# Genomic evidence for flies as carriers of zoonotic pathogens on dairy farms

Andrew Sommer, Joseph Skarlupka, Serafino Teseo, Saria Otani, Garret Suen, Kerri Coon, Panagiotis Sapountzis

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Andrew Sommer, Joseph Skarlupka, Serafino Teseo, Saria Otani, Garret Suen, et al. Genomic evidence for flies as carriers of zoonotic pathogens on dairy farms. npj Biofilms and Microbiomes, 2025, 11 (1), pp.111. 10.1038/s41522-025-00685-y. hal-05163033

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Submitted on 15 Jul 2025

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# Genomic evidence for flies as carriers of zoonotic pathogens on dairy farms

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Andrew J. Sommer<sup>1,2</sup>, Joseph H. Skarlupka<sup>1,2</sup>, Serafino Teseo<sup>3,4</sup>, Saria Otani<sup>5</sup>, Garret Suen<sup>1</sup>

Kerri L. Coon<sup>1</sup> & Panagiotis Sapountzis<sup>3</sup>

Dairy farms are major reservoirs of zoonotic bacterial pathogens, which harbor antimicrobial resistance genes (ARGs), and raise critical questions about their dissemination on and off the farm environment. Here, we investigated the role of coprophagous muscid flies (Diptera: Muscidae) as carriers of zoonotic pathogens and antimicrobial resistance. We collected cow manure and flies on a dairy farm and used shotgun metagenomics to identify the presence of clinically relevant bacteria, virulence factors, and ARGs in both environments. Our results reveal that, although the fly microbiome is largely composed of manure-associated taxa, they also harbor specific insect-associated bacteria, which may be involved in nutrient provisioning to the host. Furthermore, we identified shared ARGs, virulence factors, and zoonotic pathogens enriched within the fly gastrointestinal tract (GIT). Our study illustrates the potential flow of pathogenic microorganisms from manure to coprophagous flies, suggesting that flies may pose an important zoonotic threat on dairy farms.

Emerging infectious diseases (EID), the majority of which are zoonotic in origin, represent a significant threat to the global economy and food system<fnref n="1" />. Dairy farms and other cattle livestock systems are major reservoirs of economically significant zoonotic pathogens, including *Escherichia coli* O157, *Salmonella enterica*, *Listeria monocytogenes*, and *Coxiella burnetii*²,³. Antibiotic usage and the horizontal transfer of ARGs can contribute to the emergence of antimicrobial resistant pathogens on farms⁴, further highlighting the need to better understand the ecology of agricultural microbiomes. On dairy farms, past studies have focused primarily on the direct transmission of microorganisms from cattle to farm workers and through the food system²,⁵. In contrast, far less is known about the interactions between pathogenic microbes and other animals cohabiting on livestock facilities.

Dairy farms support a large community of synanthropic pests, including birds, rodents, and insects, which regularly interact with both cattle and farm workers. Muscid flies (Diptera: Muscidae) are globally ubiquitous, coprophagous pests that include members of the genera *Musca* (house and face flies) and *Neomyia* (false greenbottle flies). Large fly populations are sustained by a near constant supply of manure – a protein and carbohydrate rich source of nutrition for both immature fly larvae and adult flies⁶⁻⁸. Bovine manure also serves as an important source of animal-borne pathogens, which may colonize and proliferate in the fly digestive tract following ingestion⁹,¹⁰. These bacteria can be disseminated by flies via

regurgitation and defecation, increasing the potential transmission risk of pathogenic microorganisms throughout the environment¹⁰⁻¹².

Prior studies utilizing 16S rRNA amplicon sequencing and culture-dependent methodologies have shown that flies commonly harbor medi-cally important bacterial species including some within the *Staphylococcus* and *Enterobacteriaceae*¹³⁻¹⁹. Flies are also carriers of antimicrobial resistant bacteria, which often carry extrachromosomal ARGs on plasmids, trans-posable elements, or other mobile genetic elements²⁰,²¹. This is of great concern given the continuous usage of antibiotics to treat bovine diseases such as mastitis and metritis²²,²³, resulting in selective pressures that main-tain ARGs within the bovine intestinal microbiome, which, by extension, persist in bovine feces.

Studies using 16S rRNA gene amplicon sequencing, while informative, cannot be used to identify specific shared transmitted genetic features (ARGs, virulence factors, etc.) or for strain level analyses¹³⁻¹⁷. Despite the known epidemiological significance, only a handful of studies have exam-ined the potential for transfer of genomic features and/or bacterial strains between flies and agricultural environments²⁴⁻²⁶, but were either limited to culture-based approaches and Whole Genome Sequencing (WGS) of iso-lates or used metagenomic approaches only on flies and not on environ-mental or manure samples. However, approaches such as shotgun metagenomic sequencing allow for the assembly of bacterial community genomes, which can be used to identify the distribution of functional genetic

<footnote n="1">¹Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA. ²Microbiology Doctoral Training Program, University of Wisconsin-Madison, Madison, WI, USA. ³INRAE-UCA, UMR0454 MEDIS, Clermont-Ferrand, France. ⁴School of Biological Sciences, Nanyang Technological University, Singapore, Singapore. ⁵National Food Institute, Technical University of Denmark, Kongens Lyngby, Denmark. ☒e-mail: kerri.coon@wisc.edu; panagiotis.sapountzis@inrae.fr</footnote>
features like virulence factors and ARGs, in addition to mobile genetic elements<sup>27-29</sup>.

