Skip to main content
NCBI home page
MicrobiologyOpen logoLink to MicrobiologyOpen
. 2020 Oct 9;9(11):e1119. doi: 10.1002/mbo3.1119

Dynamic change of the gastrointestinal bacterial ecology in cows from birth to adulthood

Chun Y Guo 1,2,3, Shou K Ji 2,4, Hui Yan 2,4, Ya J Wang 2, Jing J Liu 2, Zhi J Cao 2, Hong J Yang 2, Wen J Zhang 1,✉, Sheng L Li 2,✉
PMCID: PMC7658451  PMID: 33034165

Abstract

The gut microbiota plays multiple critical roles in maintaining the health of the host, especially in ruminants. However, our understanding of the establishment of gut microbiota from birth to adulthood is still limited. To address this, the bacterial ecology of the rumen, abomasum, duodenum, and rectum in Holstein cows ranging in age from 1 week to 5 years old was investigated using 16S rRNA gene sequencing in this study. A major change in the composition, diversity, and abundance of bacteria was observed with increased age (p < 0.05). Microbiota gradually matured in each gut segment and followed the Gompertz model when the Chao1, Shannon, and maturity indexes (p < 0.05, r > 0.94) were applied. Importantly, the Gompertz model parameter differed between the gut segments, with the highest microbiota growth rate found in the rectum, followed by the rumen, abomasum, and duodenum. Compared to older animals, greater microbiota similarities were found in the adjacent gut segments of younger animals (p < 0.05). Our findings indicate that gut microbiotas are established quickly when cows are young and then slow with age and that early in life, hindgut microbiota may be more easily affected by the foregut microbiota.

Keywords: bacterial ecology, cows, maturity, microbiota establishment

Microbiota gradually matured followed the Gompertz model in each gut segment (rumen, abomasum, duodenum, and rectum) of cows. Microbiota in the rectum matured earliest followed by that in abomasum and rumen, and microbiota in the duodenum matured latest.

graphic file with name MBO3-9-e1119-g010.jpg

1. INTRODUCTION

Gut microbiota has been demonstrated to be important for host health, especially in ruminants such as cows that rely on gut fermentation to convert indigestible plant biomass into food products (Hess et al., 2011; Rubino et al., 2017; Shabat et al., 2016). Considerable research has focused on bovine intestinal microbial composition (Durso et al., 2010; Malmuthuge, Li, Goonewardene, Oba, & Guan, 2013; Uyeno, Sekiguchi, & Kamagata, 2010), compositional differences in different gut segments (Frey et al., 2010; Gu et al., 2013; Mao, Zhang, Liu, & Zhu, 2015; de Oliveira et al., 2013), and the functions of the gut bacterial community (Popova et al., 2017). These previous studies have expanded our knowledge of the gut bacterial community.

The gastrointestinal microbiota is a dynamic system that changes with host development (Dill‐McFarland, Weimer, Breaker, & Suen, 2019; Subramanian et al., 2014). It is believed that the gut microbiota of animals is established by introducing xenomicrobiota from their surroundings (Backhed et al., 2015; Guzman, Bereza‐Malcolm, De Groef, & Franks, 2015; Koenig et al., 2011), as animals are exposed to complex environments and microbiota since birth (Jin, Wu, Zeng, & Fu, 2017; Ren et al., 2017; Tun et al., 2017). In general, the gut microbiota increases in abundance from birth to adulthood and becomes an adult‐like microbiota with increasing age (Backhed et al., 2015; Faith et al., 2013; Rawls, Mahowald, Ley, & Gordon, 2006; Subramanian et al., 2014; Yassour et al., 2016). In previous studies on ruminants, an increase in the alpha diversity and changes in the predominant bacterial taxa have been observed as animals increase in age (Dias et al., 2018; Dill‐McFarland, Breaker, & Suen, 2017; Jami, Israel, Kotser, & Mizrahi, 2013; Yeoman et al., 2018). Although this notion of a gradual establishment of the gut bacterial community is well accepted, the details of this process remain to be elucidated.

Our study aimed to explore the gradual establishment of gut bacterial ecology in cows from birth to adulthood by analyzing the 16S rRNA gene sequences of microbiota from different gut segments, that is, rumen, abomasum, duodenum, and rectum.

2. MATERIALS AND METHODS

2.1. Sample collection

Experiments were performed at the Zhong Di breeding stock dairy farm located in Beijing, China. Fifty‐eight healthy female Holstein cattle were enrolled in the study, with ages ranging from 1 week to 5 years old. The cattle were reared under the following conditions and were weaned at 2 months old: 1‐week‐old calves (±3 days; n = 12) had free access to milk provided in buckets; 1‐month‐old calves (±3 days; n = 8) had free access to milk replacer provided in buckets and a solid starter diet (granules: flaked corn, 3:2); 2‐month‐old calves (±3 days; n = 8) had free access to milk replacer provided in buckets, solid starter diet, and hay (50% alfalfa and 50% oats); and 6‐month‐old (±3 days; n = 11), 1‐year‐old (±7 days; n = 5), 2‐year‐old (±7 days; n = 8), and 5‐year‐old (±7 days; n = 6) Holstein cows had free access to a total mixed ratio (TMR) consisting of 40% concentrated feed and 60% roughage. Each animal of the same age group was reared in individual pens in the same manner with free access to freshwater for 14 days and then fasted for 12 h before harvest.

The calves and cows were euthanized by intravenous injection of Euthanyl (240 mg/ml; Sigma‐Aldrich, Castle Hill, New South Wales, Australia). Each gastrointestinal compartment (rumen, abomasum, duodenum, and rectum) was isolated with sterile surgical thread to avoid the contents mixing. Digesta samples (2 g) from the rumen, abomasum, duodenum, and rectum were collected. However, due to contamination during sampling, there are no abomasum samples from 1‐week‐old calves. In total, 202 samples were collected from 58 animals. All samples were immediately frozen in liquid nitrogen before being analyzed.

