Skip to main content
Applied and Environmental Microbiology logoLink to Applied and Environmental Microbiology
. 2018 Apr 16;84(9):e02675-17. doi: 10.1128/AEM.02675-17

Bacterial Community Dynamics across the Gastrointestinal Tracts of Dairy Calves during Preweaning Development

Juliana Dias a,b, Marcos Inácio Marcondes a, Shirley Motta de Souza b, Barbara Cardoso da Mata e Silva c, Melline Fontes Noronha d, Rafael Tassinari Resende e, Fernanda Samarini Machado f, Hilário Cuquetto Mantovani g, Kimberly A Dill-McFarland h,*,, Garret Suen h,
Editor: Christopher A Elkinsi
PMCID: PMC5930334  PMID: 29475865

ABSTRACT

Microbial communities play critical roles in the gastrointestinal tracts (GIT) of preruminant calves by influencing performance and health. However, little is known about the establishment of microbial communities in the calf GIT or their dynamics during development. In this study, next-generation sequencing was used to assess changes in the bacterial communities of the rumen, jejunum, cecum, and colon in 26 crossbred calves at four developmental stages (7, 28, 49, and 63 days old). Alpha diversity differed among GIT regions with the lowest diversity and evenness in the jejunum, whereas no changes in alpha diversity were observed across developmental stage. Beta diversity analysis showed both region and age effects, with low numbers of operational taxonomic units (OTUs) shared between regions within a given age group or between ages in a given region. Taxonomic analysis revealed that several taxa coexisted in the rumen, jejunum, cecum, and colon but that their abundances differed considerably by GIT region and age. As calves aged, we observed lower abundances of taxa such as Bacteroides, Parabacteroides, and Paraprevotella with higher abundances of Bulleidia and Succiniclasticum in the rumen. The jejunum also displayed taxonomic changes with increases in Clostridiaceae and Turicibacter taxa in older calves. In the lower gut, taxa such as Lactobacillus, Blautia, and Faecalibacterium decreased and S24-7, Paraprevotella, and Prevotella increased as calves aged. These data support a model whereby early and successive colonization by bacteria occurs across the GIT of calves and provides insights into the temporal dynamics of the GIT microbiota of dairy calves during preweaning development.

IMPORTANCE The gastrointestinal tracts (GIT) of ruminants, such as dairy cows, house complex microbial communities that contribute to their overall health and support their ability to produce milk. For example, the rumen microbiota converts feed into usable nutrients, while the jejunal microbiota provides access to protein. Thus, establishing a properly functioning GIT microbiota in dairy calves is critical to their productivity as adult cows. However, little is known about the establishment, maintenance, and dynamics of the calf GIT microbiota in early life. In this study, we evaluated the bacterial communities in the rumen, jejunum, cecum, and colon in dairy calves across preweaning development and show that they are highly variable early on in life before transitioning to a stable community. Understanding the dairy calf GIT microbiota has implications for ensuring proper health during early life and will aid in efforts to develop strategies for improving downstream production.

KEYWORDS: 16S rRNA, rumen, dairy calf, gastrointestinal tract, microbiota

INTRODUCTION

The proper acquisition of gastrointestinal tract (GIT) microbiota in animals is critical to their survival and success. A textbook example is the dairy calf, which at birth has an immature GIT that is quickly colonized by archaeal, bacterial, and fungal communities (13) acquired from both maternal (i.e., birth and contact) and environmental (i.e., diet and housing) sources (4). Bacteria constitute the predominant and most diverse community across the calf GIT and play essential roles in fiber degradation, provisioning of nutrients such as protein and volatile fatty acids (VFAs) to the host (5), modulation of the immune system (6), and morphological development of the rumen and intestine (7).

In adult ruminants, the rumen is the primary site of feed degradation and nutrient absorption. In contrast, the rumen of neonate calves is underdeveloped in terms of volume, absorptive capacity, and metabolism. The rumen becomes functional during the first months of life through processes triggered by solid-feed intake and microbial activity (5). In order to accelerate this process and reduce early-life feed costs, dairy calves raised in commercial herds are fed restricted amounts of milk, or milk replacer, to stimulate the intake of starter concentrate and accelerate ruminal development.

Throughout the transition from a liquid to solid-based diet, the calf GIT undergoes several modifications that are critical to survival and performance during the postweaning period. As solid-feed intake and microbial activity increase, the rumen increases in surface area by expanding in volume and papillation. Additionally, the main site of digestion and absorption shifts from the intestine to the rumen, and the calf's metabolism switches from primarily glucose to VFA utilization. Development of other digestive compartments also occurs with changes in intestinal enzyme activity from lactase to maltase as well as the development of the salivary apparatus and rumination behavior (5, 79).

