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Deep culturing the fecal microbiota of healthy laying hens

Animal Microbiome volume 7, Article number: 32 (2025) Cite this article

Abstract

Background

The microbiota is implicated in several aspects of livestock health and disease. Understanding the structure and function of the poultry microbiota would be a valuable tool for improving their health and productivity since the microbiota can likely be optimized for metrics that are important to the industry such as improved feed conversion ratio, lower greenhouse gas emissions, and higher levels of competitive exclusion against pathogens. Most research into understanding the poultry microbiota has relied on culture-independent methods; however, the pure culture of bacteria is essential to elucidating the roles of individual bacteria in the microbiota and developing novel probiotic products for poultry production.

Results

In this study, we have used a deep culturing approach consisting of 76 culture conditions to generate a culture collection of 1,240 bacterial isolates from healthy chickens. We then compared the taxonomy of cultured isolates to the taxonomic results of metagenomic sequencing to estimate what proportion of the microbiota was cultured. Metagenomic sequencing detected DNA from 545 bacterial species while deep culturing was able to produce isolates for 128 bacterial species. Some bacterial families, such as Comamonadaceae and Neisseriaceae were only detected via culturing – indicating that metagenomic analysis may not provide a complete taxonomic census of the microbiota. To further examine sub-species diversity in the poultry bacteriome, we whole genome sequenced 114 Escherichia coli isolates from 6 fecal samples and observed a great deal of diversity.

Conclusions

Deep culturing and metagenomic sequencing approaches to examine the diversity of the microbiota within an individual will yield different results. In this project we generated a culture collection of enteric bacteria from healthy laying hens that can be used to further understand the role of specific commensals within the broader microbiota context and have made this collection available to the community. Isolates from this collection can be requested by contacting the corresponding author and will be provided at cost.

Background

There is significant evidence that having a gastrointestinal tract that is colonized by bacterial commensals offers several health benefits to the host [1]. The gastrointestinal microbiota in poultry plays an important role in nutrient digestion [2, 3] and absorption [4], productivity [5,6,7], immune modulation and infectious disease resistance [8, 9], stress tolerance [10,11,12], and fertility [13]. Improved productivity, in terms of feed conversion, is likely to occur because of reduced gastrointestinal inflammation [14, 15], and infectious disease resistance conferred by the microbiota appears to be primarily due to competitive exclusion of pathogens [16]. In addition, a gastrointestinal microbiota composition that decreases crude protein digestibility but increases the production of volatile fatty acids, acetate, and urea results in decreased ammonia emissions and improves the respiratory health of the birds [17].

The composition of the gastrointestinal microbiota can be heavily influenced by housing-system, litter management, age, diet, and feed additives [18]. Intensively produced poultry which were hatched in commercial hatcheries, raised in enclosed barns, and have had limited or no contact with the microbiota of birds outside their flock have a less robust microbiome compared to feral poultry or poultry raised in extensive production systems [19]. Poultry housed in extensive production systems have greater movement opportunities and possible access to outdoor environments. The microbiota of extensively raised birds tends to have fewer antibiotic resistance genes [20], greater colonization resistance [21], and more bacterial diversity [21,22,23,24] than their intensively raised counterparts. Antibiotics are used extensively in poultry production for therapeutic and growth promotion purposes – although Europe banned the use of antibiotics as growth promotors in 2006, and the use of antibiotics has been shown repeatedly to cause dysbiosis in the poultry gastrointestinal tract [25,26,27,28] and change patterns of digestion [29].

Most studies aiming to provide a census of the healthy poultry microbiome have used culture-independent analysis [21, 30], while only a handful have conducted meaningful culture-based analyses [24]. More culture-based studies are necessary to fully understand the role of the microbiota in poultry health and disease. For example, isolates and corresponding whole genome sequencing (WGS) will aid in interpreting metagenomic sequencing data for which sequences are still produced that do not correspond to identified organisms [31]. There is also significant interest in optimizing the poultry gastrointestinal microbiota to enhance feed conversion and disease resistance, and decrease noxious gas emissions; although, limited success in this area has been achieved [32]. Isolates are required to fully explore the interbacterial relationships between various bacteria, develop models for studying microbiome modification, and to develop targeted probiotics for poultry. The latter is particularly important since traditional probiotics, usually harvested from soil or fermented food, have consistently failed to colonize the gastrointestinal tract of poultry [33, 34]. We currently lack the understanding of the inter-bacterial interactions that occur in the poultry gastrointestinal microbiome required to effectively modulate it, and pure cultures are required for this research [30].

In this study we aimed to create a comprehensive culture collection of bacterial isolates from healthy laying hens. To accomplish this, we deep cultured fecal samples from six healthy laying hens using high-throughput culture followed by matrix-assisted laser desorption/ ionization – time of flight (MALD-TOF) mass spectrometry (MS) or 16S rRNA gene sequencing to identify the isolates. We then assessed how much of the chicken microbiome we were able to culture by comparing our isolate collection to a shotgun metagenomic analysis of the same samples.