2.2. DNA extraction and PCR amplification

Genomic DNA was extracted from 0.5 g of digesta samples using the E.Z.N.A.® Stool DNA Kit (Omega Bio‐Tek, Norcross, GA, U.S.) following the manufacturer's instructions. The quality of the extracted DNA was detected using 1% agarose gel electrophoresis and spectrophotometry (optical density at 260/280 nm ratio). The extracted DNA was stored at −20°C until further analysis. The V3–V4 regions of the 16S rRNA genes were amplified with the universal primers 341F (5′‐CCTAYGGGRBGCASCAG‐3′) and 806R (5′‐GGACTACNNGGGTATCTAAT‐3′). These primers also contained a set of 8‐nucleotide barcode sequences that were unique to each sample. PCRs were performed in triplicate using a 25‐μl mixture containing 2.5 μl of 10× Pyrobest Buffer, 2 μl of 2.5 mM dNTPs, 1 μl of each primer (10 μM), 0.4 U of Pyrobest DNA Polymerase (TaKaRa, Kyoto, Japan), and 15 ng of template DNA. The PCR protocol used was as follows: 95°C for 5 min; 25 cycles at 95°C for 30 s, 55°C for 30 s, and 72°C for 30 s; with a final extension of 72°C for 10 min.

2.3. Illumina MiSeq sequencing and data processing

Amplicons were extracted from 2% agarose gels and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, U.S.A) according to the manufacturer's instructions. They were then quantified using QuantiFluor™‐ST (Promega, Madison, WI, U.S.A). The amplicons of the V3–V4 hypervariable regions of the 16S rRNA genes were then sequenced using the Illumina Miseq PE300 sequencing platform (Illumina, Inc., San Diego, CA, U.S.A) by Beijing Allwegene Tech, Ltd (Beijing, China).

Raw sequences were assigned to each sample by barcodes, and low‐quality sequences were then filtered. The following were considered “low‐quality sequences”: (a) raw reads that were shorter than 110 nucleotides; (b) 300‐bp reads that were truncated at any site and received an average quality score <20 over a 50‐bp sliding window, and truncated reads that were shorter than 50 bp; and (c) exact barcode matching, two nucleotide mismatches in primer matching, and reads that contained ambiguous characters. Only clean sequences with an overlap longer than 10 bp were assembled using FLASH‐1.2.11 (Magoc & Salzberg, 2011). Reads that could not be assembled were discarded. Chimera sequences were detected using usearch6.1 (Edgar, 2010). Next, high‐quality sequences were then analyzed using the QIIME pipeline (Caporaso et al., 2010). The representative sequence datasets were classified into operational taxonomic units (OTUs) using a threshold of 97% identity and the UCLUST algorithm (Edgar, 2010). The taxonomy of each 16S rRNA gene sequence was assigned by UCLUST against the GreenGenes database (McDonald et al., 2012).

2.4. Maturity of gut microbiota

The maturity index is defined as the similarity between the gut microbiota of a young individual to that of an adult (Subramanian et al., 2014). Here, we assumed that the microbiota in adult cows at 5 years old was fully developed; thus, we could infer that gut microbiota more similar to that of adult cows (5 years old) was more developed. The maturity index was calculated using the modified Bray–Curtis similarity algorithm: Maturity index = 1‐∑i=1nxij‐xik/∑i=1nxij+xik, where x is the bacterial abundance, n is the number of identified OTUs in each gut segment, i is the list of OTUs, j is the given sample for which the maturity index was measured, and k is the sample of an adult cow (5 years old).

2.5. Gut microbiota index change fitted to the Gompertz model

The Gompertz model is a nonlinear regression analysis algorithm that is widely used to describe the dynamic changes of S‐shape data, such as growth curves. Chao1, Shannon, and maturity indexes were established by age (weeks) with the Gompertz model: index = a * exp(−b * exp(−k * age)), where “index” is the Chao1, Shannon, or maturity index observed; parameter a represents the asymptote of expected top index; parameter b is an integration constant related to the index; and parameter k is the maturity rate of the Chao1, Shannon, or maturity index.

2.6. Data analysis

The diversity matrix was calculated using the QIIME pipeline (Caporaso et al., 2010). A Venn plot was constructed, and hypergeometric tests were performed to present the distribution differences in the core bacterial community using the VennDiagram package (1.6.17); the Bray–Curtis index was calculated using the Vegan package (2.4‐5), and a correlation test was performed with the cor.test function in R software (3.3.0). Comparisons between groups were performed using two‐way ANOVA with the default parameters in the R software (3.3.0), and a p‐value <0.05 was considered significant.

3. RESULTS

3.1. Datasets

Two hundred and two samples (Table A1) were collected in the current study. After sequencing 16S rRNA genes on the Miseq platform and performing quality control, 10,781,199 clean sequences (53,372 ± 33,137 sequences per sample) were obtained. Based on 97% nucleotide sequence similarity, these sequences were assigned to OTUs, which were used to identify different taxonomic levels according to the GreenGenes database. In total, 13,827 OTUs, 508 genera, 174 families, 96 orders, 55 classes, and 28 phyla were identified from this dataset.

3.2. Bacterial composition was affected by both gut segment and cow age

Based on the established dataset, the majority of the detected sequences in all samples belonged to Firmicutes (~52.14%), Bacteroidetes (~30.72%), Proteobacteria (~6.32%), and Actinobacteria (~3.76%). These four major phyla contributed to almost 93% of total bacteria in terms of abundance, and the relative abundance of these major bacterial taxa was affected by both the gut segment they were found in and the age of the cows (p < 0.05, Figure 1).

FIGURE 1.

FIGURE 1

Open in a new tab

Change in the bacterial composition of predominant phyla in different gut segments in cows with age. The top four phyla with a mean abundance higher than 2.0% are shown (Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria). p‐value of the fixed effects of age, gut segment, and age × gut segment was lower than 0.05

Only 170 of the 13,827 OTUs were identified to species level, and most of these identified species (>98%) were present in less than 50% samples. Thus, genus‐level identification was used to further explore the bacterial taxa changes in different gut segments of cows. Three hundred and fifty‐five genera were identified in the rumen, 329 in the abomasum, 404 in the duodenum, and 268 in the rectum. Among these, 85 genera in the rumen, 131 in the abomasum, 108 in the duodenum, and 93 in the rectum were present in more than 50% of the samples and were considered as the core bacterial taxa for each gut segment. Moreover, the relative abundance of the identified core genera in each gut segment changed as the calves aged (Figure A1a; p < 0.05). Furthermore, only a small proportion of the core genera (44/183) were shared across different gut segments, and the distribution of these core genera varied between different gut segments (Figure A1b; hypergeometric test, p < 0.05). These observations at the phylum and genus level indicate that bacterial composition may be affected by both the gut segment and the age of the cow.