Concomitant with these morphophysiological adaptations, changes in diversity and abundance of the bacterial community are also observed as calves age. Previous studies reported that rumen and fecal microbiotas change significantly from birth to weaning (2, 1012). However, bacterial changes in other GIT regions during the preweaning period remain poorly explored, despite their importance in nutrition and the health of young calves.

Recently, it has been proposed that alteration to the dairy calf GIT microbiota is a promising avenue for improving both calf health and downstream milk production (13, 14). This idea has gained traction given that the calf rumen microbiota is dynamic prior to weaning (12) and that the adult rumen microbiota is correlated to milk production efficiency (15, 16) and resistant to change (17). As a result, the GIT microbiota may be amenable to alterations that will have long-lasting impacts in the adult cow. However, analysis of the calf GIT microbiota has been limited to primarily the rumen, and previous work on other sections of the GIT has either pooled samples (18) or targeted specific bacterial species (19). This prevents capture of interanimal variation and provides a restricted picture of the bacterial diversity in the calf GIT.

To address this gap in knowledge, we utilized next-generation sequencing to assess changes in the bacterial community across the GIT (rumen, jejunum, cecum, and colon) of dairy calves during preweaning development (7, 28, 49, and 63 days old). We hypothesized that the dairy calf GIT microbiota is dynamic during early life and correlated to dietary changes that accompany the weaning transition. Evaluating the GIT bacterial community in dairy calves not only provides insight into microbial acquisition and succession in early life but also will provide a framework for efforts aimed at altering the GIT microbiota for improving health and downstream milk production efficiency.

RESULTS

Sequencing.

After sequence trimming, quality filtering, and removal of chimeras, a total of 2,321,474 high-quality sequences (mean ± standard deviation [SD], 20,727 ± 19,070 per sample) and 16,999 operational taxonomic units (OTUs) (mean ± SD, 152 ± 93 per sample) which clustered at 97% similarity were obtained. Good's coverage estimation ranged from 0.997 to 0.999 before normalization, indicating that sequences sufficiently covered the diversity of the GIT bacterial communities (see Table S2 in the supplemental material). After normalization, 336,354 (3,003 ± 16 sequences per sample) and 10,637 OTUs (95 ± 49 per sample) remained.

Alpha diversity varies across the GIT but not with age.

Alpha diversity, as measured by Chao1 richness and Shannon's and inverse Simpson's diversity indexes, varied significantly by GIT region (analysis of variance [ANOVA]; false discovery rate [FDR] = 0.049, <0.001, and <0.001, respectively) but not with age (FDR = 0.782, 0.215, and 0.138, respectively) or by the interaction of these two factors (FDR = 0.974, 0.478, and 0.384 [see Table S3]). Alpha diversity did not differ among bacterial communities of the rumen, cecum, and colon but were significantly lower in the jejunum (Tukey's honest significant difference [HSD], P < 0.05 [Table S3]).

Beta diversity varies across the GIT and with age.

Beta diversity was assessed by two approaches: Venn diagrams and canonical analysis of principal coordinates (CAP). Out of 1,588 unique OTUs, 653 displayed relative abundances of >0.1%, and only 29, 30, 41, and 29 of these were shared (by at least two calves in each group) across GIT compartments of calves at 7, 28, 49, and 63 days of age, respectively (Fig. 1A). Regardless of age, jejunum samples exhibited the lowest number of unique OTUs and shared the lowest number of OTUs with other GIT regions. In contrast, the number of OTUs shared by the colon and cecum, and by the colon and rumen, tended to increase with age (Fig. 1A). Furthermore, our Venn diagram analysis revealed that only 42 (rumen), 32 (jejunum), 39 (cecum), and 48 (colon) OTUs were shared across age groups (Fig. 2A).

FIG 1.

FIG 1

(A) Venn diagram of OTUs shared between GIT regions (rumen, jejunum, cecum, and colon) within a given age group (7, 28, 49, and 63 days). Only OTUs at >0.1% relative abundance and present in at least two samples in each group were included. (B) Canonical analysis of principal coordinates (CAP) of the Bray-Curtis dissimilarity metric for bacterial community according to GIT region in each age group. Individual points in each plot represent a sample, different colors represent the GIT region, and each facet represents the age group. Percentages and P values shown along the axes represent, respectively, the proportion of dissimilarities captured by CAP and significance by permutation test.

FIG 2.

FIG 2

(A) Venn diagram of OTUs shared between age group (7, 28, 49, and 63 days) within a given GIT region (rumen, jejunum, cecum, and colon). Only OTUs at >0.1% relative abundance and present in at least two samples in each group were included. (B) CAP of the Bray-Curtis dissimilarity metric for bacterial community according to age group in each GIT region. Individual points in each plot represent a sample, different shapes represent the age group, and each facet represents the GIT region. Percentages and P values shown along the axes represent, respectively, the proportion of dissimilarities captured by CAP and significance by permutation test for CAP1 and CAP2.