Results

Deep culturing of the healthy chicken microbiota

In this study we collected 1,240 isolates from fecal samples collected from six healthy laying hens from across Canada (Table S1) [35]. At least one isolate was collected for each 128 different bacterial species and 2 fungal species (Table S2). Five bacterial species: Pseudescherichia vulneris (previously Escherichia vulneris), Filifactor villosus, Paenibacillus macerans, Paenibacillus motobuensis, and Paenibacillus odorifer and one bacterial genus – Acidovorax sp. had not previously been collected from a chicken fecal sample (Table S2). Each of the cultured species came from one of four different phyla: Bacillota (previously Firmicutes) (55%), Pseudomonadota ( previously Proteobacteria) (36%), Actinomycetota (previously Actinobacteria) (8%), and Bacteroidota (previously Bacteroidetes) (0.9%); and could be further classified into one of six bacterial classes: Bacillus (45.95%), Gammaproteobacteria (30.63%), Clostridia (9.01%), Actinomycetia (8.11%), Betaproteobacteria (5.41%), and Bacteroidia (0.90%).

Culture-independent analysis

Targeted amplicon sequencing (TAS) of the 16S rRNA gene was performed on each sample. After sequencing, a total of 194,850 sequence reads passed filter averaging 18,590 reads per sample. Following analysis each sample had a Good’s coverage of > 0.99. All 16S rRNA TAS sequences are deposited in the SRA under bioproject PRJNA1143568. On average, across the six samples, the presence of seven different phyla were detected: Bacillota (87.70%), Actinobacteriota (7.59%), Bacteroidota (2.26%), Fusobacteriota (previously Fusobacteria) (1.40%), Pseudomonadota (0.87%), Desulfobacterota (0.11%), Verromicrobiota (0.05%), and Thermoplasmatota (0.03%). At the class level 16S rRNA TAS found an average distribution of Bacilli (50.57%), Clostridia (36.79%), Actinomycetota (6.52%), Bacteroidia (2.26%), Fusobacteriia (1.40%), Coriobacteriia (1.07%), Gammaproteobacteria (0.87%), Negativicutes (0.34%), Desulfovibrionia (0.11%), Lentisphaeria (0.05%), and Thermoplasmata (0.03%).

Shotgun metagenomic sequencing efforts produced a total of 267,269,081 sequence reads. After host sequences and primers were removed there were 240,525,450 sequence reads for an average of 40,087,575 reads per sample. Each of the sequences is deposited in the SRA under bioproject PRJNA1143568. On average, across the six samples, the presence of 12 bacterial phyla were detected in the metagenomic sequence reads, the phylum represented by > 1% of sequence reads were: Bacillota (81%), Pseudomonadota (2%), Actinomycetota (12%), and Bacteroidota (4%) (Fig. 1A). At the class level the average distribution was Bacilli (69.35%), Actinomycetia (10.00%), Clostridia (9.28%), Bacteroidia (3.82%), Coriobacteriia (1.62%), and Gammaproteobacteria (1.60%). Shotgun metagenomic sequencing was able to detect the presence of 545 different bacterial species (Fig. 1A). At the species level, the core microbiota (using a prevalence threshold of 60% and an abundance threshold of 1%) was very small and contained only: Lactobacillus gallinarum, Streptococcus alactolyticus, Limosilactobacillus vaginalis, Lactobacillus kitasatonis, Enterococcus cecorum, Lactobacillus crispatus, and Lactobacillus johnsonii (Fig. 1B).

Fig. 1
figure 1

Microbiome taxon distribution hierarchically and core genome heatmap of shotgun metagenomic sequencing. A This hierarchical tree shows the taxonomic distribution of bacteria identified using shotgun metagenomic data. Ambiguous taxa names were excluded. The root node represents the entire microbiome of the six fecal samples combined, with subsequent nodes branching into distinct phyla, classes, orders, families and genera. The colour intensity of each node reflects the frequency of its appearance, with deeper colours indicating nodes that encompass more taxonomic mentions, regardless of their level in the hierarchy. B Relative abundance of core microbiota bacterial species in the shotgun metagenomic data across six healthy fecal samples. The colours indicate the prevalence of bacterial species in the total samples and their relationship with different detection thresholds

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Deep sequencing compared to culture-independent analysis

