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\begin{document}

\title{Tripartite associations between Afrotropical bats, eukaryotic parasites,
and microbial symbionts}



\author[1]{Holly Lutz}%
\author[1]{Jack Gilbert}%
\author[2]{Carl Dick}%
\affil[1]{University of California San Diego}%
\affil[2]{Western Kentucky University}%


\vspace{-1em}



  
  \date{June 03, 2024}


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\selectlanguage{english}
\begin{abstract}
Skin is the largest mammalian organ and the first defensive barrier
against the external environment. The skin and fur of mammals can host a
wide variety of ectoparasites, many of which are phylogenetically
diverse, specialized, and specifically adapted to their hosts. Among
hematophagous dipteran parasites, volatile organic compounds (VOCs) are
known to serve as important attractants, leading parasites to compatible
sources of blood meals. VOCs have been hypothesized to be mediated by
host-associated bacteria, which may thereby indirectly influence
parasitism. Host-associated bacteria may also influence parasitism
directly, as has been observed in interactions between animal gut
microbiota and malarial parasites. Hypotheses relating bacterial
symbionts and eukaryotic parasitism have rarely been tested among humans
and domestic animals, and have to our knowledge never been tested in
wild vertebrates. In this study, we use Afrotropical bats, hematophagous
ectoparasitic bat flies, and haemosporidian (malarial) parasites
vectored by bat flies as a model to test the hypothesis that the
vertebrate host microbiome is linked to parasitism in a wild system. We
identify significant correlations between bacterial community
composition of the skin and dipteran ectoparasite prevalence across four
major bat lineages, as well as striking differences in skin microbial
network characteristics between ectoparasitized and non-ectoparasitized
bats. We also identify links between the oral microbiome and presence of
malarial parasites among miniopterid bats. Our results support the
hypothesis that microbial symbionts may serve as indirect mediators of
parasitism among eukaryotic hosts and parasites.%
\end{abstract}%



\sloppy


Tripartite associations between Afrotropical bats, eukaryotic parasites,
and microbial symbionts

Lutz HL\textsuperscript{1,2,3}, Gilbert JA\textsuperscript{1,2} , Dick
CW\textsuperscript{,3,4}

\textsuperscript{1} Department of Pediatrics, University of California
San Diego, La Jolla, California, USA

\textsuperscript{2} Scripps Institution of Oceanography, University of
California San Diego, La Jolla, California, USA

\textsuperscript{3} Integrative Research Center, Field Museum of Natural
History, Chicago, Illinois, USA

\textsuperscript{4} Department of Biology, Western Kentucky University,
Bowling Green, Kentucky, USA

*Corresponding Author

Holly L. Lutz

Department of Pediatrics

University of California San Diego

9500 Gilman Drive

La Jolla, CA 92161

hllutz@health.ucsd.edu

ABSTRACT

Skin is the largest mammalian organ and the first defensive barrier
against the external environment. The skin and fur of mammals can host a
wide variety of ectoparasites, many of which are phylogenetically
diverse, specialized, and specifically adapted to their hosts. Among
hematophagous dipteran parasites, volatile organic compounds (VOCs) are
known to serve as important attractants, leading parasites to compatible
sources of blood meals. VOCs have been hypothesized to be mediated by
host-associated bacteria, which may thereby indirectly influence
parasitism. Host-associated bacteria may also influence parasitism
directly, as has been observed in interactions between animal gut
microbiota and malarial parasites. Hypotheses relating bacterial
symbionts and eukaryotic parasitism have rarely been tested among humans
and domestic animals, and have to our knowledge never been tested in
wild vertebrates. In this study, we use Afrotropical bats, hematophagous
ectoparasitic bat flies, and haemosporidian (malarial) parasites
vectored by bat flies as a model to test the hypothesis that the
vertebrate host microbiome is linked to parasitism in a wild system. We
identify significant correlations between bacterial community
composition of the skin and dipteran ectoparasite prevalence across four
major bat lineages, as well as striking differences in skin microbial
network characteristics between ectoparasitized and non-ectoparasitized
bats. We also identify links between the oral microbiome and presence of
malarial parasites among miniopterid bats. Our results support the
hypothesis that microbial symbionts may serve as indirect mediators of
parasitism among eukaryotic hosts and parasites.

Keywords: microbiome, bats, Chiroptera, Hippoboscoidea, malaria,
Haemosporidia, Afrotropics

1. INTRODUCTION

Animals are capable of hosting myriad biologically interdependent
symbionts including viruses, bacteria, archaea, and eukarya. Many
associations between eukaryotic parasites and hosts have ancient origins
(1, 2), and mounting evidence suggests that bacterial symbionts may be
responsible for mediating host-parasite interactions in ways that could
ultimately shape host evolution (3). For example, studies of human and
anthropophilic mosquito interactions have found that the human skin
microbiome can influence mosquito feeding preference, thereby affecting
transmission patterns of vector-borne pathogens (\emph{e.g.} WNV, yellow
fever, dengue, malaria, etc.), and potentially imposing selective
pressures on human populations (4). Conversely, parasites may influence
the relative abundance of host-associated microbes in ways that
facilitate parasite transmission, as has been observed in rodent models
of \emph{Plasmodium} transmission in which parasitism positively
correlates with abundance of skin-associated microbes that produce
volatile organic compound attractive to arthropod vectors
of\emph{Plasmodium} (5-7). Despite the potential evolutionary
significance of interactions between animal hosts, microbial symbionts,
and eukaryotic parasites, such interactions have not been well studied
in wild vertebrates.

