionts) was similarly calculated separately for each population,
multiplied by sample size for the respective population, and then summed across
populations to give the total number of aphids expected to have more than one
secondary symbiont. The following formula was used to generate expected
frequencies within each population: Expected Freq superinfected aphids = 1 – Freq uninfected
aphids
– Expected Frequency H. defensa single infection – Expected Frequency R. insecticola single
infection –
Expected Frequency S. symbiotica single infection – Expected Frequency Rickettsiella single
infection –
Expected Frequency X-type single infection – Expected Frequency Spiroplasma single infection
– Expected Frequency Rickettsia single infection.
SUPPLEMENTARY DISCUSSION
Causes and consequences of superinfections
Theoretical studies have led to the prediction that superinfections could
favor the evolution of more virulent symbionts (Frank 1992), although selection on
multiply infected hosts is likely to favor symbiont cooperation of vertically inherited
microbes (Vautrin et al. 2008). Regardless, superinfection creates opportunities for
genetic exchange among co-inhabiting symbionts (Bordenstein& Wernegreen
2004), making this phenomenon of importance when considering the population
genetics and evolution of symbionts, and the potential spread of beneficial traits
across species.
Experimental studies have revealed that the consequences and dynamics of
superinfection by heritable symbionts vary across host insects. In several cases,
Wolbachia densities appear not to respond to the presence of other, co-infecting
strains from this symbiont species (Ikeda et al. 2003; Mouton et al. 2003), as
predicted by at least one theoretical model (Engelstadter et al. 2007). In contrast,
densities of Wolbachia were found to decrease in pupae and young adults of
Drosophila melanogaster that also harbored Spiroplasma symbionts (Goto et al.
2006), resembling similar findings for co-infecting Wolbachia in other host systems
(Kondo et al. 2005; Narita et al. 2007). From the hosts’ perspective, studies on
parasitic wasps have shown that superinfection can eliminate fitness benefits
(White et al. 2011), alter the type of cytoplasmic incompatibility (Mouton et al.
2005), or lead to changes in symbiont virulence (Mouton et al. 2004). But not all
superinfections alter the outcomes of symbiosis. For instance, the defensive effects
of Spiroplasma against parasitic nematodes still appeared in tact for fruit fly hosts
co-infected with Wolbachia (Jaenike et al. 2010b).
So why might superinfections persist within host populations? One
possibility is that they are favored by selection, perhaps by conferring multiple
beneficial phenotypes (e.g. resistance to pathogens and wasps) or through additive
or synergistic effects (e.g. increased protection). Jaenike and colleagues (Jaenike et
al. 2010a) proposed that non-random associations between symbionts could extend
from beneficial effects of one symbiont on another’s transmission, although no
evidence was found for this in the Drosophila neotestacea system. These authors
also noted that enrichment for particular co-infections could reflect a population
that is not at equilibrium, perhaps due to hitchhiking of one symbiont along with
another that has recently spread via positive selection.
Another possible explanation for common co-infection in the field involves
acquisition via non-vertical routes of transfer, including sexual transfer and
horizontal transmission via host plants or natural enemies. Such transfer among
cytoplasmic backgrounds would facilitate the generation of multiple infections,
which would otherwise tend to be lost over time due to occasional vertical
transmission failure. In A. pisum, both H. defensa and R. insecticola have been shown
to undergo transfer from males to sexual females, infecting their progeny and
persisting in some instances for multiple generations (Moran& Dunbar 2006). These
two symbionts can similarly move among Aphis fabae (black bean aphid) through
the contaminated ovipositors of parasitoid wasps (Gehrer& Vorburger 2012), while
Rickettsia symbionts can be efficiently transferred between whiteflies through plant
phloem (Caspi-Fluger et al. 2012). Although a broader assessment of this trend
within field populations is still essential, the growing number of avenues apparently
available for symbiont transfer suggest that non-vertical transmission may be more
common than originally thought ((Baldo et al. 2008); but see (Atyame et al. 2011)).
Perhaps, then, we should see frequent occurrence of superinfection for
symbionts with a known capacity to move non-vertically. To date, the most
promising avenue for such movement seems to be sexual transfer from males to
females, observed for H. defensa and R. insecticola. Might such transmission make
these former symbionts more likely to engage in multiple vs. single infections? The
answer, thus far, appears to be no. First, across all of our surveyed populations we
saw close agreement to the number of expected superinfections including H. defensa
(57.7 out of 103 aphids with H. defensa) and the number actually observed (52).
Furthermore, R. insecticola superinfections were expected in 51.5 of the 87 aphids
harboring this symbiont, yet only 21 such co-infections were observed.
In summary, superinfections are common, but the forces maintaining them in
natural populations are largely unknown. Experimental or population level studies
on the phenotypic and fitness effects of various combinations, along with fine scale
phylogenetics and field-studies examining transmission routes and rates, would be
useful in determining the relative importance of selective versus non-selective
factors involved in the maintenance of symbiont “cocktails”.
SUPPLEMENTARY TABLE LEGENDS
Table S1: Representative 16S rRNA sequences from DGGE reactions. Sequences
include products from heritable, secondary symbionts along with those from
suspected non-symbionts. The ‘A’ sequences derive from extracted DGGE bands of
secondary symbionts using the 365F primer (Table S2). The ‘A1’ bands are from
typical DGGE products and ‘A2’ and ‘A3’ sequences (when present) represent
repeated variants. The ‘B’ sequences derive from universal 16S – 23S rRNA 559F35R PCR reaction (amplifies most bacteria but not Buchnera, Table S2) amplicons
using the forward primer. These latter amplicons were generated for samples that
yielded DGGE results showing evidence for only Buchnera or faint bands that could
not be confirmed after extraction and sequencing. The ‘C’ sequences are from
diagnostic PCR confirmation using the Rickettsia specific primer 16SR (Table S2),
and ‘D’ sequences are ‘non-symbiont’ bands from DGGE using the 356F primer. See
Excel file.