A comparison between the species that were identified via deep culturing vs those identified by metagenomic sequencing revealed that only a small percentage of the bacteria identified by shotgun metagenomic sequencing were cultured (Fig. 2). There were 545 species that were identified by shotgun metagenomics but were not isolated, 94 species that were isolated but not identified by metagenomic sequencing, and only 34 that were both sequenced and isolated (Tables S2 and S3). Members of the phyla Verrucomicrobia, Lentisphaerota (previously Lentisphaerae), Synergistota (previously Synergistetes), and Mycoplasmatota (previously Tenericutes) were identified using shotgun metagenomics, but no isolates from these phyla were cultured (Fig. 2A). There was a large cluster of Bacillota, which could not be identified beyond the phylum level that were identified by shotgun metagenomic sequencing but for which no culture was produced (Fig. 2A). In addition, DNA from members of the families Oscillospiraceae, Lachnospiraceae, and Peptostreptococcaceae was abundant and diverse based on shotgun metagenomics, but representatives of these families were not cultured. Within the Pseudomonadota phylum members of the classes Alphaproteobacteria, Deltaproteobacteria, Epsilonproteobacteria, Erysipelotrichia, Negativicutes, and Coriobacteriia were identified by shotgun metagenomic sequencing but were not cultured (Fig. 2A).

Fig. 2
figure 2

Species level comparison of shotgun metagenomic sequencing and deep culturing results. A hierarchy comparison of the bacterial species in this study was conducted to compare species identified by either culturing or shotgun metagenomic sequencing, with each terminal node representing a unique species. In A successfully cultured isolates which were identified to the species level using either MALDI-TOF MS or 16S rRNA gene sequencing are indicated using shade of pink, while grey is used for species only identified by metagenomics. In B species that were identified using shotgun metagenomic sequencing are indicated by a shade of blue, while nodes shown in grey indicate species for which isolates were obtained but which were not identified using shotgun metagenomics

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Alternatively, at the genus and species levels there were some bacteria which were cultured that were not identified at this taxonomic resolution using the shotgun metagenomics data. This included several Clostridium species including C. sartagoforme, C. sporogenes, C. paraputrificum, C. subterminale, C. perfringens, C. tertium, and Paraclostridium bifermentans (previously C. bifermentans) (Figure S1). Several Enterobacteriaceae were also able to be cultured but were not identified via shotgun metagenomics including Escherichia fergusonii, Escherichia vulneris, Shigella sonnei, Shigella flexneri, Enterobacter cloacae, Cronobacter sakazakii, Kluyvera intermedia, Leclercia adecarboxylata, and Morganella morganii (Figure S2). Finally, within Bacillaceae there were B. cereus, B. megaterium, B. pumilus, B. sonorensis, B. thuringiensis, Niallia circulans (previously B. circulans), Priestia flexa (previously B. flexus), Heyndrickxia oleronia (previously B. oleronius), and Caldibacillus thermoamylovorans (previously B. thermoamylovorans) isolates that were cultured but not identified by shotgun metagenomics as well as several Paenibacillus sp. and Lysinibacillus sp. isolates (Figure S3).

E. coli diversity in healthy chickens

A total of 443 E. coli isolates were collected and identified from the six fecal samples. From these, 114 were selected, including several strains from each sample, and whole genome sequenced to examine the extent of phylogenetic diversity within a single bacterial species which can be present in a single healthy chicken. The coverage of each sequenced genome was > 10 (Table S4). Of these 114 isolates that were identified as E. coli by MALDI-TOF MS or by sequencing the entire 16S rRNA gene, 107 were classified as E. coli based on the whole genome sequencing and 7 were identified by as E. fergusonii. ANIclustermap was used to determine that 2–11 different strains could be found in a single chicken (Table 1). In addition, each sample contained representative isolates from several phylogenetic groups (Fig. 3, Figure S4) including A, B1, B2, D1, D2, and E. Around 64% of these isolates carried ompT, ompT_1 or ompT_2 genes (68/107), 12 strains carried iutA, iutA_1 or iutA_2 genes (Table S5). No relationship was found between the prevalence of these pathogenic genes and the groups.

Table 1 E. coli isolates sequenced from different chickens
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Fig. 3
figure 3

Phylogenetic tree of select E. coli isolates. A digital DNA-DNA hybridization phylogenetic tree was made using the whole genome sequence with Genomic BLAST Distance Phylogeny (GBDP) using the TYPE Genome Server (TYGS) server. Each color represents a different E. coli phylogenetic group based on homology to reference genomes (Table S6). Branch lengths are GBDP pseudo-bootstrap support values > 60% from 100 replications [36,37,38,39]