Previously, we performed a metagenomic analysis on individual dairy
cow fecal samples to construct a manure-associated genome database and
characterized both microbial diversity and virulence factors across this
environment³⁰. Here, we expand upon our work by analyzing the meta-
genomes of muscid flies feeding on these same manure samples to explore
the transmission of microorganisms through the farm environment. We
hypothesize that coprophagous muscid flies can acquire fecal-associated
bacteria and their associated genomic features (virulence factors and ARGs).
Our results show that many highly abundant metagenome-assembled
genomes (MAGs) and reference bacterial genomes in the fly GIT micro-
biome can be traced to manure, and that pathogenic microbes (Coxiella,
E. coli pathotypes) and virulence genes may persist in the fly gut. We further
identified insect-associated microbes (Frischella, Entomomonas, Apilacto-
bacillus) in the GIT, which we hypothesize may provide mutualistic benefits
to the fly. This study is the first to offer functional predictions using
metagenomic sequencing data from environmental reservoirs (cattle
manure) and the Neomyia fly carriers interacting with them in real-time.

Results

Sample collection, metagenome sequencing, and construction
of fly-derived MAGs

A total of 29 flies were collected from a dairy farm in Saint-Genès-Cham-
panelle, France over the course of two consecutive days. Metagenomic
sequencing of the dissected GITs from these flies generated a total of 3.52
billion reads, representing ~528 Gbp of sequence data that averaged
18.2 ± 2.4 Gbp per sample. The metagenomes and their associated
sequencing metrics for the 48 cow manure samples also used in this study
have been previously reported³⁰.

Shotgun metagenomic sequences from our fly GIT microbiomes were assembled into >37 million contigs. Taxonomic classification of these contigs using the NCBI's non-redundant protein database showed that the vast majority were not represented in publicly available databases (ca. 78%), while most classifiable contigs belonged to Bacteria (>15%) and Eukaryotes (>5%), with minor contributions from Archaea (<0.4%) and Viruses (<0.5%) (Supplementary Table 1). Of note, 96% of the eukaryotic contigs belonged to the Arthropoda and Euglenozoa, with the former being the most abundant (>71% mean abundance) in almost every sample and the latter ranging from 0 to 56% per sample. A total of 506 fly GIT MAGs were constructed, of which 42 MAGs passed our filtering threshold of >85% completeness and <5% contamination (Supplementary Table 2). These 42 MAGs were resolved to 15 different bacterial genera, which included *Pre-votella* (*n* = 7), *Frischella* (*n* = 6), *Fibrobacter* (*n* = 5), and *Rumminococcus* (*n* = 3) as the most prevalent (Supplementary Table 3). We constructed a phylogeny by concatenating all shared orthologs between the 42 fly GIT MAGs, closely related manure-derived MAGs³⁰, and closely related RefSeq MAGs. This phylogenetic analysis revealed the presence of closely related MAGs shared between flies and manure (Supplementary Fig. 1).

Fly genotyping

The vast majority of collected flies were morphologically identified as
belonging to the common green fly, which consists of several greenbottle fly
species that are abundant in farms across Western Europe<sup>8,31</sup>. We genotyped
each fly by identifying the mitochondrial cytochrome oxidase subunit I
(COI) gene in their respective metagenome and comparing it against the
published mitochondrial COI genes for *Musca domestica*, *Myospila medi-
tabunda*, and *Neomyia cornicina*. A total of 27 out of 29 samples were found
to contain near-complete dipteran mitochondrial COI genes (2287 bp) that
matched with >99% sequence identity to the published mitochondrial COI
gene from *N. cornicina* (MW592695.1), which belongs to the *Muscidae*
family. For the two samples that did not yield near-complete mitochondrial
COI genes, a partial sequence was obtained that matched most closely to the
reported mitochondrial COI gene for *My. meditabunda* (KJ510632 &
FJ025642), which also belongs to the *Muscidae*. All sequences and their

closest matches were then aligned and used to construct a maximum like-
lihood phylogenetic tree (Supplementary Fig. 2). Of note, 26 of the 29
mitochondrial COI gene sequences formed a single clade with *N. cornicina*
mitochondrial COI gene sequences reported from Europe (Belgium, France,
Poland, and Portugal; KF919034, KU932144, MN868909 respectively) and
China³². Of the other three sequences, one formed a sister clade with several
other published *N. cornicina* sequences, while the other two formed a distant
clade with sequences from *My. meditabunda*.

Fly GIT and cow manure microbiome structure

We next sought to characterize fly GIT bacterial communities in relation to
manure to identify the potential for microbial transmission in the dairy
environment. To accomplish this, we performed a read mapping recruitment
analysis of the 29 fly GIT and the 48 manure metagenomes against a reference
MAG database containing the 42 MAGs derived from our fly GIT meta-
genomes and the 2114 MAGs derived from cow manure³⁰. For each sample,
we determined the CLR transformed abundances of MAGs across each
bacterial order. Notably, the bacterial orders Bacteroidales, Burkholderiales,
and Enterobacterales were relatively abundant across the fly microbiome
(Supplementary Fig. 3). Bacteroidales was also one of the most abundant
bacterial orders across manure samples (Supplementary Fig. 4). Beta-
diversity analysis (Achiton distance) further revealed clustering of bacterial
order abundance by sample type, indicating that the fly GIT community
structure is distinct from that of cow manure (Supplementary Fig. 5).

We next determined the 50 most abundant MAGs (CLR transformed
counts) in both the fly GIT and cow manure samples to identify the potential
microbial flow through the environment. We found that 34 MAGs from 17
different genera were among the 50 most common MAGs shared between
both sample types, suggesting that the potential transfer of these MAGs
from manure to flies via feeding is common (Fig. 1). To identify potentially
enriched MAGs in the fly GIT and manure samples, we performed an
*indicspecies* analysis, which revealed a total of 41 MAGs enriched in manure
samples and 21 MAGs enriched in the fly GIT (Supplementary Fig. 6;
Supplementary Table 4). This included highly enriched fly GIT MAGs
belonging to the genera *Frischella*, *Enterococcus*, *Entomomonas*, *Dysgono-
monas*, *Flavobacterium*, *Prevotella*, *Apilactobacillus*, and *Acinetobacter*.
In contrast, the most enriched cow manure MAGs belonged to the Lachnos-
piraceae, *Selenomonas*, *Bifidobacterium*, and *Flintibacter*.