Table S2: PCR primers and reaction conditions used in this study. see Word
document for full legend
Table S3: Summary of 454 sequencing results. The proportions of 16S rRNA
sequence reads assigned to each symbiont clade (heritable symbionts in red) or
bacterial OTU (non-symbionts in black) are indicated for products sequenced from
10 pools of 18-25 aphids from five different populations. RDP classification of
representative sequences is provided (down to the lowest level with 80% bootstrap
support), as are phylogenetic affinities/closest relatives of representative
sequences. Total numbers of quality sequence reads for each library are indicated at
the bottom of each column.
Table S4: Results of secondary symbiont surveys across the five sampled
populations. Presence (“1”) versus absence (“0”) is shown for all seven major
secondary symbionts (and bacteriophage APSE) across surveyed pea aphids, based
on the results of DGGE and confirmatory diagnostic PCR. The numbers of symbionts
found within individual aphids is provided, along with information on aphid
collections (dates, locations, host plants) and an indication of whether aphids were
included in our quantitative analyses.
Table S5: Statistical analyses comparing symbiont frequencies across
populations. Results of 2-tailed Fisher’s Exact Tests are included for comparisons
between host races (within PA) and between locations (for alfalfa-feeding aphids).
P-values are indicated for all relevant comparisons, as are indications of significance
after Bonferroni correction and significance in separate tests on a subset of PA
aphids collected during the same time frame.
Table S6: Statistical analyses comparing symbiont frequencies over time in the
PA alfalfa population. Results of 2-tailed Fisher’s Exact Tests are included for
comparisons between adjacent collection time points (bins of 4 week “quarters”).
Table S7: Statistical analyses on the numbers of symbionts per aphid across
populations. Numbers of symbionts per aphid are shown for each population (as in
Table 2) along with p-values from t-tests comparing symbiont #’s/individual across
populations. Also indicated is significance after Bonferroni correction and results of
t-tests on a dataset including only infected aphids (i.e excluding all uninfected
aphids).
Table S8: Observed and expected numbers of aphids harboring specific single,
double, triple, and quadruple infections. Results of 2-tailed Fisher’s Exact Tests
are included for all infection types for which the numbers of observed or expected
aphids was greater than two. Also indicated are significance levels after Bonferroni
correction.
Table S9: Observed and expected numbers of aphids harboring particular
pairwise combinations of symbionts. Results of 2-tailed Fisher’s Exact Tests are
included for all pairwise infection types for which the numbers of observed or
expected aphids was greater than two. Also indicated are significance levels after
Bonferroni correction.
Table S10: Genotypes of H. defensa and APSE from NY, PA, WI, and UT aphids.
Sequences of the recJ and 16S rRNA gene from H. defensa and of the P3 gene of H.
defensa-associated APSE phage were included in maximum likelihood phylogenetic
analyses. Clades in each of these three gene trees were identified and numbered,
and used to assign allele IDs to all sequences. Genotype IDs were then derived for all
strains with sequences from all three genes. Genotypes that are potentially the
result of recombination or horizontal phage transferred are indicated.
SUPPLEMENTARY FIGURE LEGENDS
Figure S1: Example Denaturing Gradient Gel Electrophoresis (DGGE) result.
Shown here are DGGE results of 16S rRNA PCR products from bacteria of ten aphid
clones (Lanes 1-5, 7-11) along with a “standard” sample containing DNA from four
of the known secondary symbionts of aphids. While symbionts typically yielded a
single band, the unique four-band pattern for some H. defensa is shown in lanes 4, 5,
10, and 11 (and clearly labeled in lane 11). Clones with products in lanes 1, 2, 7 & 8
harbored S. symbiotica; those in lanes 1, 4, 5, 10, & 11 H. defensa; and those in lanes
3 & 9 possessed R. insecticola. Occasionally, lanes would exhibit very faint bands
with migration patterns similar to H. defensa (e.g. 3 and 9), but diagnostic PCR failed
to confirm these infections. We thus considered these clones to be free of H. defensa.
Figure S2: Proportions of 454 16S rRNA sequence reads corresponding to
various bacteria. Reads with closest relatedness to heritable symbionts are
indicated in color, while those with relatedness to bacterium from non-animal hosts
are indicated in white, gray, and black. Shown in these graphs, specifically, are the
proportions of reads averaged across two libraries separately across five aphid
populations. Source DNA for 16S rRNA amplification and sequencing was derived
from 18-25 aphids per library.
Figure S3: 16S rRNA haplotypes for three secondary symbionts at likely
polymorphic sites. The proportions of sequence reads from each population
corresponding to the detected sequence haplotypes are indicated for H. defensa,
Rickettsia, and R. insecticola across five populations.
Figure S4: Maximum likelihood phylogeny of 16S rRNA genes of Regiella.
Sequences obtained here through 454 (arrows) and Sanger sequencing (asterisks)
are included here, as are those of close relatives identified from GenBank. Bootstrap
values over 80% are shown on their respective nodes.
Figure S5: Maximum likelihood phylogeny of 16S rRNA genes of Rickettsia.
Sequences obtained here through 454 (arrows) and Sanger sequencing (asterisks)
are included here, as are those of close relatives identified from GenBank. Bootstrap
values over 80% are shown on their respective nodes.
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