Bats (Mammalia: Chiroptera) are an ideal system for examining the
interactions between microbial symbionts, eukaryotic parasites, and
hosts. Bats are among the most speciose orders of mammals (second only
to the Rodentia), providing a diverse comparative phylogenetic framework
for hypothesis testing, and they harbor a great diversity of eukaryotic
parasites such as dipteran insects, haemosporidia, and helminths (8-11).
Bats are also associated with microbial pathogens of importance for
human health (\emph{e.g.} \emph{Bartonella} , \emph{Pasteurella} , SARS
coronaviruses, filaviruses, rabies, Hendra and Nipah viruses (12-15)),
and serological surveys have supported the role of Afrotropical bats as
reservoirs for a number of viruses (16-18), making our understanding of
factors regulating parasite and pathogen transmission all the more
relevant. Inter- and intraspecific transmission of pathogens among bats
and other animals is an area of increasing concern in light of recent
zoonotic pandemics. Bat flies (Arthropoda: Hippoboscoidea), which are
obligate blood-feeding parasites of bats, are known to transmit several
pathogens of human relevance (19, 20) and also are the primary hosts for
bat-specific malarial parasites (Apicomplexa: Haemosporida). Bat flies
are typically host-specific (21, 22) and prevalence can vary widely
within a single species across different geographic locations (23).
Malarial parasites vectored by bat flies are also typically
host-specific but are observed less frequently in many bats relative to
ectoparasitic bat flies.

Bat flies are nutritionally dependent on their hosts and maintain
contact throughout most of their lives, living in fur and on skin
membranes and leaving their hosts only for brief reproductive periods
(24). How these parasites maintain host-specificity with bats over
evolutionary time is unknown, but blood protein and immunocompatibility
between host and parasite are thought to play a role (21, 25). The
proximal mechanism by which parasites locate ``preferred'' hosts is also
unknown, but as has been observed in other hematophagous parasites (e.g.
mosquitoes (26-28), tse-tse flies (29)), may involve host-associated
chemical cues. Chemical cues can be produced in a number of ways,
including via metabolic processes involving bacteria in the gut, oral
cavity, or on the skin (30, 31).

Here, we build on previous broad-scale studies of Afrotropical
bat-associated microbes (32, 33) to characterize parasitism by bat flies
and haemosporidia in four widespread lineages of bats. Using these data,
we test the hypothesis that the bat microbiome (skin, oral, and gut) is
linked to parasitism by two obligate, host-specific eukaryotic
parasites.

2. METHODS \& MATERIALS

2.1 Host and parasite sampling

Sampling was conducted at sixteen field sites in Kenya and Uganda from
August to October 2016, using a combination of mist-netting and
hand-netting. Samples were taken from bats collected as voucher
specimens for biodiversity inventories, allowing for extensive
post-mortem sampling of skin and fur from multiple points on the body.
These included three biopsies from wing membrane, one from tail
membrane, one from the ear, one from the interscapular region of the
back, and one from the interclavicular region of the chest (Figure 1)
using 3mm sterile disposable biopsy punches
(Integra\textsuperscript{TM}Miltex\selectlanguage{ngerman}®). Biopsy samples from each
individual were combined and stored in sterile 95\% ethanol. Whole
tongues were collected (from apex to root) and stored in 95\% ethanol
for oral microbiome analysis. We collected \textasciitilde{}2-4mL of
blood from euthanized bats via cardiac puncture. Blood was placed on
Whatman FTA cards for nucleic acid extraction, and 2-3 blood films per
individual were prepared for microscopic analyses. The remainder of each
blood sample was stored in cryovials placed in lN2. Following
euthanasia, skin, and blood sampling, bats were fumigated in ethyl
acetate for 15 minutes and then examined for ectoparasites. Presence or
absence of dipteran parasites was noted, and parasites were collected
into 95\% ethanol for taxonomic identification. Bat flies were
identified morphologically by examination under magnification using a
Leica MZ16 stereozoom microscope.~ Collected specimens were compared to
relevant taxonomic keys, to descriptions in the alpha-literature, and to
reference collections of the Field Museum of Natural History, Chicago
and the Bernice P. Bishop Museum, Honolulu.

Malarial parasite presence and taxonomic identity were determined as
described in Lutz et al. (2015). In brief, DNA was extracted from whole
blood using Qiagen DNeasy (Qiagen, Valencua, CA) and screened for the
presence of malarial parasites using triplicate PCR and Sanger
sequencing confirmation, followed by BLASTn to confirm parasite
taxonomy. All sampling was conducted in accordance with the Field Museum
of Natural History IACUC. Host and parasite vouchers are accessioned at
the Field Museum of Natural History (Chicago, IL, USA) (Table S1;S2).