3.3. Bacterial alpha diversity changed with age following a Gompertz curve

Both the Chao1 (which reflects the number of expected species) and Shannon indexes (which accounts for both the abundance and evenness of the species presented in a given community), which were used to assess the alpha diversity change, were affected by age (p < 0.05) and increased in each gut segment of the cows with age (Figure 2).

FIGURE 2.

FIGURE 2

Open in a new tab

Change of alpha diversity with increasing age in different gut segments of cows. Alpha diversity was calculated via the Chao1 and Shannon indexes. p‐value of the fixed effects of age, gut segment, and age × gut segment was lower than 0.05

The linear, quadratic, and Gompertz models were used to fit the alpha diversity indexes to the age (in weeks) of the cows. The Gompertz model had the best fit for Chao1 and Shannon indexes for most gut segments based on the Akaike information criterion and Bayesian information criterion (Table A2; Figure A2). The observed indexes and the predicted indexes by the Gompertz model were highly correlated, indicating that the Gompertz model can be used to describe the growth of gut microbiota in the current study (Figure A3; Table A3; r > 0.94, p < 0.01).

By establishing the Gompertz model, the rate at which the bacterial community matured in each bovine gut segment can be found. First, in terms of the mature microbiota of the 4 gut segments, the rumen had the highest alpha diversity (3528 and 9.19 for the Chao1 and Shannon indexes, respectively), followed by the abomasum and duodenum, while the rectum had the lowest alpha diversity (1630 and 7.22 for the Chao1 and Shannon indexes, respectively) (Figure 3a,b). Different growth rates (parameter k) of alpha diversity were also found in the gut segments. The microbiota in the rectum matured faster and earlier in life than that in other segments. Based on the Chao1 and Shannon indexes, microbiota in the rectum reached 80% microbiota maturity (close to full maturity) the quickest at 10–20 weeks, followed by the rumen, the abomasum, and the duodenum, which needed substantially more time to reach 80% microbiota maturity (Table A4). The inflection point of the Gompertz model represents the time of the highest microbiota growth rate during maturity, with the inflection of the Chao1 index in the rectum occurring much earlier in life (at 4 weeks old) than in other segments. The inflection point of the Shannon index in each gut segment had a negative value, which indicates that while the Shannon index value infers fast growth early in life, the growth rate gradually decreased with increasing age (Table A4).

FIGURE 3.

FIGURE 3

Open in a new tab

Dynamic change of bacterial alpha diversity and maturity index with increasing age fitted to the Gompertz function. (a) Dynamic change of the Chao1 index. (b) Dynamic change of the Shannon index. (c) Dynamic change of maturity index. Solid lines are the fitted Gompertz curves, and dashed lines are the expected asymptotes. Detailed information is shown in Tables A2, A3, A4, and Figures A2 and A3 for the Chao1 and Shannon indexes, and more detailed information is shown in Tables A5 and A6, and Figure A4 is for the maturity index

3.4. Microbiota maturity changed with age following the Gompertz curve

The maturity index of microbiota, which was defined as the similarity (Bray–Curtis matrix) between the gut microbiota of young and adult cows (5 years old) at the OTU level, changed in every gut segment as cows aged, following the Gompertz model (Figure A4a). Additionally, the Gompertz model fit the maturity index well because the maturity index predicted by the Gompertz model was highly correlated with the observed maturity index (Figure A4b; Table A5; r > 0.98, p < 10−4).

Under the parameters observed for the Gompertz model of the maturity index, the growth rate of the microbiota maturity index varied between the different gut segments (Figure 3c). Although the parameters of the Gompertz model at maturity were not consistent with those of alpha diversity, a similar trend was observed (Table A6). Microbiota maturity in the rectum was expected to have the fastest growth rate because it had the highest k parameter value, followed by the abomasum and the rumen, with the duodenum having the lowest growth rate. The different growth rates were also supported by the predicted age to reach 80% maturity and the inflection age (Table A6). Our findings concerning both the dynamic change of microbiota maturity and alpha diversity indicate that the gastrointestinal bacterial community changed more quickly in younger cows than in older animals. Importantly, the growth rate of the bacterial community in different gut segments also differed, with the highest growth rate found in the rectum and the lowest rate in the duodenum. The bacterial community in the rectum also matured earliest, followed by the rumen, the abomasum, and, lastly, the duodenum.

3.5. Dynamic changes of signature taxa in different gut segments

The relative abundance of the identified core bacterial taxa was fitted to the predicted microbiota maturity in each gut segment to illustrate the dynamic change of bacterial taxa. In the rumen, 22 genera were positively correlated (r > 0.43), and 1 genus (Desulfovibrio) was negatively correlated (r = −0.54) with the predicted microbiota maturity index (p < 0.05; Figure 4a). In the abomasum, 22 genera were positively correlated (r > 0.53), and 5 genera were negatively correlated (r < −0.54) with predicted microbiota maturity index (p < 0.05; Figure 4a). In the duodenum, all detected genera (16 genera) were positively correlated (r > 0.48) with the predicted microbiota maturity index (p < 0.05; Figure 4a). In the rectum, 32 genera were positively correlated (r > 0.43), and 4 genera were negatively correlated (r < −0.47) with the predicted microbiota maturity index (p < 0.05; Figure 4a). Furthermore, the core bacteria which were correlated with microbiota maturity had a little overlap between the different gut segments, with only the relative abundance of Ruminococcaceae_UCG‐010 in all the gut segments fitting the Gompertz model (Figure 4b). These results showed that the bacterial composition in each gut segment dynamically changed as calves aged, and the bacterial taxa that contributed to microbiota maturity also differed between gut segments.