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Discussion

In this study, we confirmed the presence of a diverse microbiota – including substantial inter-species diversity in the case of E. coli, in the intestinal tract of laying hens, demonstrated a large discrepancy in the composition of microbiota observed by using deep culturing versus culture-independent metagenomic sequencing analysis, and created a large culture collection of bacterial isolates from fecal samples taken from healthy chickens. Like other studies, our culture-dependent work produced isolates belonging to the phyla Bacillota, Actinomycetota, Pseudomonadota, and Bacteroidota [40,41,42]. Our study built on these earlier studies by using a wider array of culture conditions, and therefore, was able to produce isolates for five additional species and one additional genus which had not previously been isolated from poultry. For isolation of bacteria from chicken feces in aerobic conditions Sheep Blood Agar, Orange Serum Agar, Modified Letheen Agar, and R2A were very successful for isolating diverse bacteria. For anaerobic culture, Glucose Bromcresol Purple Agar was successful in producing a diverse group of organisms. However, including a more diverse selection of media was important for isolating some less common taxa; for example, our P. vulneris isolate was isolated on Drigalski Lactose Agar. Although our deep culturing approach resulted in a large and diverse collection of isolates there were several notable and common chicken commensals that have previously been isolated but were not isolated here. A few such examples are Faecalibacterium sp. [43] and Akkermansia sp. [44]. The absence of these genera in our culture collection likely reflects our use of feces as the isolate source since Faecalibacterium sp. has generally been isolated from the ceca [43] and Akkermansia sp. resides in the mucus layer of the intestines and may not be readily shed in the feces. Both are also anaerobes, and our sampling techniques did not maintain strictly anaerobic conditions through the sample collection and aliquoting process. Some notable chicken species including Bacillus subtilis, Lactobacillus crispatus, and Ligilactobacillus salivarius were also not isolated here. It is unclear why these species were not isolated although several representatives of the Bacillus and Lactobacillus genera were.

Our metagenomic sequencing efforts identified the presence of 12 phyla, although the composition of the microbiota observed through sequencing was heavily dominated by Bacillota – which also agrees with previous work [45]. The massive discrepancy between the composition microbiota observed via culture-independent and culture-dependent work has also been seen in similar studies and calls into question the best strategy to truly understand this, or any, microbial population [45]. Culture-independent strategies, at least for now, are unable to differentiate living from dead bacterial cells and this contributes to the discrepancy. There is also unlikely to be a truly universal growth media, and therefore, culture-dependent strategies will be unable to be comprehensive.

Within the Bacillota phyla metagenomic sequencing revealed that DNA associated with Oscillospirale and Lachnospirales was present, abundant, and diverse; however, members of these classes were not cultured in our study. Oscillospirale has been widely identified in human and animal gastrointestinal tracts; although, a pure culture has not yet been produced from intestinal samples from any host [44]. Metagenomic sequencing data suggests that Oscillospirale may produce several short-chain fatty acids (SCFAs) dominated by butyrate and may therefore be an excellent next-generation probiotic [46]. Butyrate is the main energy source for enterocytes and is involved in several biological processes that contribute to a healthy gut [47, 48]. Future studies should focus on using metagenomic assembled genomes from this class to design strategies, that optimize media components based on genomic guidance [49]. For example, based on the functional annotation results, different isolation strategies can be applied, such as specific carbon source selection[49, 50], or reducing nitrogen that selectively isolates bacteria that can fix nitrogen [51]. The presence of Lachnospirales in the ceca and feces of chickens has been associated with high productivity and optimal feed conversion ratios (FCR), and based on metagenomic sequences Lachnospirales is also likely a good producer of SCFAs [52]. Several members of this class have been isolated from the human gastrointestinal tract and these efforts could be used to direct future attempts to isolate members of this class from the chicken gastrointestinal tract in the future [53].

Pediococcus acidilactici has recently received a lot of attention as a potential probiotic in laying hens. P. acidilactici has been associated with enhanced egg weight, enhanced egg mass output, increased eggshell thickness [54, 55], improved FCR, and improved retention of calcium and phosphorus [56]. In the present study, we collected six P. acidilactici isolates from a single chicken, while none of the other 5 individual chickens yielded an isolate of this species – despite the same culturing techniques being applied. The fact that natural colonization by live P. acidilactici appears to be rare, combined with studies demonstrating the positive effects that this bacterium may have on productivity, may indicate that increased use of P. acidilactici probiotics may be beneficial to the poultry industry.

Most studies on commensal E. coli rely on a single isolate per individual host under the assumption that it will be representative of within-host diversity due to the clonal nature of commensal E. coli [57,58,59,60]. However, the few studies that have been carried out to investigate E. coli diversity in individual humans [61,62,63], have found multiple E. coli genogroups to be carried in the same host [58, 61]. This was indeed the finding in our study, which demonstrated substantial interhost diversity of E. coli genogroups and genomic content. In humans, when genogroup B2 is present, the host tends to have lower diversity of other genogroups because B2 has virulence associated traits which are thought to enhance its fitness [64]. The apparent dominance of the B2 genogroup was not observed in our poultry – indeed, the most genotypically diverse E. coli populations included B2.