2.2 Microbial DNA extraction, library preparation, and data generation

DNA was extracted using the MoBio PowerSoil 96 well soil DNA isolation
kit (catalog no. 12955-4; MoBio, Carlsbad, CA, USA) following the
standard Earth Microbiome Project protocol
(http://www.earthmicrobiome.org/). PCR amplification and sequencing were
performed as previously described in Lutz et al. (2019). Amplicon
sequence variants (ASVs) were identified using Deblur following standard
demultiplexing and quality filtering using the Quantitative Insights
into Microbial Ecology pipeline (QIIME2) (34). Skin, oral, and gut
libraries were rarefied to an even read depth of 5000 reads, 1000 reads,
and 1000 reads per library, respectively, based on rarefaction curve
estimates. All 16S rRNA sequence data are publicly available via the
QIITA platform (https://qiita.ucsd.edu) under the study identifier (ID)
11815 and the European Bioinformatics Institute (EBI) under accession
number PRJEB32520; additional sequence library information is provided
in Table S2. Code for sequence processing and analyses can be viewed at
https://github.com/hollylutz/BatMP.

2.3 Statistical analyses

Alphadiversity and betadiversity analyses were performed using the
programming language R (35) and packages vegan2.4-2 (36), phyloseq (37),
dplyr (38), and ggplot2 (39). Differences in mean alphadiversity
measures (observed richness and Shannon index metrics) between
ectoparasitized and non-parasitized bats within families were assessed
using the Kruskal-Wallis test. PERMANOVA tests for differences in
betadiversity were performed using the adonis2 function (R package
vegan2.4-2 (36), with 1000 permutations.

We evaluated differences among skin associated microbial ASVs between
parasitized and non-parasitized bats grouped at the host family level by
ranking multinomial regression coefficients (hereafter referred to as
ranked differentials). This approach, implemented using the program
Songbird (40), relies on estimated centered log ratios of features
between sample groupings and thereby surpasses the need for absolute
measures of feature differentiation between groupings. Songbird
multinomial regressions were run for 100,000 epochs with a batch size of
3, minimum feature count of 5, a learning rate of 1e-5, and a
differential prior of 0.50. Ranked differentials were visualized using
the program Qurro (41).

To examine whether skin microbial communities differ in stability and
structure between parasitized and non-parasitized bats, we reconstructed
skin microbial networks using the R package Sparse Inverse Covariance
Estimation for Ecological Association Inference (SPIEC-EASI) (42). All
network datasets were filtered to contain only ASVs that appeared in at
least three individuals within each respective dataset and consisted of
skin microbial libraries grouped by host family and ectoparasite status.
Network results produced with SPIEC-EASI were summarized using the R
packages CAVnet (43) and igraph (44). Network stability was assessed by
sequentially removing network nodes (ordered by betweenness centrality
and degree) and observing natural connectivity (\emph{i.e.} eigenvalue
of the graph adjacency matrix) as nodes are removed.

3. RESULTS

3.1 Microbiome, ectoparasite, and malarial parasite sampling and
detection.

We sampled 283 individuals representing eight species from four
chiropteran families (Hipposideridae, Miniopteridae, Rhinolophidae, and
Pteropodidae). Rarefaction and quality filtering of 16S rRNA libraries
resulted in the retention of 237 skin samples (29,270 ASVs, rarefied to
5000 reads), 202 oral samples (3,361 ASVs, rarefied to 1000 reads), and
230 gut samples (5,771 ASVs, rarefied to 1000 reads) for microbiota
profiling (Table 1). Hippoboscoid ectoparasites were recovered from all
host taxa, with an average prevalence of 51\% (SD±13\%). Malarial
parasitism was restricted almost entirely to the family Miniopteridae,
within which prevalence ranged from 47-65\% (mean 53\%±11\%). All
malarial parasites observed in miniopterids belonged to the
haemosporidian genus \emph{Polychromophilus} , and shared 99-100\%
sequence similarity to Cytochrome \emph{b} lineages previously
identified in Kenyan and Uganda miniopterid bats (11). We observed only
two non-miniopterid individuals of the species \emph{Rhinolophus
clivosus acrotis} (Rhinolophidae) to be positive for malarial parasites,
which belonged to the genus Nycteria and exhibited 98\% sequence
similarity to a Cytochrome \emph{b} lineage previously identified in
Uganda. All other bats were negative for haemosporidia by molecular and
microscopic analyses (Table S1).

3.2 Associations between the bat microbiota and ectoparasitism.

No differences were observed between skin, oral, or gut microbial
alphadiversity and ectoparasitized or non-ectoparasitized bats at the
host family or species levels (\emph{p} \textgreater{} 0.05,
Kruskal-Wallis) (Figure 2). However, subtle but significant differences
in skin-associated bacterial betadiversity were observed between
parasitized and non-parasitized bats for both weighted and unweighted
UniFrac metrics (\emph{p} \textless{} 0.005, PERMANOVA). Differences
were only observed among the gut and oral microbiota using unweighted
but not weighted UniFrac metric (\emph{p} \textless{} 0.002, PERMANOVA)
(Table 2), suggesting that differences were driven by the compositional
variance of rare taxa.