FIGURE 4.

FIGURE 4

Open in a new tab

Core bacterial taxa at the genus level that contributed to microbiota maturity in different gut segments. (a) Dynamic change of core genera that contributed to microbiota maturity in the rumen, abomasum, duodenum, and rectum with increasing age (p < 0.05). The warmer colors indicate a higher bacterial abundance, while cooler colors indicate a lower bacterial abundance. (b) Shared core genera that contributed to microbiota maturity in the rumen, abomasum, duodenum, and rectum (hypergeometric test, p < 0.05)

3.6. The similarity of bacterial communities in the foregut and hindgut

The bacterial community in the hindgut may be affected by the community in the foregut because of bacterial transfer (Ji et al., 2018). This may explain the similarity between the bacterial compositions in the foregut and hindgut, as active bacterial transfer between the two habitats would result in similar bacterial communities (Jonathan & Robert, 2010). By analyzing the similarity indexes at the OTU level using the Bray–Curtis index, this study found that the bacterial communities in the rumen and abomasum were similar in 1‐, 2‐, and 6‐month‐old calves, but different to those of 1‐, 2‐, and 5‐year‐old cows (p < 0.05). Similar results were also observed between bacterial communities in the abomasum and duodenum (p < 0.05), although the similarity indexes in the duodenum and rectum were not affected by the cows' ages (p > 0.05; Figure 5). These observations of bacterial community similarity and differences in the foregut and hindgut indicate that the similarity in bacterial communities in the foregut and hindgut may be affected by age; with increasing age of a cow, bacterial community similarity in the foregut and hindgut decreased, especially from 6‐month‐old to 1‐year‐old.

FIGURE 5.

FIGURE 5

Open in a new tab

Bray–Curtis similarity of microbiota in the foregut (rumen) and hindgut (rectum) of cows. ***p < 0.05

4. DISCUSSION

The gut microbiota has been demonstrated to be important to the host (Delzenne & Bindels, 2018), especially in ruminants such as cows (Hess et al., 2011; Rubino et al., 2017). Previous studies have shown that bacteria colonized the rumen by the first day after birth, though the rumens of preweaning calves were not well developed (Guzman et al., 2015; Jami et al., 2013; Yeoman et al., 2018), and microbial succession in the rumen and gut then becomes gradually established as cows aged (Dill‐McFarland et al., 2017). However, the process of microbiota establishment was still not fully understood. Therefore, the purpose of the current study was to elucidate this issue from an ecological perspective.

Different gut segments (rumen, abomasum, duodenum, and rectum) in cows were used to explore the establishment of the microbiota because spatial heterogeneity in the microbial community along the gut has been previously demonstrated in mice (Gu et al., 2013; Wang et al., 2010), chickens (Rehman, Vahjen, Awad, & Zentek, 2007), pigs (Looft et al., 2014), humans (Costello et al., 2009), and cows (Godoy‐Vitorino et al., 2011; Yeoman et al., 2018). Although the majority of bacteria in different gut segments belonged to just a few taxa (such as the Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria phyla), we observed significant bacterial community differences in terms of alpha diversity, composition, and abundance of dominant and core bacteria between different gut segments, and between cows with different ages. These results are also supported by previous studies in cows (Jami et al., 2013; Li, Connor, Li, Baldwin, & Sparks, 2012) and humans (Koenig et al., 2011; Subramanian et al., 2014; Yatsunenko et al., 2012) and suggest that the relative abundance of bacterial taxa may be affected by both the specific gut segment and the age of the cow. Furthermore, the establishment of bacterial ecology in different gut segments may differ in growth rate (Dias et al., 2018; Yeoman et al., 2018), which can be effectively described from an ecological perspective (Subramanian et al., 2014).

Bacterial alpha diversities, as described by the Chao1 and Shannon indexes, were mostly used to assess the establishment of the gut bacterial community (Guzman et al., 2015; Jami et al., 2013; Yeoman et al., 2018). The maturity index, which also reflects the establishment of the gut bacterial community, is defined as a measure of the similarity between the gut microbiota of calves and that of adult cows (Subramanian et al., 2014). We found that successive gradual change of both alpha diversity and maturity indexes for each gut segment with age followed the Gompertz curve, with gut microbiota established quickly in young animals, then slowing with age. Previous studies detected two pivotal times in the establishment of microbiota in the mammalian gut: The first is at birth when the mammalian gut captures bacteria from the vagina, colostrum, and its surrounding environment to establish its bacterial community (Guzman et al., 2015; Koenig et al., 2011). The second is at weaning, when animals transition from liquid food to solid food, and the gut microbiota shifts quickly to become that of an adult's (Favier, Vaughan, De Vos, & Akkermans, 2002). In humans, the gut microbiota establishes quickly before 6 months of age (Yassour et al., 2016). The gut microbiota of 12‐month‐old infants is similar to those of their mothers (Backhed et al., 2015), whereas the gut microbiota of 3‐year‐olds resembles that of adults (Faith et al., 2013; Yatsunenko et al., 2012). Furthermore, with the parameters of the fitted Gompertz function, we also observed that the growth rates of bacterial communities differed between gut segments, with the highest growth rate found in the rectum. Thus, it can be inferred that the bacterial community in the rectum matured early in life, followed by the rumen and abomasum. The duodenum had the lowest growth rate and matured later than the other segments. The varied growth rate of bacterial communities in different gut segments may be partially ascribed to our findings that show the different signature taxa in each gut segment.