Our CAP analysis showed that Bray-Curtis dissimilarities of the bacterial community were attributed to GIT region (permutation test, P = 0.001), age (P = 0.001), and interaction of GIT and age (P = 0.005). The segregation of bacterial communities in the different compartments of the GIT of calves was evident from the first week of life and persisted with age (Fig. 1B). However, the bacterial communities of the cecum and colon displayed a high degree of similarity, as these samples clustered together at all developmental stages (Fig. 1B). Within GIT regions, dissimilarities in the structure of bacterial communities were most notable between calves at 7 and 63 days of age. However, decreasing distance within the rumen, jejunum, cecum, and colon samples of calves at 63 days indicated that bacterial community composition tended to be less heterogeneous as calves aged (Fig. 2B).

Core bacterial communities vary with GIT and age.

Our taxonomic composition analysis revealed 1,588 unique OTUs assigned to 23 phyla, 112 families, and 164 genera. We identified a core microbiome composed of 30 bacterial taxa at a relative abundance of ≥0.1% and present in at least 50% of all samples (see Fig. S1 in the supplemental material). Furthermore, we identified significant differences in the relative abundances of these core OTUs (ANOVA, Tukey's HSD) among GIT region, age group, and age within each compartment (Fig. 3; see also Table S4 in the supplemental material).

FIG 3.

FIG 3

Changes in the relative abundance of the most abundant bacterial taxa (sequences summarized at phylum [p_], family [f_], and genus [g_] levels at average abundance of ≥0.1% and present in at least 50% of all samples) according to GIT region, age group, and their interaction (GIT*Age). Means followed by the same letter are not significantly different (P > 0.05) by Tukey's HSD test (see Table S4 in the supplemental material). Taxon rows without letters were not significantly different for that variable.

A total of 27 core bacterial taxa varied according to GIT region and/or age. Changes exclusively ascribed to GIT region include the predominance of members from the genus Bifidobacterium and the family Mogibacteriaceae in the rumen (ANOVA; FDR = 0.001 and <0.001, respectively; Tukey's HSD, P < 0.05), the family Enterobacteriaceae in the jejunum and large intestine (FDR < 0.001; Tukey's HSD, P < 0.05), as well as the families Lachnospiraceae and Ruminococcaceae (FDR < 0.001 and 0.002, respectively; Tukey's HSD, P < 0.05) in the cecum and colon (Fig. 3; see also Table S4). In regard to age effects, we found 12 bacterial taxa that varied across developmental stages, but all of them also varied according to GIT and age. No changes in the relative abundances of bacterial taxa were exclusively ascribed to calf age (Fig. 3; see also Table S4).

Further, several taxa varied simultaneously according to GIT region and age. At the phylum level, the abundance of the Bacteroidetes (ANOVA; FDR < 0.001) remained high and unchanged in the rumen, decreased in the jejunum, and increased in the cecum and colon as the calves aged (Tukey's HSD, P < 0.05). Proteobacteria were abundant in the jejunum of neonate calves, decreased at 28 days, and subsequently increased at 49 and 63 days (Tukey's HSD, P < 0.05). Meanwhile, the abundance of proteobacteria (FDR = 0.018) in the rumen and large intestine remained low and unchanged (rumen and cecum) as the calves aged (Tukey's HSD, P < 0.05). At the family level, the major changes included increases in the Paraprevotellaceae (FDR < 0.001) at 28 and 49 days, the Clostridiaceae (FDR < 0.001) in the jejunum in 63-day-old calves, and candidate family S24-7 (FDR = 0.030) in the cecum and colon as the calves aged (Tukey's HSD, P < 0.05). With the exception of the cecum, Coriobacteriaceae (FDR < 0.001) increased with age and became abundant in the rumen, jejunum, and colon in 63-day-old calves (Tukey's HSD, P < 0.05).