Using data from both the CLR transformed counts of highly
abundant MAGs (Fig. 1) and our enrichment analysis (Supplementary
Fig. 6), we selected a set of MAGs representative of the fly GIT, cow
manure, and taxa shared between the two groups. We functionally
annotated these MAGs using the G database to determine KEGG
orthologs (KOs) both unique to and common between groups
(Fig. 2A). We next analyzed three intersections of KOs: KOs shared
between the fly GIT and the common group, unique KOs to the fly GIT,
and unique KOs to cow manure (Supplementary Table 5). Analysis of
each KO's associated Clusters of Orthologous Group (COGs) anno-
tations showed that KOs related to transcription, cell motility and
trafficking, secretion and vesicular transport (COG groups K, N & U)
were significantly higher (Fisher Exact Test, *p* < 0.05) in the KOs
unique to the fly GIT, relative to either common or unique to manure
groups (Fig. 2B). We also found the intersection of the fly and common
groups to have significantly higher proportion of housekeeping KOs
involved in cell division (D), translation (J), replication (L), and
posttranscriptional modification (O) as compared to the unique fly and
manure groups. We further identified complete metabolic pathways
encoded within the MAGs unique to and common between the fly GIT
and cow manure microbiomes (Fig. 2C). We found that a portion of the
fly group MAGs, which included the highly enriched *Frischella* and
*Entomomonas* MAGs, encoded pathways related to the metabolism of
cofactors and vitamins (Lipoic acid biosynthesis and Pyridoxal-P
biosynthesis). We also identified several pathways involved in amino
acid biosynthesis, energy metabolism, carbohydrate metabolism, and
drug resistance, which were unique to the fly-specific MAGs (Fig. 2C).
A.

<figure><img src="image_4.png" /><figcaption>**Fig. 1** | Comparison of highly abundant MAGs identified from fly and manure samples. Chord diagrams depict the 50 most abundant MAGs found in flies (A) and manure (B) with their corresponding abundances in both manure (blue arc) and fly GITs (red arc) shown by connecting lines/chords. The thickness of each line/chord is proportional to the corresponding MAG's abundance, which was calculated as the</figcaption></figure>

B.

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centered log ratio normalized proportion of the total counts successfully mapped to the MAG divided by the estimated number of genomes in each sample (see methods). MAGs with <20% prevalence across all samples were removed from the dataset prior to CLR transformation and downstream analysis.

## Comparison of virulence factors between the fly and manure microbiomes

We examined virulence factors present in the fly GIT and cow manure metagenomes by mapping reads against the VFdb database. Our analysis revealed a total of 1934 virulence factors shared between the fly GIT and cow manure microbiomes (Fig. 3A). We repeated the same comparison for those VFs present in at least 50% of each group and found a total of 43 VFs shared between the fly GIT and cow manure. Shared VFs were primarily related to adherence and immune modulation (encoding for surface antigens and capsules). Notably, we also identified a gene coding for a Shiga toxin (stx1vB, VFG043752) that was present in at least 50% of fly and cow manure samples, with an additional Shiga toxin (stxB, VFG001829) present in 50% of the cow manure samples. We also identified 18 VFs associated with Coxiella present in at least 50% of the fly samples (Supplementary Table 7). We then performed an additional filtering step that retained VF genes for which mapped reads covered at least 90% of the gene length and were also present in at least 50% of the samples. Using these criteria, we identified seven VFs associated with the fly GIT microbiome, which were classified as belonging to Escherichia coli and Coxiella burnetii and included six VFs related to secretion and one to adherence (stgB) (Supplementary Table 7). The CLR transformed counts suggested that the abundance of espL4, espX5, CBUK_RS01150, and coxFIC1 was significantly higher (Wilcoxon test, *p* < 0.05) across fly GIT samples as compared to cow manure (Fig. 3B).

## Comparison of antibiotic resistance genes between the fly and manure microbiomes

We similarly identified ARGs across our fly GIT and cow manure metagenomes through read mapping against the ResFinder Database. We found 86 ARGs common across all samples and, after removing ARGs present in less than 50% of the groups, only 40 ARGs were retained (Fig. 3A). Using a method similar to our VF analysis, we examined how many ARGs would be retained after filtering for those genes present in ≥50% of the samples with reads mapping to ≥90% of the gene length (Fig. 3A). We identified 18 ARGs common between both groups: 1 unique to the fly GIT and 13 unique to cow manure

(Supplementary Table 7). We then examined the CLR transformed abundances of these 32 ARGs (Fig. 3C), which notably showed higher relative abundances (Wilcoxon test, *p* < 0.05) of a beta-lactam blaOXA gene (ACWG01000053), three tetracycline resistance genes, and an aminoglycoside resistance gene (KF864551) in the fly GIT as compared to cow manure. Conversely, we found CLR transformed abundances of a macrolide resistance gene (AY928180) and three other tetracycline genes to be significantly higher in manure samples (Wilcoxon test, *p* < 0.05). We then compiled the antimicrobial usage (AMU) records on the farm from the last 5 years to examine the potential correlation of ARGs discovered with antibiotic administration. We found that macrolides, aminoglycosides, tetracyclines, and beta-lactam antibiotics were all commonly administered to sick animals and ARGs linked to these antibiotics were commonly found in manure and fly metagenomes (Fig. 3, Supplementary Table 7). However, formal statistical evaluation showed no significant correlation between antibiotic administration and ARG presence (co-inertia *p* > 0.05).