Capturing bacteria from the surroundings (i.e., bacterial transfer) is proposed to play an important role in gut microbiota establishment (Backhed et al., 2015; Guzman et al., 2015; Koenig et al., 2011). Thus, the bacterial community in the hindgut may be affected by the community in the foregut because of bacterial transfer (Ji et al., 2018), which may explain the similar bacterial compositions in the foregut and the hindgut. Work by Jonathan and Robert (2010) demonstrates this, showing that transferring bacteria active between two habitats resulted in similar bacterial communities (Jonathan & Robert, 2010). Here, we demonstrated that the similarity of the bacterial composition shown by the Bray–Curtis matrix between the foregut and the hindgut was high in young animals and decreased sharply with increasing age. This similarity could be roughly dichotomous at around 6 months of age in cows, indicating foregut–hindgut microbiota were more similar up until 6 months, past which point the microbiota composition diverged in cows. These findings imply that the hindgut of younger animals can capture bacteria more easily from the foregut than in older animals, as physical barriers may be established as the gastrointestinal tract develops (Malmuthuge & Guan, 2017), thus preventing microbiota in foregut affecting that in the hindgut. This may also explain why the microbiota in the hindgut matured earlier than the microbiota in the foregut, with the hindgut forming a barrier at an early age.

The limitation of the current study is that a follow‐up design was not used to look at the same individuals over time, which would provide more precise and detailed information. However, in the current study, the microbiota change over increasing age is notable, and the observation of maturity is also comparable with the findings of previous studies on cow rumens (Dill‐McFarland et al., 2017; Jami et al., 2013) and humans (Backhed et al., 2015; Subramanian et al., 2014; Yatsunenko et al., 2012), which can help support our main conclusion.

5. CONCLUSIONS

It has been demonstrated that bacterial composition may be affected by both the gut segment they are found in and the age of the cow. This was done by tracking the establishment of the gastrointestinal microbiota in cows from 1 week old to adulthood. Microbiota alpha diversity and maturity changed with age following the Gompertz curve in each gut segment (rumen, abomasum, duodenum, and rectum). The gut microbiota was established quickly in young animals and then slowed with age until it stabilized at maturity. Importantly, differences in Gompertz model parameters were also found in different gut segments, with the highest microbiota growth rate found in the rectum. Thus, we can conclude that the bacterial community in the rectum matured early in life, followed by rumen and abomasum bacteria, with the duodenum having the slowest growth rate and late maturity of the microbiota. The microbiota similarity in adjacent gut segments was higher in younger animals than older animals, which indicates that microbiota in the hindgut may be more easily affected by the foregut microbiota early in life. These findings have expanded our understanding of the dynamic changes of microbiota in different gut segments from birth to adulthood and hints that intervention early in life may be an effective way to manipulate the gut microbiota in cows, thereby improving gut health and the performance of cows.

CONFLICT OF INTEREST

None declared.

AUTHOR CONTRIBUTIONS

Chun Y. Guo: Investigation (lead); writing – original draft (equal). Shou K. Ji: Conceptualization (equal); data curation (lead); formal analysis (lead); writing – original draft (lead). Hui Yan: Data curation (supporting); writing – original draft (supporting). Ya J. Wang: Writing – review & editing (supporting). Jing J. Liu: Investigation (supporting). Zhi J. Cao: Writing – review & editing (supporting). Hong J. Yang: Methodology (supporting). Wen J. Zhang: Conceptualization (equal); methodology (equal); supervision (lead); writing – review & editing (lead). Sheng L. Li: Conceptualization (lead); funding acquisition (lead); methodology (lead); supervision (equal); writing – review & editing (equal).

ETHICS STATEMENT

The experimental design and procedures were approved by the Animal Care and Use Committee of the College of Animal Science and Technology of China Agricultural University (Project number: 31772628) in compliance with the Regulations for the Administration of Affairs Concerning Experimental Animals (The State Science and Technology Commission of P. R. China, 1988).

ACKNOWLEDGMENTS

This research was supported by grants from the National Dairy Industry and Technology System (CARS‐36) and the State Key Laboratory of Animal Nutrition (project: 2016–2020). We thank Yun Du, Zhaohai Wu, Jianxin Xiao, Jinghui Li, and Guoqing Sun for their technical help in collecting samples; Xiaofeng Zhang from Allwegene for rDNA sequencing; and Yan He from Wageningen University for editing the English text of this manuscript's draft. This manuscript, with partial results, has been released as a pre‐print at [Research Square] (Guo et al., 2019).

Appendix A.

TABLE A1.

Numbers of samples collected of different gut sections by cow age

Gut sections Ages
W1 M1 M2 M6 Y1 Y2 Y5
Rumen 12 8 8 11 5 8 6
Abomasum 0 8 8 4 5 6 6
Duodenum 12 8 8 4 5 8 5
Rectum 11 8 8 11 5 8 6
Open in a new tab

TABLE A2.

AIC and BIC of Chao1 and Shannon index fitted by linear, quadratic, and Gompertz models

Index Gut sections AIC BIC
Linear Quadratic Gompertz Linear Quadratic Gompertz
Chao1 Rumen 945 847 853 951 855 861
Abomasum 588 542 551 592 549 557
Duodenum 782 778 778 788 786 786
Rectum 880 852 821 886 860 830
Shannon Rumen 211 159 150 217 167 158
Abomasum 112 93 89 118 99 96
Duodenum 170 152 148 176 159 156
Rectum 201 191 131 207 199 140
Open in a new tab

Abbreviations: AIC, Akaike information criterion; BIC, Bayesian information criteria.

TABLE A3.

Correlations of predicted and observed Chao1 and Shannon indexes

Indexes Age Rumen Abomasum Duodenum Rectum
Predicted Observed Predicted Observed Predicted Observed Predicted Observed
Chao1 W1 535 480 – – 601 343 361 234
M1 655 949 834 974 641 887 594 760
M2 875 855 989 873 710 751 970 994
M6 1694 1497 1516 1602 957 1278 1545 1403
Y1 2680 2873 2168 1887 1348 1253 1628 1768
Y2 3394 3627 2783 3156 2029 1997 1631 1686
Y5 3527 3230 2990 2782 2881 2901 1631 1639
r 0.98 0.96 0.97 0.98
p value 5.57 * 10−5 1.84 * 10−3 2.14 * 10−4 1.32 * 10−4
Shannon W1 4.75 4.57 – – 4.28 3.62 3.96 3.85
M1 5.11 5.59 5.48 5.63 4.53 5.60 5.51 5.91
M2 5.68 5.64 5.86 5.64 4.92 4.81 6.73 6.26
M6 7.18 6.84 6.87 7.03 6.04 6.44 7.21 7.37
Y1 8.41 9.08 7.74 7.73 7.17 6.87 7.22 7.99
Y2 9.09 9.21 8.31 8.31 8.06 7.84 7.22 6.67
Y5 9.19 8.87 8.44 8.43 8.33 8.68 7.22 7.30
r 0.98 0.99 0.94 0.94
p value 1.22 * 10−4 5.75 * 10−5 1.36 * 10−3 1.67 * 10−3
Open in a new tab

TABLE A4.