Multinomial regression analysis of the skin microbiome using Songbird
found models that included ectoparasite status of hosts significantly
outperformed null models (Miniopteridae pseudo Q\textsuperscript{2} =
0.12, Hipposideridae pseudo Q\textsuperscript{2} = 0.28, Rhinolophidae
pseudo Q\textsuperscript{2} = 0.17, Pteropodidae pseudo
Q\textsuperscript{2} = 0.26), allowing us to identify a number of ASVs
potentially associated with ectoparasitism (Figure 3). The bacterial
order Actinomycetales exhibited the greatest number of ASVs that were
differentially abundant between parasitized and non-parasitized bats.
Indeed, eight of fourteen ASVs found to be consistently associated with
the presence or absence of ectoparasites in all four host families
belonged to the order Actinomycetales, with the remaining six belonging
to the order Bacillales (phylum Firmicutes) and orders Burkholderiales,
Pseudomonadales, Rhizobiales, and Sphingomonadales (phylum
Proteobacteria) (Table 3).

Network analyses revealed striking differences in the topology and
stability of the skin microbiome in parasitized versus non-parasitized
bats, revealing a significant decrease in cluster size
(\emph{p}\textless{} 0.05, Mann-Whitney-Wilcoxon rank sum test) and
median node degree (\emph{p} \textless{} 0.05, \emph{t} test), as well
as a significant reduction in network connectivity (\emph{p} \textless{}
0.05, \emph{t} test) for parasitized bats from three of the four bat
families examined (pteropodids being the exception) (Figure 4).

3.3 Host microbiome and haemosporidian parasitism

Of the bat taxa sampled, only species belonging to the family
Miniopteridae exhibited malarial parasitism adequate for statistical
analysis. We observed no differences in alphadiversity of the skin,
oral, and gut microbiota of bats based on malarial infection status but
identified significant differences between unweighted UniFrac
betadiversity of the oral microbiota between malarial and non-malarial
bats (\emph{p} \textless{} 0.002, PERMANOVA) (Table 4). Multinomial
regression analyses identified a number of bacterial ASVs associated
with malarial parasitism. Two ASVs exhibiting the greatest proportional
increase in malaria positive bats belonged to the species \emph{Pantoea
agglomerans} and the genus \emph{Acinetobacter} . ASVs most strongly
associated with absence of malarial parasites belonged to the family
Pasteurellaceae (Figure 5).

\textbf{4. DISCUSSION}

In this study we identify associations between obligate ectoparasitic
bat flies and the skin microbiome of four Afrotropical bat families, and
limited association between rare taxa in the gut and oral microbiota.
Network analyses identified consistent, stable, and taxonomically rich
clusters of bacteria on the skin of non-ectoparasitized bats, compared
to relatively disconnected and apparently transient bacteria on the skin
of bats harboring ectoparasites. In addition to these links between
ectoparasitism and the bat microbiome, we found a significant
association between the oral microbiota and infection by malarial
parasites among bats belonging to the family Miniopteridae. These
results are the first to examine links between the microbiota and
eukaryotic parasitism in wild bats and support the hypothesis that
parasitism may be in part mediated by host-associated bacteria.

We found a number of ASVs belonging to the genus \emph{Mycoplasma} that
were associated with the presence of ectoparasites across multiple bat
families, as well as ASVs that were positively associated with
ectoparasitism in all four bat families studied, suggesting possible
convergence of bacterial associations with hippoboscoid ectoparasitism
among these hosts. Bacteria positively correlating with ectoparasitism
included Actinomycetales ASVs in the genera \emph{Corynebacterium}
,\emph{Dermacoccus} , \emph{Janibacter} , and \emph{Kocuria} . Some
bacteria in these genera have been shown experimentally to produce VOCs
that are attractive to other host-seeking hematophagus arthropods
including anopheline mosquitoes (26, 45) and \emph{Rhodnius prolixus}
kissing bugs (the primary vector of Chagas disease) (46), and it is
therefore interesting to find them consistently associating with
blood-feeding hippoboscoid across divergent bat families. Although we
did not quantify or characterize VOCs in this study, we hypothesize that
the bacterial ASVs in these genera may be producing similar VOCs, such
as sulfur-containing compounds identified in the head space
of\emph{Corynebacterium minutissimum} (\emph{e.g.} dimethylsulfide,
dimethyltetrasulfide, octasulfur) associated with anopheline mosquito
attraction to humans (45). Further validation is certainly needed.

Network analyses showed that presence of hippoboscoid parasites was
significantly associated with a reduction in the size and stability of
skin microbial clusters, with non-parasitized bats exhibiting fewer
clusters that contained greater microbial diversity. The differences in
these network statistics were shared by all four bat families in the
study and were significant for all but pteropodid fruit bats. Similar
patterns have been observed in human-mosquito interactions, in which
individuals with lower bacterial diversity on the skin are significantly
more attractive to blood-seeking mosquitoes than individuals with higher
diversity (27). In humans, skin bacteria play a known role in attracting
mosquitoes via their production of VOCs and studies have shown that
variation in skin microbial community composition can increase or
decrease human attractiveness to blood-seeking mosquitoes (7, 27, 28).
Similar mechanisms may be at play in the bat-ectoparasite system,
particularly given the shared evolutionary history of dipterans (47).

As suggested by studies of human-mosquito interactions (7, 27, 48),
bacteria positively associated with increased rates of blood-feeding
dipteran host selection may be producing VOCs on which the insects rely
to identify their hosts. Bacteria that are negatively associated with
such insects may be consuming the products of the former, or may be
producing VOCs of their own that mask those of the former (suggested by
Verhulst et al. (27)). To better understand the mechanisms underlying
these correlations in wild populations, future experiments should
consider including sampling and characterization of VOCs \emph{in
vivo}through mass spectrometry and other metabolomics approaches.