## Tracing fly-manure interactions and identification of pathogens in the dairy barn environment

As noted in our sampling approach, flies were collected standing on or hovering above cow manure, which urged us to examine whether we could identify traces of the bovine COI gene in the fly GIT. We found single alleles of the bovine COI gene in 23/29 fly samples, which we presume is correlated to the most recent manure from which they fed. Comparison of these sequences showed that recovered *Bos taurus* COI genes from flies formed three distinct clusters (Supplementary Fig. 7). We then used these data to identify potential zoonotic pathogens shared between fly GIT and cow manure metagenomes belonging to the same COI cluster, which would suggest the potential acquisition of these pathogens from cow manure pools. We identified 47 bacterial genomes and plasmids associated with VF genes that were shared between our fly GIT and cow manure samples with at least 90% coverage (Fig. 3). Sequencing reads were then mapped to the retrieved full genome (chromosomal and plasmid) sequences and were resolved to 8 pathogen genomes that were found in at least one fly GIT sample and one
<figure><img src="image_6.png" /><figcaption>**Fig. 2** | Functional analysis of the most abundant MAGs. A Comparison of shared and unique KEGG orthologs (KOs) in the 21 representative fly-selected, manure-selected, and common MAGs. Genus identification of each MAG is shown below the Venn-diagram, with parentheses indicating when multiple MAGs were assigned to the same genus. Daggers (†) indicate MAGs without a specific genus identification. B Bar graph showing the distribution of clusters of orthologous groups of proteins (COGs) associated with the common transmissible KOs (top bar, orange), unique KOs to flies (middle bar, red), and unique KOs to manure (bottom bar, blue). Lines</figcaption></figure>

indicate comparisons where the proportion of counts is significantly different (Fisher's exact test, *p* < 0.05). C Overview of complete KEGG metabolic pathways in the fly-selected, manure-selected, and common MAG groups. The ternary plot shows the relative proportion of MAGs in each group encoding complete metabolic pathways. The position of the pathway on the ternary plot represents the prevalence in each group, with pathways equally common between groups in the center. The color of each point indicates the metabolic class the pathway belongs to.

cow manure sample with >10% total genome coverage. These pathogens included 4 *E. coli*, 2 *Shigella*, and 2 *Coixella* genomes, which were shared between the fly GIT and cow manure microbiomes. When comparing the carriage of each of these pathogen genomes to the bovine mitochondrial COI gene (Fig. 4), we found specific instances where both fly GIT and cow manure metagenomes harboring highly similar bovine COI genes also shared the same pathogens. We found that, not only were these pathogens more commonly identified in the fly GIT samples, but that they often could be mapped with a higher coverage and at a greater relative abundance as compared to their cow manure counterparts (Supplementary Fig. 8).

Discussion

In this study, we used deep metagenomic sequencing to investigate the microbiome of muscid flies in relation to bovine manure on a pasture-based dairy farm in France. Bovine manure represents a major reservoir of zoonotic pathogens, including *E. coli* O157, *Salmonella* spp., and *C. burnetti*²,³; however, few studies have attempted to trace the dissemination of manure-associated microbes through the dairy farm environment. As the microbiota of dipteran insects is often derived from environmental bacteria³,³,⁴, we hypothesized that coprophagous flies could acquire manure-associated

microbes and pathogens. Our analysis identified genomic signatures (VFs, ARGs, pathogen genomes) shared between the fly GIT and the cow manure microbiome, which suggests the potential for bacteria to passage from cow manure to flies. Collectively, our results indicate that *Neomyia* flies may serve as underrecognized carriers of zoonotic pathogens and may contribute to disease transmission on dairy farms.

Dairy farms support large populations of ecologically diverse fly taxa, which regularly interact with cattle hosts, farm workers, and bovine manure. While most synanthropic muscid flies utilize manure as a preferred oviposition site, adult flies will feed on different nutritional sources including manure (*Musca domestica*, *Neomyia* spp.), cattle blood (*Stomoxys* spp., *Haematobia* spp.), or cattle eye secretions (*Musca autumnalis*)³⁵. To date, most studies on the muscid fly microbiota have been focused on *M. domestica*, whose gut microbiome is often dominated by Lactobacillales, Enterobacterales, and Bacillales¹⁶⁻¹⁹, and has been shown to participate in the carriage of zoonotic pathogens in both field and laboratory settings¹⁰,¹²,³⁶⁻³⁸. Recent 16S rRNA gene amplicon sequencing studies of *M. domestica* and *Stomoxys* flies collected from livestock facilities have shown that the majority of the fly microbiota can be identified in nearby manure samples¹³,¹⁶,¹⁹, indicating that the microbiota of flies is shaped by interactions
<figure><img src="image_7.png" /><figcaption>**Fig. 3** | Distribution of virulence factors (VFs) and antimicrobial resistance genes (ARGs) in manure (blue) and fly (red) metagenomes. A Venn diagram showing in descending order all common and unique virulence (left) and antimicrobial resistance (right) features, features that were present in at least 50% of fly and cow samples (50% prevalence), and features corresponding to genes for which the reads covered ≥90% of the gene length and were present in at least half of the samples (≥50% prevalence & ≥90% coverage). Left circle (blue) corresponds to the manure microbiomes; right circle (red) corresponds to the fly GIT microbiomes. B Box plot</figcaption></figure>

showing the relative abundances (centered normalized log ratios) of 6 VFs with ≥50% prevalence and ≥90% coverage in fly samples. C Box plot showing the relative abundance of corresponding ARGs with at least 50% prevalence and 90% coverage in either fly or manure samples. Top bars (red) depict fly abundances; bottom bars (blue) depict manure abundances. VFs were identified through mapping against the VFdb and ARGs were identified through mapping against the Resfinder database. Asterisks indicate comparisons with a significantly different abundance between fly and manure samples (*p* < 0.05, Wilcoxon signed rank test).