Gompertz model parameters of the Chao1 and Shannon indexes

Index Gut sections Parameters
a b k 80% maturity (weeks) Inflection age (weeks)
Chao1 Rumen 3528 1.96 0.038 72 17.7
Abomasum 2992 1.43 0.029 88 12.3
Duodenum 3021 1.64 0.014 190 35.3
Rectum 1630 1.72 0.133 20 4.1
Shannon Rumen 9.19 0.69 0.039 57 –
Abomasum 8.44 0.49 0.034 64 –
Duodenum 8.33 0.69 0.029 76 –
Rectum 7.22 0.79 0.268 10 –
Open in a new tab

TABLE A5.

Correlations of predicted and observed maturity indexes

Age Rumen Abomasum Duodenum Rectum
Predicted Observed Predicted Observed Predicted Observed Predicted Observed
W1 0.016 0.016 – – 0.052 0.043 0.004 0.008
M1 0.025 0.025 0.019 0.024 0.058 0.051 0.018 0.012
M2 0.047 0.037 0.050 0.032 0.068 0.052 0.083 0.083
M6 0.180 0.181 0.256 0.288 0.112 0.152 0.418 0.421
Y1 0.398 0.417 0.468 0.439 0.195 0.222 0.528 0.488
Y2 0.548 0.503 0.525 0.495 0.370 0.356 0.534 0.481
Y5 0.567 0.614 0.527 0.571 0.618 0.623 0.534 0.535
r 0.994 0.991 0.995 0.982
p value 5.7 * 10−6 1.2 * 10−4 4.0 * 10−6 8.5 * 10−5
Open in a new tab

TABLE A6.

Gompertz model parameters of maturity indexes

Gut sections Parameters
a b k 80% maturity (weeks) Inflection age (weeks)
Rumen 0.57 3.73 0.045 65 29.3
Abomasum 0.53 4.38 0.069 50 21.4
Duodenum 0.66 2.58 0.015 176 63.2
Rectum 0.53 5.47 0.120 31 14.1
Open in a new tab

FIGURE A1.

FIGURE A1

Open in a new tab

Core bacterial taxa at the genus level in different gut segments. (a) Dynamic change of core genera in the rumen, abomasum, duodenum, and rectum with age increase; the worm color indicated a higher bacterial abundance, while the cold color indicated a lower bacterial abundance. (b) Shared core genera in the rumen, abomasum, duodenum, and rectum (hypergeometric test, p < 0.01)

FIGURE A2.

FIGURE A2

Open in a new tab

Dynamic changes of Chao1 and Shannon indexes fitted by linear (black), quadratic (red), and Gompertz models (blue)

FIGURE A3.

FIGURE A3

Open in a new tab

Correlations of predicted and observed Chao1 and Shannon indexes with Pearson correlation

FIGURE A4.

FIGURE A4

Open in a new tab

Dynamic change of maturity index fitted by (a) the Gompertz model, and (b) correlations of predicted and observed maturity indexes

Guo CY, Ji SK, Yan H, et al. Dynamic change of the gastrointestinal bacterial ecology in cows from birth to adulthood. MicrobiologyOpen. 2020;9:e1119 10.1002/mbo3.1119

Chun Y. Guo and Shou K. Ji contributed equally to the article.

Contributor Information

Wen J. Zhang, Email: zhangwj1022@sina.com.

Sheng L. Li, Email: lisheng0677@163.com.

DATA AVAILABILITY STATEMENT

Sequence data have been deposited in the NCBI (https://www.ncbi.nlm.nih.gov/sra) under the accession numbers PRJNA649813.