In poultry, E. coli is generally carried as a commensal, although Avian Pathogenic E. coli (APEC) can cause colibacillosis in poultry. Colibacillosis is a serious extraintestinal infection that can cause outbreaks in poultry production facilities with up to 20% mortality [65]. The pathogenicity of APEC strains is not fully understood, and there is no single genetic determinant to identify APEC isolates but five genes including: iroN (salmochelin), iutA or aerJ (aerobactin), ompT (outer membrane protease), iss (serum resistance), and hlyF (toxin) are generally used for APEC virulotyping [66]. Virulence plasmids, often referred to as ColV plasmids, are also associated with virulence genes that contribute to APEC strains causing infections [67, 68]. In the avian intestinal tract, APEC is generally carried as a commensal and usually causes opportunistic infections that occur after other infections or environmental stress [69, 70]; although, some APEC strains may be the primary pathogen to cause disease in otherwise healthy poultry [71]. The phylogenetic groups A, A1, F, D, and B1 are the predominant genotypes for APEC strains [72] – and while several of these genotypes were isolated in our study, we did not see evidence of colibacillosis in our chickens. In the future, it would be interesting to determine, if cases in cases where APEC is the primary pathogen, if overgrowth of a single E. coli linage, and displacement of other lineages, precedes colibacillosis – and if this result could be avoided by optimizing the competitive exclusion potential of the other E. coli populations in the chicken’s gastrointestinal tract.

A limitation of the present study is that even though several culture conditions were used to increase the range of isolates collected, not every culture condition could be tested and therefore some culturable lineages, for which culture protocols exist, were still missed. We cultured very few Bacteroidota and Bacillota and no Fusobacteriota even though both were detected by shotgun metagenomics and each has been previously cultured from the poultry intestine [24]. Adding additional nutrients, such as pectin, is essential for culturing Bacteroidota and Fusobacteriota is more easily cultured in liquid culture, while our study focused on using solid agar media [73]. In addition, each of our anaerobic cultures were carried out using an atmosphere composed of 85% N2, 5% CO2, and 5%H2. We did not include any culture conditions to isolate microaerophilic bacteria or bacteria that prefer a higher CO2 environment, and are known to be present in the poultry intestine, such as Campylobacter spp.. No Archaea were isolated in this study, although they have been reported in metagenomic sequencing.

Conclusions

A thorough understanding of the composition and functional potential of the microbiota requires analysis by culture-independent and -dependent methods. While culture-independent methods can provide insight into currently uncultured bacterial populations, the techniques are biased, overlook the rare biosphere, lack information about viability, and do not produce strains for further experimentation. Having a culture collection will allow future work to elucidate the metabolic functions of specific isolates, identify genes important for host colonization, and determine which are the most effective for providing competitive exclusion against bacterial pathogens. This study has produced the largest culture collection of bacterial isolates from healthy chicken fecal samples to date and these isolates will be used as a resource to enhance understanding of this important environment.

Methods

Ethics statement

This project and all animal handling procedures were approved by the Faculty of Agricultural and Environmental Sciences Animal Care Committee under protocol number 2020–8186.

Sample collection

Fecal samples were collected from six apparently healthy laying hens in Ontario, Quebec, Manitoba, Saskatchewan (× 2), and Alberta (Table S2). Sample collection packages were mailed to selected egg producers with detailed instructions on collecting samples. Producers were instructed to collect fecal samples by placing a chicken on a clean sterile surface in a contained environment and to wait for the chicken to defecate naturally. After defecation they were instructed to split the feces into six approximately equal aliquots using a sterile spatula and an aliquot was to be used to inoculate each of the six transport media: 1/2 Tryptic Soy Broth (TSB) (BD Company), Amies Transport Media (Thermo Fisher Scientific), thioglycolate medium with Hemin and Vitamin K (Anaerobe System), anaerobic tissue transport medium surgery pack (Anaerobe System), RNA later (Thermo Fisher Scientific), and BioFreeze (Alimetrics). Each sample was mixed well, placed into a containment bag, and sent to the lab using an overnight delivery service. Upon arrival at the lab a 1 mL aliquot of each shipment condition was mixed with an equal volume of 1:7 glycerol to PBS and stored at −80 for future use. The samples contained in 1/2 TSB and amies transport medium were combined for aerobic culture, and the samples contained in thioglycolate medium and anaerobic tissue transport medium were combined for anaerobic culture. The design of workflow for deep culturing is provided in Figure S5.