with the animal feces or other elements of the local environment. Likewise, whole genome sequencing of *E. coli* isolates has revealed the presence of highly similar bacterial strains shared between *M. domestica* and animal manure samples²⁴,²⁵. In this study, we contribute to the growing body of fly-microbe literature through parallel metagenomic sequencing of flies and manure on a pasture based dairy farm. Nearly all flies in this study were molecularly identified as *Neomyia* flies, which although common to pasture based dairy farms in Europe⁸,³⁹, remain highly understudied in comparison to other synanthropic muscid flies. Our study represents, to our knowledge, the first culture-independent analysis of the *Neomyia* fly gastrointestinal microbiota. Comparing the taxonomic and functional characteristics of the fly microbiome to bovine manure revealed commonly shared MAGs between fly and manure samples, which included bacterial taxa (*Fibrobacter*, *Sutteralla*, and various Bacteroidales) associated with the bovine GIT microbiome³⁰. While termites and other social insects harbor specialized lineages of cellulose degrading *Fibrobacter*⁴⁰,⁴¹, phylogenetic analysis revealed that the fly-derived *Fibrobacter* MAGs are closely related to *Fibrobacter* genomes constructed from manure samples collected at the same facility. Our analysis also revealed that, despite the presence of shared taxa, microbial community composition is still largely determined by sample type. This supports the hypothesis that, while flies acquire a

substantial portion of their microbiota through the consumption of manure¹³,⁴², physiological conditions (pH, enzymatic activity, oxygen levels), environmental factors, and competition between resident microorganisms in the fly GIT all likely contribute to the microbial diversity we identified in the flies we sampled⁴². For example, our finding that rumen-associated obligate anaerobes, such as *Bifidobacterium* and members of the Lachnospiraceae family³⁰,⁴³,⁴⁴, were enriched in manure microbiomes but not in the fly GIT suggests that these bacteria likely cannot effectively colonize the fly GIT. However, some sequences enriched in the fly GIT microbiome included taxa (*Apilactobacillus*, *Entomomonas*, and *Frischella*) commonly associated with nectarivorous insects, which may have been acquired through feeding on sugar substrates or sources of plant matter¹²,⁴⁵⁻⁴⁷. *Frischella*, the most highly enriched MAG in our fly samples, has been previously identified as a honeybee symbiont that can colonize a spatially restricted niche in the bee GIT and alter scab formation immune responses⁴⁸,⁴⁹. While *Frischella* has also been identified in fruit flies (Diptera: Tephritidae)⁵⁰, this is the first report, to our knowledge, of *Frischella* in coprophagous flies and further studies are needed to determine if *Frischella* represents a specialized bacterial symbiont in the fly GIT.

Our functional metabolic predictions of MAGs suggest a potential role in providing mutualistic services to the host. A comparison of identified
<figure><img src="image_8.png" /><figcaption>**Fig. 4 |** PCA plots of fly and cow samples identified as containing pathogen sequences. Cow and fly samples were plotted on a PCA based on the dissimilarity of the recovered *Bos taurus* COI sequences. Fly samples are represented by red dots and manure samples are represented by blue dots. Samples with at least 10% total</figcaption></figure>

coverage against the respective reference pathogen genome are labeled. Corresponding coverage and CLR transformed abundance for each pathogen are shown in Supplementary Fig. 8.

complete metabolic pathways in the common, unique to manure, or unique to fly GIT group of MAGs showed that the latter encoded several complete pathways (Fig. 2C). This included pathways involved in the metabolism of co-factors/vitamins (Vitamin B6/Pyridoxal-P biosynthesis, Lipoic acid biosynthesis) and amino acids (Valine/Isoleucine biosynthesis), which were attributed to *Frischella*. The biosynthesis of pyridoxal-P is particularly significant as insects must derive B vitamins from the diet or from microbial symbionts⁵¹. This pathway was present in several MAGs unique to manure and the fly GIT, including *Frischella*, a bacterium which may represent an important source of B-vitamin and amino acid provisioning due to its ubiquitous presence across fly samples.

The next objective of our study was to determine if the fly GIT can serve as a reservoir for manure-associated pathogenic microbes. We identified a set of VFs common to the fly microbiome, which included genes originating from both *C. burnettii*, the causative agent of Q fever⁵², as well as various *E. coli* pathotypes. Our analysis further revealed VFs that mapped with high coverage and were highly prevalent in the fly GIT. These included *E. coli* T3SS and *C. burnetti* T4SS effectors, which were significantly enriched in the fly GIT microbiome compared to cow manure (Fig. 3B; Supplementary Table 7). Genes related to secretion systems represented a substantial proportion of highly prevalent VFs in flies but were comparatively less prevalent in the manure microbiome (twenty-nine secretion system related VF genes were found in the prevalent fly GIT features but only six in the respective manure group features). Expression of secretion systems has been shown to promote colonization in other invertebrate hosts⁵³,⁵⁴, which could contribute to the ability of these bacterial pathogens to transition into the fly GIT niche and may help explain the high prevalence and coverage of the observed *E. coli* and *Coxiella* pathotypes in the fly GIT environment.

Beyond the mere presence of VFs across fly GIT microbiomes, we also found specific cooccurrence patterns suggesting potential cow manure to fly transmission through our bovine COI gene analysis. By mapping reads from the fly and manure metagenomes to the pathogen

genomes associated with the VFs identified (Fig. 3, Supplementary Table 7), we identified four *Escherichia*, two *Shigella*, and two *Coxiella* genomes, which could be mapped to flies and manure samples that shared the same bovine COI (Fig. 4). We found that the relative pathogen abundance and coverage was normally higher in flies compared to manure samples (Supplementary Fig. 8), which suggests that manure-associated bacteria may be able to proliferate in the fly GIT¹³,⁴². Although we found multiple pairs of manure and fly samples with similar bovine COI genes that carried sequences associated with these pathogens, we also found instances of pathogen carriage where there was no overlap in COI gene similarity (i.e., flies with bovine COI sequences that were not similar to the bovine COI sequences from the manure samples), suggesting that the flies may have acquired these pathotypes elsewhere (e.g., previous feeding) but persisted in the fly GIT. Alternatively, the presence of pathotypes in the flies' guts but not in the manure sample could be related to a temporal delay, if the animals may have shed some of these pathogens at some point before the collections took place, which the flies then acquired via feeding.