Associations between the oral microbiome and malarial parasitism were
supported by unweighted UniFrac diversity metric analysis, suggesting
that ASVs contributing to observed differences are relatively rare among
the oral microbiota. Upon further investigation of differential
microbiota abundances, we found a bacterial ASV belonging to the
species\emph{Pantoea agglomerans} to be most strongly associated with
miniopterid bats infected with malaria. Interestingly, \emph{P.
agglomerans} has been the target of numerous paratransgenesis
experiments aimed at controlling the transmission of malarial parasites
(\emph{Plasmodium} spp.) in anopheline and culicine mosquitoes (49). A
common constituent of the dipteran midgut, \emph{P. agglomerans} has
been associated with the production of `Immunopotentiator from \emph{P.
agglomerans} 1' (IP-PA1), a broad-spectrum antibiotic effective against
bacterial, fungal, and viral pathogens (50). How and why this bacterium
is associated with the oral microbiome of malarial bats requires more
in-depth investigation. As no other bat groups experienced rates of
malarial parasitism adequate for statistical analyses, we were unable to
explore this relationship further. Future studies that incorporate
greater sampling of malaria-positive species may reveal more robust
microbial associations, as have been documented in numerous experiments
with controlled rodent and human malaria infections (5-7, 48, 51, 52).

Although we cannot ascertain causality of differences in the microbial
composition of skin in this study, our results support the hypothesis
that these differences may provide a mechanism by which ectoparasites
can locate or distinguish hosts. Alternatively, observed differences in
microbial composition could result from microbial transfer from
parasites to hosts -- indeed \emph{Mycoplasma} bacteria, which were
commonly associated with ectoparasitism in our study, are a common
constituent of the hippoboscoid bat fly microbiome (53). Bat flies spend
the majority of their lives living on the skin and fur of their
chiropteran hosts, providing ample opportunity for the exchange of
microbes between bat and bat fly. Moreover, even some host-species
specific bat flies readily transfer between intraspecific host
individuals (54) effectively utilizing the host population as habitat.
Our analysis of the skin microbiota identified significant differences
in microbial betadiversity as well as differentially abundant bacteria
between parasitized and non-parasitized bats at the host family level,
but we were unable to ascertain the origin of these bacteria. Bacteria
associated with parasitized bats may have originated in the bat flies
themselves or may have been acquired from the environment. Given the
known effect of locality and apparent absence of host phylogenetic
signal in microbial community composition of skin (33), one possible
explanation is that local environmental variables play a greater role in
determining host-bacteria associations in bats. Indeed, in North
America, multiple bat species have been found to share many bacterial
genera with soil and plant material (55), and the bat skin microbiome
has previously been documented to shift at the colony level over time
(56). Thus, local conditions and bacterial composition of bat roosts are
likely playing an important role in driving the composition of skin
bacteria, and thereby potentially influencing which individuals become
parasitized. Ecological and behavioral studies of bats have also
observed that many species exhibit localized migration between caves,
and it has been suggested that this behavior may be associated with the
avoidance of parasites and pathogens (57). Longitudinal analyses of
individuals will provide much-needed insight into the effect of local
migration on skin microbial community composition and ectoparasite
prevalence.

ACKNOWLEDGMENTS

We thank the Kenya Wildlife Service and the Uganda Wildlife Authority
for permission to conduct research in national parks, and Simon Musila
of the National Museums of Kenya for his logistical support. For
assistance in the field, we thank Mike Bartonjo of the National Museums
of Kenya, Phausia Kweyu of Karatina University, Dr. Robert Kityo, Sadic
Babyesiza, Solomon Sebuliba, and Cissy Akoth of the Makerere University
Zoological Museum, and Drs. Brian Amman, Jonathan Towner, and Rebecca
Tiller of the Centers for Disease Control and Prevention for their
logistical assistance. HLL was supported by the National Science
Foundation Postdoctoral Research Fellowship in Biology (award number
1611948).

AUTHOR CONTRIBUTIONS

HLL conceived and designed study, and performed field work, laboratory
and data analyses. JAG guided statistical analyses and provided
laboratory support. CWD provided taxonomic identification of
hippoboscoid parasites. All authors contributed to writing of the
manuscript.

REFERENCES

1. Stevens JR, Noyes HA, \& Gibson WC (1999) The ancient and divergent
origins of the human pathogenic trypanosomes, Trypanosoma brucei and T.
cruzi. \emph{Parasitology} 118:107-116.

2. Larremore DB\emph{, et al.} (2015) Ape parasite origins of human
malaria virulence genes. \emph{Nature Communications} 6(1).

3. McFall-Ngai M\emph{, et al.} (2013) Animals in a bacterial world, a
new imperative for the life sciences. \emph{Proc Natl Acad Sci U S
A}110(9):3229-3236.

4. Kwiatkowski DP (2005) How malaria has affected the human genome and
what human genetics can teach us about malaria. \emph{Am. J. Hum.
Genet.} 77:171-190.

5. Penn D \& Potts WK (1998) How do major histocompatibility complex
genes influence odor and mating preferences? \emph{Advances in
Immunology} 69:411-436.