REFERENCES

  1. Backhed, F. , Roswall, J. , Peng, Y. , Feng, Q. , Jia, H. , Kovatcheva‐Datchary, P. , … Wang, J. (2015). Dynamics and stabilization of the human gut microbiome during the first year of life. Cell Host & Microbe, 17(5), 690–703. 10.1016/j.chom.2015.04.004 [DOI] [PubMed] [Google Scholar]
  2. Caporaso, J. G. , Kuczynski, J. , Stombaugh, J. , Bittinger, K. , Bushman, F. D. , Costello, E. K. , … Knight, R. (2010). QIIME allows analysis of high‐throughput community sequencing data. Nature Methods, 7(5), 335–336. 10.1038/nmeth.f.303 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Costello, E. K. , Lauber, C. L. , Hamady, M. , Fierer, N. , Gordon, J. I. , & Knight, R. (2009). Bacterial community variation in human body habitats across space and time. Science, 326(5960), 1694–1697. 10.1126/science.1177486 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. de Oliveira, M. N. , Jewell, K. A. , Freitas, F. S. , Benjamin, L. A. , Totola, M. R. , Borges, A. C. , … Suen, G. (2013). Characterizing the microbiota across the gastrointestinal tract of a Brazilian Nelore steer. Veterinary Microbiology, 164(3–4), 307–314. 10.1016/j.vetmic.2013.02.013 [DOI] [PubMed] [Google Scholar]
  5. Delzenne, N. M. , & Bindels, L. B. (2018). Gut microbiota in 2017: Contribution of gut microbiota‐host cooperation to drug efficacy. Nature Reviews Gastroenterology & Hepatology, 15(2), 69–70. 10.1038/nrgastro.2017.170 [DOI] [PubMed] [Google Scholar]
  6. Dias, J. , Marcondes, M. I. , Motta de Souza, S. , Silva, E. , Cardoso da Mata, B. , Fontes Noronha, M. , … Suen, G. (2018). Bacterial community dynamics across the gastrointestinal tracts of dairy calves during preweaning development. Applied and Environment Microbiology, 84(9), e02675‐17 10.1128/AEM.02675-17 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Dill‐McFarland, K. A. , Breaker, J. D. , & Suen, G. (2017). Microbial succession in the gastrointestinal tract of dairy cows from 2 weeks to first lactation. Scientific Reports, 7, 40864 10.1038/srep40864 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Dill‐McFarland, K. A. , Weimer, P. J. , Breaker, J. D. , & Suen, G. (2019). Diet influences early microbiota development in dairy calves without long‐term impacts on milk production. Applied and Environment Microbiology, 85(2), e02141‐18 10.1128/AEM.02141-18 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Durso, L. M. , Harhay, G. P. , Smith, T. P. , Bono, J. L. , Desantis, T. Z. , Harhay, D. M. , … Clawson, M. L. (2010). Animal‐to‐animal variation in fecal microbial diversity among beef cattle. Applied and Environment Microbiology, 76(14), 4858–4862. 10.1128/AEM.00207-10 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Edgar, R. C. (2010). Search and clustering orders of magnitude faster than BLAST. Bioinformatics, 26(19), 2460–2461. 10.1093/bioinformatics/btq461 [DOI] [PubMed] [Google Scholar]
  11. Faith, J. J. , Guruge, J. L. , Charbonneau, M. , Subramanian, S. , Seedorf, H. , Goodman, A. L. , … Gordon, J. I. (2013). The long‐term stability of the human gut microbiota. Science, 341(6141), 1237439 10.1126/science.1237439 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Favier, C. F. , Vaughan, E. E. , De Vos, W. M. , & Akkermans, A. D. L. (2002). Molecular monitoring of succession of bacterial communities in human neonates. Applied and Environmental Microbiology, 68(1), 219–226. 10.1128/aem.68.1.219-226.2002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Frey, J. C. , Pell, A. N. , Berthiaume, R. , Lapierre, H. , Lee, S. , Ha, J. K. , … Angert, E. R. (2010). Comparative studies of microbial populations in the rumen, duodenum, ileum and faeces of lactating dairy cows. Journal of Applied Microbiology, 108(6), 1982–1993. 10.1111/j.1365-2672.2009.04602.x [DOI] [PubMed] [Google Scholar]
  14. Godoy‐Vitorino, F. , Goldfarb, K. C. , Karaoz, U. , Leal, S. , Garcia‐Amado, M. A. , Hugenholtz, P. , … Dominguez‐Bello, M. G. (2011). Comparative analyses of foregut and hindgut bacterial communities in hoatzins and cows. The ISME Journal, 6(3), 531–541. 10.1038/ismej.2011.131 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Gu, S. , Chen, D. , Zhang, J. N. , Lv, X. , Wang, K. , Duan, L. P. , … Wu, X. L. (2013). Bacterial community mapping of the mouse gastrointestinal tract. PLoS One, 8(10), e74957 10.1371/journal.pone.0074957 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Guo, C. Y. , Ji, S. K. , Yan, H. , Wang, Y. J. , Liu, J. J. , Cao, Z. J. , … Sheng, L. L. (2019). Dynamic establishment of the gut bacterial ecology of cows from birth to adulthood. Retrieved from https://www.researchsquare.com/article/c5019010-eba3-433b-b989-eb48ed388c8a/v1 [DOI] [PMC free article] [PubMed]
  17. Guzman, C. E. , Bereza‐Malcolm, L. T. , De Groef, B. , & Franks, A. E. (2015). Presence of selected methanogens, fibrolytic bacteria, and proteobacteria in the gastrointestinal tract of neonatal dairy calves from birth to 72 hours. PLoS One, 10(7), e0133048 10.1371/journal.pone.0133048 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Hess, M. , Sczyrba, A. , Egan, R. , Kim, T. W. , Chokhawala, H. , Schroth, G. , … Rubin, E. M. (2011). Metagenomic discovery of biomass‐degrading genes and genomes from cow rumen. Science, 331(6016), 463–467. 10.1126/science.1200387 [DOI] [PubMed] [Google Scholar]
  19. Jami, E. , Israel, A. , Kotser, A. , & Mizrahi, I. (2013). Exploring the bovine rumen bacterial community from birth to adulthood. ISME Journal, 7(6), 1069–1079. 10.1038/ismej.2013.2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Ji, S. , Jiang, T. , Yan, H. , Guo, C. , Liu, J. , Huawei, S. U. , … Li, S. (2018). Ecological restoration of antibiotic‐disturbed gastrointestinal microbiota in foregut and hindgut of cows. Frontiers in Cellular and Infection Microbiology, 8(79). 10.3389/fcimb.2018.00079 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Jin, Y. , Wu, S. , Zeng, Z. , & Fu, Z. (2017). Effects of environmental pollutants on gut microbiota. Environmental Pollution, 222, 1–9. 10.1016/j.envpol.2016.11.045 [DOI] [PubMed] [Google Scholar]
  22. Jonathan, B. L. , & Robert, E. R. (2010). The theory of island biogeography revisited, Oxfordshire, UK: Princeton University Press. [Google Scholar]