Our finding of *C. burnetti* strains from one manure sample in multiple flies could potentially indicate the ability of this pathogen to radiate across environments. The widespread persistence of *C. burnetti* RSA493 in flies is of significant concern due to the extremely low infectious dose required to cause disease symptoms⁵²,⁵⁵, and suggests an increased risk for Q fever among farm workers through fly to human transmission. The factors aiding to its wide dispersal could be related to both the multiple secretion systems present and the presence of a near-complete Pyridoxal biosynthesis pathway, which may be important for fly host physiology (see above). While this hypothesis is at present based only on the occurrence of predicted enzymes of predicted bacterial genomes (and thus remains unsubstantiated), future studies are warranted to explore whether the presence of human or animal pathogens in insect vectors may be facilitated by their ability to produce useful (by)products for their insect host.
We also found that manure and fly samples with similar bovine COI genes also harbored sequences mapped to potential *E. coli* and *Shigella* pathogens. The presence of *E. coli* or other Enterobacteriaceae in the fly GIT microbiome is not surprising, as these are known commensals in dipteran insects<sup>33,34</sup>, but our finding of large genomic sequences belonging to enterohemorrhagic *E. coli* O111 and O157 as well as the opportunistic urinary tract pathogen *E. coli* VR50 in both fly GIT and cow manure microbiomes is of concern, given that these strains are known to cause illness in humans<sup>56,57</sup>. Further comparative studies are needed to understand if manure-associated insects carry higher rates of virulent strains of *E. coli* or other zoonotic pathogens.

In addition to the acquisition of zoonotic pathogens in the fly GIT microbiome, there is also growing concern regarding the selection for ARGs through antimicrobial use on dairy farms. In Europe, ceftiofur, tetracyclines, and lincosamides are commonly used on dairy farms<sup>38</sup>, and this was confirmed through our examination of antimicrobial use records from our research farm. We found that antibiotic usage for the 48 animals included administration of antibiotics containing cephalosporins and tetracyclines as active compounds (data not shown). Our corresponding analysis of ARGs in the cow manure and fly GIT microbiomes revealed the presence of both *tet* and *bla*OXA genes, which are associated with resistance to tetracycline and beta-lactams, respectively<sup>59</sup>. While the congruency of these findings suggest that long-term use of these antibiotics may have resulted in the selection for these specific ARGs, we found no evidence of such a relationship, and we acknowledge that AMR emergence and dissemination are linked to various factors, including environment and ecology, and not solely to AMU<sup>60</sup>. However, it is important to note that our findings of the same ARGs in both cow manure and fly GIT microbiomes suggests the transfer of these genes across environments and would thus pose a significant risk for opportunistic pathogens in the fly GIT that could acquire these ARGs via horizontal transfer.

To our knowledge, this study is the first to offer metagenomic insight for the potential transmission, and persistence of specific microbes from cow manure to muscid (*Neomyia*) flies. Our study identifies a microbiome that is specific to the muscid fly GIT with genomic features that overlap with cow manure. Importantly, fly GIT and cow manure microbiomes share numerous VFs and ARGs belonging to known zoonotic pathogens. This suggests that these genomic features may be acquired by the fly during feeding, but more functional experiments and a wider sampling campaign, including more samples from multiple farms will be needed to confirm these findings. Furthermore, considering how the microbial content of manure potentially changes across seasons and years, longitudinal studies will be needed to examine how the transmission potential may vary depending on temporal change. Follow-up studies will also help formulate hypotheses about the environmental or physiological constraints preventing some microbes from transmitting to the fly GIT (e.g., physiological, ecological or related to the virulence of some microbes). Such studies may also elucidate the roles of flies in the dissemination of ARGs on active livestock facilities, as has been previously observed under laboratory conditions<sup>38,42,61</sup>.

We found strong specificity for pathogens like *C. burnetti*, which were recovered from multiple flies consuming the same cow manure. This suggests that some pathogens may more easily transmitted across environments, which presents significant occupational hazards for farm workers who are frequently exposed to these potential vectors. As such, flies could serve as a potential route of zoonotic transmission off of farms, given that house flies in agricultural settings can travel over three kilometers within 24 h<sup>62</sup>. Transmission of these pathogenic microbes to humans and other animals can occur through the contamination of food as flies disseminate GIT-associated bacteria when feeding via defecation and regurgitation of salivary fluids<sup>10-12</sup>. Our findings are further compounded by antibiotic use in the agricultural industry, which represents one of the largest users of antibiotics and is a known contributor to the rise of antibiotic resistance. Indeed, our study found the presence of beta-lactam resistance genes in known

pathogens both in the fly GIT and in cow manure, which coincided with the usage of antibiotics containing these active compounds on our research farm. Overall, our study highlights the need for increased scrutiny of farm management practices to consider the role of flies and other potential vectors as risk factors for the dissemination of zoonotic pathogens both on and off of farm environments.

## Methods

### Sample collection

Twenty-nine flies, which were either standing on or hovering above freshly defecated cow manure, were collected using a fly net at an INRAE experimental dairy farm in Saint-Genès-Champanelle, France over the course of two days (15 and 17 September 2020). Following collection with a mosquito net, flies were placed on ice (which rendered them unconscious immediately) for approximately four hours. They were then transferred (on ice) and dissected under a stereoscope on a petri dish filled with ice to keep them anaesthetized. Using sterile forceps, their GITs were dissected in a drop of sterile phosphate-buffered solution and placed into sterile Eppendorf tubes, which were stored at -80 °C until DNA extraction. The treatment of cow fecal samples collected the same day from the field has been described before<sup>30</sup>.

### DNA extraction, library preparation, and sequencing

Genomic DNA was extracted from dissected fly GITs using a Qiagen DNeasy Blood and Tissue Kit (Qiagen, Germany) following manufacturer's instructions. DNA quality and quantity were determined using a Nanodrop (ThermoFischer Scientific, Waltham, MA) and then sent to Novogene (Singapore) for sequencing. Library construction was performed using the PCR-free NEBNext Ultra II DNA Library Prep Kit, and all samples were sequenced in a single run of an Illumina NovaSeq 6000 using a 2 × 150 bp paired-end sequencing kit, generating a minimum of 60 million reads per sample. Sequencing of the 48 cow manure samples used in this study were performed using identical parameters, as described in Teseo et al.<sup>30</sup>. Manure sample reads are available under BioProject accession number PRJNA681986.