Culture conditions used to isolate aerobic bacteria

The media and deep culturing approach was adapted from Lagier et al., 2016 [31]. Samples shipped in aerobic shipping media were cultured by mixing the samples and then combining 1.4 mL of mixed-sample into phosphate buffered saline (PBS) to a dilution of 10−3, 10−5 and 10−7 and then spread-plating 200 uL of each dilution onto each of Brain Heart Infusion Agar (Thermo Fisher Scientific), Brain Heart Infusion Agar with 15 g/ml NaCl, Brucella Agar (BD Company), Drigalski Lactose Agar (Thermo Fisher Scientific), EMB Agar (BD Company), Glucose Bromcresol Purple Agar (MilliporeSigma), Hektoen Enteric Agar (HiMedia), Legionella CYE Agar (Thermo Fisher Scientific), MacConkey Agar (BD Company), Marine 2216 Agar (BD Company), Modified Letheen Agar (Hardy Diagnostics), Mueller Hinton Agar (BD Company), Orange Serum Agar (Hardy Diagnostics), R2A Agar (BD Company), Regan-Lowe Agar (BD Company), Salmonella Shigella Agar (BD Company), Sheep Blood Agar (BD Company), Thiosulfate-Citrate-Bile-Sucrose Agar (BD Company), and Yersinia Selective Agar (Thermo Fisher Scientific) prepared as per the manufacturer’s instructions. Each plate was incubated at 37 °C for 48 h.

Culture conditions used to isolate anaerobic bacteria

Samples shipped in anaerobic shipping media were cultured mixing the samples and then combining 1.4 mL of mixed-sample into PBS to a dilution of 10−3, 10−5 and 10−7 and then spread-plating 200 uL of each dilution onto each of Drigalski Lactose Agar, EMB Agar, Glucose Bromcresol Purple Agar, Hektoen Enteric Agar MacConkey Agar, Marine 2216 Agar, Modified Letheen Agar, R2A Agar, Orange Serum Agar, Schaedler Agar (BD Company), Sheep Blood Agar, Thioglycolate Agar (HiMedia), Thiosulfate-Citrate-Bile-Sucrose Agar, Wilkins-Chalgren Anaerobic agar (Thermo Fisher Scientific), and Yersinia Selective Agar. Each plate was incubated in anaerobic chamber (85% N2, 5% CO2, and 5% H2 (Linde Canada)) or an anaerobic jar with anaerobic pack at 37 °C for 48 h.

Enrichment culture conditions

The mixed aerobic samples were also enriched by inoculating 5 mL into each of two tubes of each of Plus Aerobic/F Medium (BD Company) containing 5% defibrinated sheep blood (Cedarlane) and 5% rumen fluid (Fisher Scientific). One of the two tubes was incubated at 80 °C for 1 h before being incubated at 37 °C to select spore-forming bacteria, while the second tube was directly incubated at 37 °C. At 3, 7, 14, 21, and 28 days of enrichment, 1 mL of each enrichment culture was removed, diluted to 10−3, 10−5 and 10−7 in PBS, and spread plated onto each of aerobic media used above for aerobic growth. The heat-treated enrichment culture was diluted to 10−3, 10−4 and 10−5 in PBS and spread plated into Sheep Blood Agar, Brain Heart Infusion Agar, Modified Letheen Agar, Glucose Bromcresol Purple Agar and incubated at 37 °C for 48 h.

To enrich samples shipped in anerobic culture media, all manipulations and incubations were carried out in an anaerobic chamber in 85% N2, 5% CO2, and 5% H2. The mixed anaerobic samples were enriched by inoculating 5 mL into each of two Lytic Anaerobic Medium bottles (BD Company). One of the two bottles was incubated at 80 °C for 1 h before being incubated at 37 °C to select spore-forming bacteria, while the second tube was directly incubated at 37 °C. At 3, 7, 14, 21, and 28 days of enrichment, 1 mL of the enrichment was plated onto each anaerobic media listed above for anaerobic growth. The heat-treated enrichment culture was diluted to 10−3, 10−4 and 10−5 in PBS and spread plated onto Brain Heart Infusion Agar, Modified Letheen Agar, Wilkins-Chalgren Anaerobic Agar, and Glucose Bromcresol Purple Agar.

After incubation on agar each single, unique, and well-isolated colony was streaked for isolation onto the same agar media that was used to produce the colony and incubated at 37 °C for 48 h. After incubation, a single well isolated colony was chosen to inoculate 200 uL of TSB and was cultured for 48 h at 37 °C. After incubation, the liquid culture was mixed with 200 uL v/v 50% glycerol in sterilized water and stored at − 80 °C.

Isolate identification

Each isolate was streaked onto a Tryptic Soy Agar (TSA) plate directly from the − 80 °C stock culture. The TSA streak plate was incubated at 37 °C for 48 h. An anaerobic chamber with a gas mix of 85% N2, 5% CO2, and 5% H2 was used to manipulate and incubate anerobic bacteria. A single, well isolated colony was selected with toothpick and then plated on MALDI-TOF target plate. Matrix solution (1 μL) (10 mg α-cyano-4-hydroxycinnamic acid (CHCA), 25ul TFA, 500ul acentonitrile, 475ul molecular grade water) was added to the colony. All target plates were analyzed using the Ultraflextreme MALDI-TOF/TOF mass spectrometer (Bruker) at the Research Institute of McGill University Health Centre, Drug Discovery Platform within 24 h. The MBT Compass software was used to identify the isolate. A species level identification was reported if the Score Value for the best match organism was ≥ 2.0 or if the best match and the second best match both identified the same species and both had a Score Value of > 1.7; otherwise, a genus was reported for isolates that had a Score Value of > 1.7 [74].