6. Busula AO, Verhulst NO, Bousema T, Takken W, \& de Boer JG (2017)
Mechanisms of Plasmodium-Enhanced Attraction of Mosquito
Vectors.\emph{Trends in Parasitology} 33(12):961-973.

7. Busula AO, Takken W, JG DEB, Mukabana WR, \& Verhulst NO (2017)
Variation in host preferences of malaria mosquitoes is mediated by skin
bacterial volatiles. \emph{Med Vet Entomol} 31(3):320-326.

8. Dittmar K, Morse, Solo F., Dick, Carl W., Patterson, Bruce D. (2015)
Bat fly evolution from the Eocene to the present (Hippoboscoidea,
Streblidae and Nycteribiidae). \emph{Parasite Diversity and
Diversification: Evolutionary Ecology Meets Phylogenetics} , ed S.
Morand BRK, D. T. J. Littlewood (Cambridge University Press, Cambridge,
U. K.).

9. Dick CW, Patterson, B. D. (2006) \emph{Micromammals and
Macroparasites: From Evolutionary Ecology to Management} (Springer, Kato
Bunmeisha, Japan).

10. Schaer J\emph{, et al.} (2013) High diversity of West African bat
malaria parasites and a tight link with rodent Plasmodium
taxa.\emph{Proceedings of the National Academy of Sciences}
100:17415-17419.

11. Lutz HL\emph{, et al.} (2016) Diverse sampling of East African
haemosporidians reveals chiropteran origin of malaria parasites in
primates and rodents. \emph{Mol Phylogenet Evol} 99:7-15.

12. Bausch DG, Schwarz, L. (2014) Outbreak of Ebola virus disease in
Guinea: where ecology meets economy. \emph{PLoS Negl Trop
Dis}8(7):e3056.

13. Hayman DT, Bowen, R.A., Cryan, P.M., McCracken, G.F., O'Shea, T.J.,
Peel, A.J., Gilbert, A., Webb, C.T., Wood, J.L. (2013) Ecoogy of
zoonotic infectious diseases in bats: current knowledge and future
directions. \emph{Zoonoses and Public Health} 60:2-21.

14. Billeter SA\emph{, et al.} (2012) Bartonella species in bat flies
(Diptera: Nycteribiidae) from western Africa.
\emph{Parasitology}139(3):324-329.

15. Sawatsky B RC, Weingartl HM, Czubet M. (2008) Hendra and Nipah
Virus. \emph{Animal Viruses: Molecular Biology.} , (Caister Academic
Press, Norfolk, UK).

16. Towner JS\emph{, et al.} (2009) Isolation of genetically diverse
Marburg viruses from Egyptian fruit bats. \emph{PLoS
Pathog}5(7):e1000536.

17. Olival KJ \& Hayman DT (2014) Filoviruses in bats: current knowledge
and future directions. \emph{Viruses} 6(4):1759-1788.

18. Amman BR\emph{, et al.} (2015) A Recently Discovered Pathogenic
Paramyxovirus, Sosuga Virus, is Present in Rousettus aegyptiacus Fruit
Bats at Multiple Locations in Uganda. \emph{J Wildl Dis} 51(3):774-779.

19. Goldberg TL, Bennett AJ, Kityo R, Kuhn JH, \& Chapman CA (2017)
Kanyawara Virus: A Novel Rhabdovirus Infecting Newly Discovered
Nycteribiid Bat Flies Infesting Previously Unknown Pteropodid Bats in
Uganda. \emph{Scientific Reports} 7(1).

20. Bennett AJ, Paskey AC, Kuhn JH, Bishop-Lilly KA, \& Goldberg TL
(2020) Diversity, Transmission, and Cophylogeny of Ledanteviruses
(Rhabdoviridae: Ledantevirus) and Nycteribiid Bat Flies Parasitizing
Angolan Soft-Furred Fruit Bats in Bundibugyo District,
Uganda.\emph{Microorganisms} 8(5):750.

21. Dick CW \& Patterson BD (2007) Against all odds: Explaining high
host specificity in dispersal-prone parasites. \emph{International
Journal for Parasitology} 37:871-876.

22. Dick CW \& Dittmar K (2014) Parasitic bat flies (Diptera: Streblidae
and Nycteribiidae): host specificity and potential as vectors.\emph{Bats
(Chiroptera) as Vecotrs of Diseases and Parasities,}Parasitology
Research Monographs, eds Klimpel S \& Melhorn H (Springer-Verlag, Berlin
Heidelberg), Vol 5, pp 131-155.

23. Urbieta GL, Torres JM, Anjos EACD, Carvalho CME, \& Graciolli G
(2019) Parasitism of Bat Flies (Nycteribiidae and Streblidae) on Bats in
Urban Environments: Lower Prevalence, Infracommunities, and
Specificity.\emph{Acta Chiropterologica} 20(2):511.

24. Szentiványi T, Christe P, \& Glaizot O (2019) Bat Flies and Their
Microparasites: Current Knowledge and Distribution. \emph{Frontiers in
Veterinary Science} 6.

25. Dick CW \& Patterson BD (2006) Obligate ectoparasites of
bats.\emph{Micromammals and Macroparasites} , eds Morand S, Krasnov B,
\& Poulin R (Springer, Tokyo), pp 179-194.