### Data filtering, contig assembly, and MAG building

Following shotgun metagenomic sequencing, we performed data filtering, assembly, and bin construction using a pipeline established previously<sup>30</sup>. Briefly, raw reads were trimmed and filtered using Trimmomatic and assembled using megahit (v1.1.1) with the "-meta-large" option and a minimum of 300 bp contig length<sup>63,64</sup>. Autometa was used to map filtered reads to the assembled contigs and create a coverage table<sup>65</sup>. Prodigal was used to identify open reading frames (ORFs) and the resulting predicted proteins were used to compare against the NCBI non-redundant (nr) protein database (downloaded December 2020)<sup>66,67</sup>. Taxonomic classification of assembled contigs was performed through sequence comparison against the NCBI prokaryotic genome database<sup>68</sup>. Contigs greater than 1000 base pairs were binned by both taxonomy and sequence characteristics. MAGs were then constructed using the Autometa pipeline<sup>65</sup>. MAG completeness was determined using CheckM<sup>69</sup>, which resulted in a total of 42 non-redundant fly-derived MAGs with >85% completeness and <5% contamination.

### Fly genotyping

To genotype the collected flies and build a host phylogeny, we first identified existing mitochondrial COI genes from the metagenome of each sample using KMA v1.3.11 against three partial reference sequences of the *Neomyia cornicina* COI (MW592695.1, KF919034.1, KU932144.2)<sup>70,71</sup>. The identified 29 COI sequences were compared to the NCBI's non-redundant (nr) database (January 2023) using BLASTN, and the closest published sequences were obtained and used to build a phylogeny. For samples W15 and W20, for which the COI gene was more similar to the COI gene for *Myospila meditabunda*, we ran the KMA algorithm using the published *M. meditabunda* COI genes (KJ510632.1 and FJ025642.2). All sequences were
aligned using MUSCLE and gaps were trimmed using trimAl⁷²,⁷³. Phylogenies were constructed using PhyML implemented in Seaview with 100 bootstraps, and the tree was further edited in Inkscape⁷⁴⁻⁷⁶.

## Phylogenomic analysis of fly-derived MAGs

Phylogenomic trees were constructed for the 42 non-redundant fly GIT MAGs, the closest Refseq genomes used to identify these MAGs through Autometa, and the closest MAGs from the 2114 cow manure MAGs derived in Teseo et al.³⁰. Single copy orthologs for MAGs within each dataset were identified using orthofinder and alignments were built using MUSCLE v3.8.31⁷³,⁷⁷. We removed all positions with gaps in 10% or more of the sequences using TrimAl unless this resulted in <50% of the original sequences and alignments were further tested for recombination using the Phipack software⁷²,⁷⁸. Concatenated alignments were built using AMAS, and model selection was performed with Protest⁷⁹,⁸⁰. Maximum likelihood (ML) phylogenomic trees were built using RaxML v889 using the rapid Bootstrap analysis algorithm with 1,000 bootstraps, and the best scoring ML tree from each run was imported into R (Version 4.2.3) and further modified using ggtree⁸¹⁻⁸³.

## Analysis of microbial community structure

A recruitment analysis was performed on reads from all fly GIT and cow manure metagenomes against a combined database of the 42 non-redundant MAGs from the fly GIT metagenome and the 2114 non-redundant MAGs from our previous study³⁰. To accomplish this, we first built a combined database of MAGs derived from the 29 fly GIT metagenomes (as described above) and a collection of metagenomes (N = 436) from cow manure samples that included a subset (N = 48) collected from the same farm and during the period of fly collection³⁰. This combined database of MAGs was dereplicated using dRep with default options (*primary clusters cutoff: 0.9, secondary clusters cutoff: 0.99*) to generate a non-redundant MAG database⁹⁴. Using KMA (k-mer alignment) a reference template was constructed from this non-redundant MAG database and the filtered reads from the 29 fly GIT and the 48 cow manure metagenomes were aligned to create a count table of contigs/MAGs in each sample. Sample counts were normalized by estimating genome equivalents using MicrobeCensus and used to create relative abundance proportion tables, which were used for all downstream analyses⁸⁵. Abundance tables were further imputed to adjust 0 counts and MAGs with <20% prevalence across all samples were removed. Tables were transformed to centered-log ratios (CLR) using the zComposition package in R when relevant⁸⁶. The 50 most abundant MAGs in the fly and manure samples were visualized as chord diagrams using the circulate package in R⁸⁷. CLR transformed abundances of bacterial orders (MAGs were merged by taxonomy) were visualized using ggplot2⁸⁸. Sample similarity based on the Aitchison distances of bacterial order abundances was calculated using vegan package in R and visualized using PCA⁸⁹.

## Enrichment and functional analysis of MAGs

To determine if specific MAGs from our non-redundant MAG database were enriched in either the fly GIT or cow manure samples, we performed a differential abundance test on normalized proportions of MAG abundance counts using the indicspecies package in R (Version 1.7.14, 1000 permutations)⁹⁰, with p-values adjusted for false discovery rates using the “fdr” multiple testing correction. This showed that 21 MAGs were highly specific to the fly-samples and 41 to the cow manure samples (FDR corrected *p* < 0.05). For the cow manure group, out of the 41 which were statistically significant enriched, we chose the 21 that showed the biggest specificity to the cow-manure group (association value > 0.86). Finally, a set of MAGs defined as common between the fly GIT and cow manure metagenomes was determined as the intersection of the 100 most abundant MAGs in the fly GIT and cow manure dataset, again by picking the top 21, in order to create three groups with equal MAG numbers.