Isolates that could not be accurately identified at the species level using MALDI-TOF MS were identified using 16S rRNA Sanger sequencing. Briefly, the isolate was grown from the − 80 °C stock culture 37 °C on TSA for 24 h. A single, well isolated colony was suspended in PBS. A freeze-heat method where cells are heated to 100 °C for 5 min, stored at − 80 °C for 30 min, and re-heated to 100 °C for 5 min was used to lyse cells and release DNA. The 16S rRNA DNA was then amplified with using the 8F and 1492R primers, and the amplicon was Sanger sequenced in the forward and reverse directions using the same primers at the Laval University sequencing facility.

DNA Extraction

Upon arrival at the lab, samples shipped in RNA later or BioFreeze were centrifuged at 16,100 × g for 10 min. The supernatant was discarded, and the pellet was stored at − 80 °C until DNA extraction. The DNA was extracted using the Power Fecal Pro DNA extraction kit (Qiagen) using 0.25–0.3 g of sample and according to the manufacturer’s instructions. Purified DNA was stored at − 80 °C.

16S rRNA targeted amplicon sequencing and analysis

The V4 hypervariable region of the bacterial 16S rRNA gene was amplified from the extracted bacterial DNA using PCR with the F515 and R806 primer pair and sequenced using Illumina MiSeq platform using pared end sequencing (2 × 251 bp) (Illumina Inc.) [75]. The HotStartTaq® Plus Master Mix Kit (Qiagen) was used for PCR and the amplification cycle included initial denaturation at 95 °C for 5 min, 35 cycles of denaturation at 95 °C for 30 s, annealing at 50 °C for 30 s, extension at 72 °C for 1 min, and final extension at 72 °C for 10 min. The amplicon libraries were purified using Agencourt AMPure® XP (Beckman Coulter, Brea, CA, USA) as per the manufacturer’s instructions and quantified using Invitrogen™ Quant-iT™ dsDNA Assay Kit. The libraries were then normalized and pooled followed by sequencing using the MiSeq reagent kit V2 (Illumina Inc.) for 502 cycles (2 × 251 bp) and the MiSeq benchtop sequencer (Illumina Inc.).

FASTQ files were generated from the Illumina Miseq sequencer and processed using DADA2 pipeline (v1.29.0) following the authors’ recommendations [76]. Briefly, the sequences were trimmed at 200 bp (forward) and 240 bp (reverse). All the samples were sent for error rate study, and 44,326,480 total bases in 260,744 sequence reads were used for forward sequence error rate and 52,670,288 total bases in 260,744 sequence reads were used for reverse sequences error rate study. Based on the error rate, the reads were filtered before being used for further analysis. Forward and reverse samples were merged as contigs. After removing chimeras, the reads were assigned to a taxonomy using the SILVA database (v138.1). Then this data was analyzed with MicrobiomeAnalyst [77].

Metagenomic sequencing and analysis

Purified DNA was amplified and prepared for shotgun metagenomic sequencing using the Nextera XT library preparation kit (Illumina) according to the manufacturer’s instructions. The prepared DNA samples were sequenced on the Illumina NovaSeq platform using paried-end (PE150) reads at the McGill Genome Center.

The sequencing reads were analyzed with biobakery workflow [78] with MetaPhlAn (v4.0.3), KneadData (v 0,12,0), and Trimmomatic (v 0.39). Host genome reference database was created with bowtie2, containing human genome hg38 from USUC, and chicken genome Galgal4 from Ensembl. Then, the results of metaphlan analysis were merged, transformed into the phyloseq data format, and visualized using the MicrobiomeAnalyst server [77]. To visualize the well-classified taxa, we used Metacoder (v 0.3.6) [79] with package phyloseq (v 1.42.0) and ggplot (v 3.4.4)) in R studio (v 4.2.2) to create a heat tree at the genus level. Ambigious taxa names containing numbers, special characters or irregular naming conventions were excluded.

Comparison of taxonomic analysis

The taxonomic results of shotgun metagenomic sequencing at the species level, from MetaPhlAn analysis, were extracted and compared with the results from culturomics. At the species level, all the metagenomic taxa were included, regardless of naming ambiguites, aiming to capture the full dataset and direct compare the taxa. The differences of species distribution between shotgun metagenomic sequencing and deep culturing were visualized via Metacoder (v 0.3.6) [79] (with package phyloseq (v 1.42.0) and ggplot (v 3.4.4)) in R studio (v 4.2.2).