26. Verhulst NO, Takken W, Dicke M, Schraa G, \& Smallegange RC (2010)
Chemical ecology of interactions between human skin microbiota and
mosquitoes. \emph{FEMS Microbiol Ecol} 74(1):1-9.

27. Verhulst NO\emph{, et al.} (2011) Composition of human skin
microbiota affects attractiveness to malaria mosquitoes. \emph{PLoS
One}6(12):e28991.

28. Verhulst NO\emph{, et al.} (2009) Cultured skin microbiota attracts
malaria mosquitoes. \emph{Malar J} 8:302.

29. Soni N, Chahda JS, \& Carlson JR (2019) Odor coding in the antenna
of the tsetse flyGlossina morsitans. \emph{Proceedings of the National
Academy of Sciences} 116(28):14300-14308.

30. Klowden MJ (2013) \emph{Physiological Systems in Insects} (Academic
Press, London) Third Edition Ed.

31. Ezenwa VO, Gerardo NM, Inouye DW, Medina M, \& Xavier JB (2012)
Animal behavior and the microbiome. \emph{Science} 338(6104):198-199.

32. Se Jin Song JGS, Frédéric Delsuc, Jessica Metcalf, Katherine Amato,
Michael W. Taylor, Florent Mazel,, Holly L. Lutz KW, Gary R. Graves,
Gregory Humphrey, Jack A. Gilbert, Shannon J. Hackett,, Kevin P. White
HRS, Sarah M. Kurtis,mJack Withrow, Thomas Braile, Matthew Miller,,
Kevin G. McCracken JMM, Vanessa O. Ezenwa, Allison Williams, Jessica M.
Blanton,, \& Valerie J. McKenzie RK (2020) Comparative analyses of
vertebrate gut microbiomes reveal convergence between birds and
bats.\emph{mBio} 11(1):e02901-02919.

33. Lutz HL\emph{, et al.} (2019) Ecology and host identity outweigh
evolutionary history in shaping the bat microbiome.
\emph{mSystems}4(6):e00511-00519.

34. Bolyen E\emph{, et al.} (2019) Reproducible, interactive, scalable
and extensible microbiome data science using QIIME 2. \emph{Nature
Biotechnology} 37(8):852-857.

35. Team RC (2019) R: A language and environment for statistical
computing (R Foundation for Statistical Computing, Vienna, Austria).

36. Oksanen JB, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn,
D., Minchin, P. R., O'Hara, R. B., Simpson, G. L., Solymos, P., Stevens,
M. H. H., Szoecs, E., Wagner, H. (2018) vegan: Community Ecology
Package), 2.5-1.

37. McMurdie PJ \& Holmes S (2013) phyloseq: an R package for
reproducible interactive analysis and graphics of microbiome census
data. \emph{PLoS One} 8(4):e61217.

38. Wickham H, François R, Henry L, \& Müller K (2018) dplyr: A Grammar
of Data Manipulation), R package version 0.7.6.

39. Wickham H (2016) \emph{ggplot2: Elegant Graphics for Data
Analysis}(Springer-Verlag, New York, NY).

40. Morton JT\emph{, et al.} (2019) Establishing microbial composition
measurement standards with reference frames. \emph{Nature
Communications} 10(1).

41. Fedarko MW\emph{, et al.} (2020) Visualizing~'omic feature rankings
and log-ratios using Qurro. \emph{NAR Genomics and Bioinformatics} 2(2).

42. Kurtz ZD\emph{, et al.} (2015) Sparse and compositionally robust
inference of microbial ecological networks. \emph{PLoS Comput
Biol}11(5):e1004226.

43. Cardona C (2017) CAVNet: Creation Analysis and Visualization of
Networks).

44. Csardi G, Nepusz, T. (2006) The Igraph Software Package for Complex
Network Research. \emph{InterJournal, Complex Systems} 1695.

45. Verhulst NO\emph{, et al.} (2010) Differential Attraction of Malaria
Mosquitoes to Volatile Blends Produced by Human Skin Bacteria.\emph{PLoS
ONE} 5(12):e15829.

46. Tabares M\emph{, et al.} (2018) Behavioral responses of Rhodnius
prolixus to volatile organic compounds released in vitro by bacteria
isolated from human facial skin. \emph{PLOS Neglected Tropical
Diseases}12(4):e0006423.

47. Wiegmann BM\emph{, et al.} (2011) Episodic radiations in the fly
tree of life. \emph{PNAS} 108(14):5690-5695.

48. Robinson A\emph{, et al.} (2018) Plasmodium-associated changes in
human odor attract mosquitoes. \emph{Proc Natl Acad Sci U S
A}115(18):E4209-E4218.

49. Dinparast Djadid N\emph{, et al.} (2011) Identification of the
Midgut Microbiota of An. stephensi and An. maculipennis for Their
Application as a Paratransgenic Tool against Malaria. \emph{PLoS
ONE}6(12):e28484.

50. Dutkiewicz J, Mackiewicz B, Lemieszek MK, Golec M, \& Milanowski J
(2016) \textless{}i\textgreater{}Pantoea
agglomerans\textless{}/i\textgreater{}: a mysterious bacterium of evil
and good. Part IV. Beneficial effects. \emph{Annals of Agricultural and
Environmental Medicine} 23(2):206-222.