For each group of MAGs, we then extracted the functional annotation information, to identify common and unique orthologs. In short, predicted

protein sequences were used to perform a diamond BLAST (v2.0.5.143) against the eggNOG protein database (v2020-11-12) with a 1e-05 E-value cutoff, and the best match of each query was then used as input for eggNOG-mapper (version 2.0.4)⁹¹,⁹². The resulting unique and shared KEGG KOs as well as the associated clusters of orthologous groups (COGs) were visualized using ggplot2 and ggvenndiagram in R⁸⁸,⁹³. Differences in COG counts were statistically evaluated using a Fisher's exact test and Bonferroni correction for multiple comparisons. Complete functional pathways encoded by MAGs were determined through comparison against the KEGG Modules database.

## Virulence factor and antimicrobial resistance gene annotation

Reads from the 29 fly GIT and 48 cow manure metagenomes were aligned against a KMA reference template constructed from nucleotide sequences obtained from the VFdb (October 2022) and the Resfinder (October 2022) databases⁹⁴,⁹⁵. Count tables were then normalized and transformed using MicrobeCensus and the zComposition package, as described above. Differences in CLR transformed abundances between fly and manure samples were determined using two-sided Wilcoxon signed-rank tests, with p-values adjusted for multiple comparisons using a Bonferroni correction based on the number of CLR transformed genes in each set (N = 399 VFs; N = 57 ARGs). To examine the correlation between ARGs and antimicrobial use on the farm, we first obtained records for all antimicrobial usage from 2017 to 2021. We then performed a coinertia analysis using the ‘ade4’ package in R to correlate antimicrobial use against the ARGs identified in our metagenomes⁹⁶.

## Identifying bovine mitochondrial COI genes from fly GIT metagenomes

To link individual flies to specific manure pools, we identified and compared mitochondrial COI gene sequences found in the fly GIT and manure metagenomes against the *Bos taurus* genome. This was accomplished by mapping the sequence reads of each metagenome against the mitochondrial COI (NC_006853.1) genes from the *Bos taurus* genome (GCF_002263795.1_ARS-UCD1.2) using KMA. Although not all fly GIT metagenomes contained a mitochondrial COI gene sequence that mapped to *Bos taurus* (23/29 recovered), most of the ones that did have, presented a > 99% sequence identity. Comparisons of these sequences with the mitochondrial COI genes recovered from the cow manure metagenomes revealed sequence identities ranging mostly from 98.6 to 99.9% across the majority of the gene alignment (range: 73–100%, median: 99%).

We then examined whether multiple mitochondrial COI alleles could be identified in our fly GIT metagenomes, indicating that a given fly consumed cow manure that originated from more than one manure sample. To accomplish this, we identified the consensus mitochondrial COI gene sequences from each of the 48 cow manure metagenomes and the 23 fly GIT metagenomes for which mitochondrial COI genes were identified. Alignment of these mitochondrial COI genes using bowtie2 showed that for each fly GIT sample, only a single allele could be identified (allele frequency = 0.99)⁹⁷. For both the fly samples and the combined fly-manure dataset, distance matrixes for the bovine COI gene alignments were calculated using an F81 model with the phangorn package in R⁸⁸. Principal component analysis (PCA) suggested that the first component of the analysis could explain the most variation, and based on sample clustering, three groups were assigned.

## Identification of pathogen genomes across samples

We next sought to determine if specific pathogen genomes could be linked to the identified bovine COI clusters, which could suggest transmission of microbial pathogens from manure to fly samples. A pathogen genome reference set (chromosomes and plasmids) was constructed by retrieving NCBI genomes associated with virulence factor genes that were identified as shared between fly and manure samples with at least 90% coverage of their gene length. The fly GIT and manure metagenomes were mapped using KMA to the retrieved genomes (excluding results with <10% genome
coverage), and CLR transformed abundance was determined as described above.

## Data availability

Raw Illumina reads from the 48 manure samples associated with Teseo et al.³⁰ are available in the NCBI Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra) under BioProject accession number PRJNA681986; reads from the 29 fly samples generated in this study are available under BioProject accession number PRJNA911623. Scripts used for analysis and figure generation are available at https://forgemia.inra.fr/sapountzis0454medis/Neomyia_microbiome. Supplementary Tables 5 and 7 are available at https://github.com/kcoonlab/neomyia-metagenomics. All other data generated by this study are available as Supplementary Information herein.

Received: 21 June 2024; Accepted: 16 March 2025; Published online: 19 June 2025

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## Acknowledgements

We would like to thank Sabine Leroy and the entire farm staff at the INRAE research farm in Saint-Genès-Champanelle, France for their help in sampling and animal management, and the Mésocentre Clermont Auvergne University and AuBi platform for providing support regarding computation and storage resources. We would also like to thank the MEDIS, Coon and Suen laboratories for their support and insightful discussions. This study was supported by a MICA starting grant to P.S., two United States Department of Agriculture (USDA) National Institute of Food and Agriculture (NIFA) Hatch grants (#WIS06036 and #WIS04039 to K.L.C. and G.S., respectively), a UW Dairy Innovation Hub Short-Term, High-Impact award (to K.L.C.) and a presidential postdoctoral Fellowship from the Nanyang Technological University of Singapore to S.T. A.J.S. was additionally supported by a USDA AFRI Education and Workforce Development Predoctoral Fellowship (#2023-67011-40337).

## Author contributions

S.T. and P.S. conceived the study and collected samples. All authors contributed to the design of the analysis. A.J.S. and P.S. led the analyses and

wrote the initial paper draft, while J.H.S., S.O., G.S., and K.L.C. contributed to revisions. All authors read and approved the final manuscript prior to submission.

## Competing interests

The authors declare no competing interests.

## Ethical approval

This study exclusively involved coprophagous muscid flies (Diptera: Muscidae) and did not involve any vertebrates or higher invertebrates. All procedures, including anesthesia and dissection, were conducted in accordance with standard ethical guidelines for invertebrate research. As Muscidae are not classified as higher invertebrates or regulated animals under institutional or national guidelines, no formal ethical approval was required.

## Additional information

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41522-025-00685-y.

**Correspondence** and requests for materials should be addressed to Kerri L. Coon or Panagiotis Sapountzis.

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