Whole genome sequencing and analysis of E. coli Isolates

A total of 117 E. coli isolates from different samples and isolated through different isolation methods were selected for whole genome sequencing (15–20 isolates from each sample). The isolates were recovered from the − 80 °C stock on TSA and incubated at 37 °C for 18 h. A single well isolated colony was collected and re-suspend in 300 uL lysis buffer (Promega) and with Proteinase K (Promega). The solution was incubated at 37 °C for 20 min with vortexing twice during the incubation period, and then the solution was transferred to the correct well of a cartridge, and DNA was purified using the Maxwell RSC Blood DNA Kit and the Maxwell® RSC instrument (Promega) following the manufacturer’s instructions. The quality and quantity of DNA were examined using a NanoDrop spectrophotometer (Thermo Fisher) and Quant-iT dsDNA High-Sensitivity Assay Kit (Invitrogen).

The purified DNA was used with the Nextera DNA Flex Prep kit for library preparation following the manufacturer’s instructions (Illumina, USA). Libraries were sequenced using the MiSeq benchtop sequencer (Illumina, USA) and the MiSeq Reagent Kit v3 (2 × 300 bp).

Raw sequence reads were uploaded to the Compute Canada Beluga Server for analysis. Adapters and low-quality reads (average score of 20 at least in any 5-base window, with function SILDINGWINDOW: 5: 20) were removed with Trimmomatic (v0.39) [80]. Sequence quality was checked with FastQC before and after trimming [81, 82]. Sequences were assembled with SPAdes de novo assembler [83] and contigs smaller than 1 kb were removed with bbmap tools reformat [84], and quality was assessed with Quast [85] and BBmap [84]. Assembled contigs were checked for genome completeness and inter-species contamination using CheckM [86] Bacterial species identification was confirmed with the whole genome sequence using GTDB-tk [87]. Each whole genome sequence that was confirmed as E.coli was assembled into a cluster map using ANIclustermap [88]. We used 99.9% as the differential threshold to classify identical isolates. A DNA-DNA hybridization tree was generated using 12 reference genomes, and one Escherichia fergusonii genome downloaded from NCBI (Table S5). All the genomes were submitted to the TYGS for digital DNA-DNA hybridization and whole-genome-tree-making [38]. The Python package ETE 3 [89] was used to regenerate the mapping tree and differentiate the group with different colours based on the study of E. coli DNA-DNA hybridization grouping with different E.coli reference genomes [36]. The annotation results were used to identify genes iroN (salmochelin), iutA or aerJ (aerobactin), ompT (outer membrane protease), iss (serum resistance) and hlyF (toxin) genes, which related to the potential pathogenicity.

Availability of data and materials

Shotgun metagenomic sequencing, 16S rRNA TAS, and E. coli whole genome sequence reads were submitted to the Sequence Read Archive (SRA) under Bioproject PRJNA1143568.

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Acknowledgements

We would like to thank Ministère de l’Agriculture, des Pêcheries et de l’Alimentation (MAPAQ), The McGill Sustainability Systems Initiative (MSSI), and Egg Farmers of Canada for providing the financial support for this work. We also want to thank the individual Egg Farmers who provided the samples to enable this study.

Funding

We would like to thank Ministère de l’Agriculture, des Pêcheries et de l’Alimentation (MAPAQ), The McGill Sustainability Systems Initiative (MSSI), and Egg Farmers of Canada for providing the financial support for this work. The funding organizations were not involved in the design, analysis, or interpretation of data; nor were they involved in writing the manuscript.

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  1. Faculty of Agricultural and Environmental Sciences, Macdonald Campus, Mcgill University, Montreal, QC, Canada

    Zhixuan Feng, Natalia Lorenc, Bridget O’Brien, Guangwen Sun, Zhiwei Li, Dongyun Jung & Jennifer Ronholm

Authors
  1. Zhixuan Feng
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  2. Natalia Lorenc
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  3. Bridget O’Brien
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  4. Guangwen Sun
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  5. Zhiwei Li
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  6. Dongyun Jung
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  7. Jennifer Ronholm
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Contributions

Funding was acquired by JR. JR and ZF conceived and designed the experimental work. ZF, NL, BO, GS, ZL, and DJ each participated in experimental work as well as analysis and interpretation of data. ZF and JR wrote the original draft of the manuscript. JR supervised the work. All authors read, revised, and approved the submitted version. Each author agreed both to be personally accountable for the author’s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature.

Corresponding author

Correspondence to Jennifer Ronholm.

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Ethics approval and consent to participate

This project and all animal handling procedures were approved by the Faculty of Agricultural and Environmental Sciences Animal Care Committee under protocol number 2020–8186.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Feng, Z., Lorenc, N., O’Brien, B. et al. Deep culturing the fecal microbiota of healthy laying hens. anim microbiome 7, 32 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s42523-025-00395-y

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s42523-025-00395-y

Keywords

Animal Microbiome

ISSN: 2524-4671