51. De Moraes CM\emph{, et al.} (2014) Malaria-induced changes in host
odors enhance mosquito attraction. \emph{Proc Natl Acad Sci U S
A}111(30):11079-11084.

52. de Boer JG\emph{, et al.} (2017) Odours of Plasmodium
falciparum-infected participants influence mosquito-host
interactions.\emph{Sci Rep} 7(1):9283.

53. Kelly A. Speer TT, Alexis Brown, et al. (Parasite species identity
and local community diversity mediate effects of habitat fragmentation
on bacterial microbiomes. \emph{Authorea} .

54. Overal WL (1980) Host-relations of the batfly Megistopoda aranea
(Diptera: Streblidae) in Panama. 52: 1--20. \emph{University of Kansas
Science Bulletin} 52:1-20.

55. Avena CV\emph{, et al.} (2016) Deconstructing the Bat Skin
Microbiome: Influences of the Host and the Environment. \emph{Front
Microbiol} 7:1753.

56. Kolodny O\emph{, et al.} (2019) Coordinated change at the colony
level in fruit bat fur microbiomes through time. \emph{Nat Ecol
Evol}3(1):116-124.

57. Lewis SE (1995) Roost Fidelity of Bats: A Review. \emph{Journal of
Mammalogy} 76(2):481-496.

FIGURE LEGENDS

Figure 1. Diagram of host-parasite associations and sampling. A) Bat
host, with orange circles indicating locations from which skin and fur
samples were collected; dashed circle indicates interscapular sampling
region on dorsal side of bat. B) hippoboscoid bat fly, C) bat red blood
cells infected by malarial haemosporidian parasites. Illustration by
Madison Erin Mayfield @MEMIllustration.

Figure 2. Alphadiversity among the skin, oral, and gut microbiota of
bats grouped by family and ectoparasite status.

Figure 3. Ranked differential features associated with skin of
ectoparasitized bats grouped by host family; full range of ranked
features (including negatively associated features) shown in gray inset.
Highlighted features include those observed in all four bat families
(red), those observed in three of the four bat families (orange), and
features belonging to the genus \emph{Mycoplasma} that were also shared
by three of four bat families. Bars of highlighted features have been
enlarged for clarity.

Figure 4. Network characteristics of the skin microbiome among
ectoparasitized and non-parasitized bats grouped by host family,
including A) cluster size density (* indicates \emph{p} -value
\textless{} 0.05, Mann-Whitney-Wilcoxon rank sum test), B) degree
distribution (* indicates \emph{p} -value \textless{} 0.05,
\emph{t-}test), and C) Fruchterman-Reingold network topology colored by
individual network clusters.

Figure 5. Ranked differential features associated with oral of malarial
miniopterid bats. Highlighted are the two highest-ranked features
associated with presence of malarial parasites (\emph{P.
agglomerans}(red), \emph{Acinetobacter} sp. (orange)), and the most
abundant features associated with absence of malarial parasitism (ASVs
in the Pasteurellaceae family (dark green)).

DATA AVAILABILITY STATEMENT

All 16S rRNA sequence data are publicly available via the QIITA platform
(https://qiita.ucsd.edu) under the study identifier (ID) 11815 and the
European Bioinformatics Institute (EBI) under accession number
PRJEB32520. Code for sequence processing and analyses can be viewed at
https://github.com/hollylutz/BatMP. Host and parasite vouchers are
accessioned at the Field Museum of Natural History (Chicago, IL, USA).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure1/Figure1}
\caption{{Diagram of host-parasite associations and sampling. A) Bat host, with
orange circles indicating locations from which skin and fur samples were
collected; dashed circle indicates interscapular sampling region on
dorsal side of bat. B) hippoboscoid bat fly, C) bat red blood cells
infected by malarial haemosporidian parasites.~Illustration by Madison
Erin Mayfield @MEMIllustration.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure11/Figure2}
\caption{{\begin{quote}
Alphadiversity among the skin, oral, and gut microbiota of bats grouped
by family and ectoparasite status.
{\label{181100}}
\end{quote}%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure3/Figure3}
\caption{{Ranked differential features associated with skin of ectoparasitized
bats grouped by host family; full range of ranked features (including
negatively associated features) shown in gray inset. Highlighted
features include those observed in all four bat families (red), those
observed in three of the four bat families (orange), and features
belonging to the genus~\emph{Mycoplasma} that were also shared by three
of four bat families. Bars of highlighted features have been enlarged
for clarity.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure4-redux/Figure4-redux}
\caption{{Network characteristics of the skin microbiome among ectoparasitized and
non-parasitized bats grouped by host family, including A) cluster size
density (* indicates \emph{p} -value \textless{} 0.05,
Mann-Whitney-Wilcoxon rank sum test), B) degree distribution (*
indicates \emph{p} -value \textless{} 0.05, \emph{t-}test), and C)
Fruchterman-Reingold network topology colored by individual network
clusters
{\label{941160}}%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure5/Figure5}
\caption{{Ranked differential features associated with oral of malarial
miniopterid bats. Highlighted are the two highest-ranked features
associated with presence of malarial parasites (\emph{P.
agglomerans}(red), \emph{Acinetobacter} sp. (orange)), and the most
abundant features associated with absence of malarial parasitism (ASVs
in the Pasteurellaceae family (dark green)).%
}}
\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