  23. Koenig, J. E. , Spor, A. , Scalfone, N. , Fricker, A. D. , Stombaugh, J. , Knight, R. , … Ley, R. E. (2011). Succession of microbial consortia in the developing infant gut microbiome. Proceedings of the National Academy of Sciences of the United States of America, 108(Suppl 1), 4578–4585. 10.1073/pnas.1000081107 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Li, R. W. , Connor, E. E. , Li, C. , Baldwin, R. L. , & Sparks, M. E. (2012). Characterization of the rumen microbiota of pre‐ruminant calves using metagenomic tools. Environmental Microbiology, 14(1), 129–139. 10.1111/j.1462-2920.2011.02543.x [DOI] [PubMed] [Google Scholar]
  25. Looft, T. , Allen, H. K. , Cantarel, B. L. , Levine, U. Y. , Bayles, D. O. , Alt, D. P. , … Stanton, T. B. (2014). Bacteria, phages and pigs: The effects of in‐feed antibiotics on the microbiome at different gut locations. The ISME Journal, 8(8), 1566–1576. 10.1038/ismej.2014.12 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Magoc, T. , & Salzberg, S. L. (2011). FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics, 27(21), 2957–2963. 10.1093/bioinformatics/btr507 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Malmuthuge, N. , & Guan, L. L. (2017). Understanding host‐microbial interactions in rumen: Searching the best opportunity for microbiota manipulation. Journal of Animal Science and Biotechnology, 8, 8 10.1186/s40104-016-0135-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Malmuthuge, N. , Li, M. , Goonewardene, L. A. , Oba, M. , & Guan, L. L. (2013). Effect of calf starter feeding on gut microbial diversity and expression of genes involved in host immune responses and tight junctions in dairy calves during weaning transition. Journal of Dairy Science, 96(5), 3189–3200. 10.3168/jds.2012-6200 [DOI] [PubMed] [Google Scholar]
  29. Mao, S. , Zhang, M. , Liu, J. , & Zhu, W. (2015). Characterising the bacterial microbiota across the gastrointestinal tracts of dairy cattle: Membership and potential function. Scientific Reports, 5, 16116 10.1038/srep16116 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. McDonald, D. , Price, M. N. , Goodrich, J. , Nawrocki, E. P. , DeSantis, T. Z. , Probst, A. , … Hugenholtz, P. (2012). An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME Journal, 6(3), 610–618. 10.1038/ismej.2011.139 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Popova, M. , McGovern, E. , McCabe, M. S. , Martin, C. , Doreau, M. , Arbre, M. , … Waters, S. M. (2017). The structural and functional capacity of ruminal and cecal microbiota in growing cattle was unaffected by dietary supplementation of linseed oil and nitrate. Frontiers in Microbiology, 8, 937 10.3389/fmicb.2017.00937 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Rawls, J. F. , Mahowald, M. A. , Ley, R. E. , & Gordon, J. I. (2006). Reciprocal gut microbiota transplants from zebrafish and mice to germ‐free recipients reveal host habitat selection. Cell, 127(2), 423–433. 10.1016/j.cell.2006.08.043 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Rehman, H. U. , Vahjen, W. , Awad, W. A. , & Zentek, J. (2007). Indigenous bacteria and bacterial metabolic products in the gastrointestinal tract of broiler chickens. Archives of Animal Nutrition, 61(5), 319–335. 10.1080/17450390701556817 [DOI] [PubMed] [Google Scholar]
  34. Ren, T. , Boutin, S. , Humphries, M. M. , Dantzer, B. , Gorrell, J. C. , Coltman, D. W. , … Wu, M. (2017). Seasonal, spatial, and maternal effects on gut microbiome in wild red squirrels. Microbiome, 5(1), 163 10.1186/s40168-017-0382-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Rubino, F. , Carberry, C. , M Waters, S. , Kenny, D. , McCabe, M. S. , & Creevey, C. J. (2017). Divergent functional isoforms drive niche specialisation for nutrient acquisition and use in rumen microbiome. ISME Journal, 11(4), 932–944. 10.1038/ismej.2016.172 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Shabat, S. K. , Sasson, G. , Doron‐Faigenboim, A. , Durman, T. , Yaacoby, S. , Berg Miller, M. E. , … Mizrahi, I. (2016). Specific microbiome‐dependent mechanisms underlie the energy harvest efficiency of ruminants. ISME Journal, 10(12), 2958–2972. 10.1038/ismej.2016.62 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Subramanian, S. , Huq, S. , Yatsunenko, T. , Haque, R. , Mahfuz, M. , Alam, M. A. , … Gordon, J. I. (2014). Persistent gut microbiota immaturity in malnourished Bangladeshi children. Nature, 510(7505), 417–421. 10.1038/nature13421 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Tun, H. M. , Konya, T. , Takaro, T. K. , Brook, J. R. , Chari, R. , Field, C. J. , … Child Study Investigators (2017). Exposure to household furry pets influences the gut microbiota of infant at 3–4 months following various birth scenarios. Microbiome, 5(1), 40 10.1186/s40168-017-0254-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Uyeno, Y. , Sekiguchi, Y. , & Kamagata, Y. (2010). rRNA‐based analysis to monitor succession of faecal bacterial communities in Holstein calves. Letters in Applied Microbiology, 51(5), 570–577. 10.1111/j.1472-765X.2010.02937.x [DOI] [PubMed] [Google Scholar]
  40. Wang, Y. , Devkota, S. , Musch, M. W. , Jabri, B. , Nagler, C. , Antonopoulos, D. A. , … Chang, E. B. (2010). Regional mucosa‐associated microbiota determine physiological expression of TLR2 and TLR4 in murine colon. PLoS One, 5(10), e13607 10.1371/journal.pone.0013607 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Yassour, M. , Vatanen, T. , Siljander, H. , Hamalainen, A. M. , Harkonen, T. , & Ryhanen, S. J. , … Xavier, R. J. (2016). Natural history of the infant gut microbiome and impact of antibiotic treatment on bacterial strain diversity and stability. Science Translational Medicine, 8(343), 343ra81 10.1126/scitranslmed.aad0917 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Yatsunenko, T. , Rey, F. E. , Manary, M. J. , Trehan, I. , Dominguez‐Bello, M. G. , Contreras, M. , … Gordon, J. I. (2012). Human gut microbiome viewed across age and geography. Nature, 486(7402), 222–227. 10.1038/nature11053 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Yeoman, C. J. , Ishaq, S. L. , Bichi, E. , Olivo, S. K. , Lowe, J. , & Aldridge, B. M. (2018). Biogeographical differences in the influence of maternal microbial sources on the early successional development of the bovine neonatal gastrointestinal tract. Scientific Reports, 8(1), 3197 10.1038/s41598-018-21440-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Sequence data have been deposited in the NCBI (https://www.ncbi.nlm.nih.gov/sra) under the accession numbers PRJNA649813.

Articles from MicrobiologyOpen are provided here courtesy of Wiley

RESOURCES