At the genus level, the major changes observed in the rumen were decreases in the Bacteroides (FDR = 0.005), Blautia (FDR < 0.001), Oscillospira (FDR < 0.001), Parabacteroides (FDR < 0.001), Paraprevotella (FDR < 0.001), and Streptococcus (FDR = 0.039) and increases in Bulleidia (FDR < 0.001), Prevotella (FDR = 0.037), Ruminococcus (FDR = 0.046), and Succiniclasticum (FDR < 0.001) in 28 calves (Tukey's HSD, P < 0.05). In the jejunum, Lactobacillus (FDR = 0.024) was overrepresented in calves from 7 to 49 days, but its proportion decreased markedly at 63 days (Tukey's HSD, P < 0.05). Meanwhile, the abundance of Bulleidia, Ruminococcus, and Turicibacter increased in the jejunum in older calves (Tukey's HSD, P < 0.05). In the large intestine, the abundance of Faecalibacterium (FDR < 0.001) and Lactobacillus decreased, while Paraprevotella and Prevotella increased as calves aged (Tukey's HSD, P < 0.05). Further, Blautia was abundant in the cecum and colon in neonates, but its proportion decreased in older calves (Tukey's HSD, P < 0.05). Likewise, the abundance of Bacteroides decreased in the cecum and colon in calves at 28 and 49 days and increased significantly in the cecum in older calves (Tukey's HSD, P < 0.05). In the cecum, the abundance of Turicibacter decreased as calves aged, while Phascolarctobacterium (FDR < 0.001) decreased at 28 and 49 days but increased at 63 days. In the colon, the abundance of Oscillospira and Ruminococcus remained high and unchanged across the developmental stages of calves (Tukey's HSD, P < 0.05), while Phascolarctobacterium, Succiniclasticum, and Turicibacter increased in older calves (Tukey's HSD, P < 0.05).

DISCUSSION

Characterization of the microbiota in the calf GIT has mostly been restricted to bacterial communities in the rumen and feces of young ruminants (2, 11, 12, 21, 22). This is largely due to the primary interest in the rumen as the major fermentation organ and the noninvasive nature of fecal sampling. As a result, these sites are often used as a proxy to represent the microbiota of the GIT (18, 23). However, previous work has demonstrated that the rumen and fecal microbiota are not good representatives of other GIT compartments (28). To address this, and to further understand how these communities change during preweaning development, we investigated the colonization of digestive compartments in young ruminants using microbiota analysis.

Overall, interindividual variation decreased and diversity increased in the calf GIT microbial communities as animals aged. This is similar to findings described in previous reports (11, 12, 18) and suggests a succession of microorganisms colonizing the GIT that coalesces toward an adult composition. In particular, we observed that the GIT of neonate calves contain several taxa (i.e., Bacteroides, Prevotella, and Ruminococcus) known to be involved in the degradation of fiber and starch (2527) and that are commonly identified in adult ruminants (2832). These taxa were present as early as 1 week of age, when calves had been exposed only to liquid feeds, including colostrum and milk. This supports a model of colonization by potentially beneficial digestive bacteria across the calf GIT very early in life, even in the absence of solid dietary substrates (2, 3).

Although some bacterial taxa were common across the calf GIT (Fig. 3; see also Table S4 in the supplemental material), GIT-related differences in composition and abundance were evident in the rumen, jejunum, and colon bacterial communities, with the microbiotas in the large intestine compartments (cecum and colon) being most similar to each other in terms of composition and abundance (Fig. 2 and 3). Moreover, few OTUs were shared between the rumen, jejunum, cecum, and colon (Fig. 1A), indicating that bacterial segregation occurs throughout the calf GIT (18). This finding may reflect the particular characteristics specific to each GIT region (e.g., morphology, pH, secretions, rate of passage, physical structure, and size of particle), and each compartment likely selects for a distinct microbial community that coevolves symbiotically with the host (33). Further investigation of the microbial communities across the calf GIT is needed in order to fully elucidate the role of factors such as diet in calf nutrition and microbiota acquisition.

In addition to differences in microbiota across GIT regions, different bacterial taxa were found to dominate each GIT region as calves aged. Age-related changes were most apparent when comparing the first stages (7 to 28 days) of development to later stages (49 to 63 days), as expected. This is likely due to differences in diet, as early on in life, calves consume predominantly milk before transitioning to increasing amounts of starter concentrate (offered ad libitum in our study). In the rumen bacterial community, age effects were most evident for bacteria in the genera Bacteroides, Parabacteroides, Paraprevotella, and Streptococcus, whose dominance at 7 days was replaced by members of the Bulleidia, Prevotella, Ruminococcus, and Succiniclasticum by 28 days, shortly after the beginning of starter concentrate intake (Fig. 3; see also Table S4). These results support our previous observations (34) showing that inclusion of concentrate in the calf diet promotes the colonization of Bulleidia (saccharolytic organism) and Succiniclasticum (succinate consumer) rather than Bacteroides and Parabacteroides in the rumen.

Distinct age effects were also observed in the lower gut (jejunum, cecum, and colon) in dairy calves. As calves aged and consumed more starter concentrate, relative to milk, we observed an increase in saccharolytic and amylolytic taxa. These included bacteria in the genera Bulleidia (35) and Turicibacter (36) in the jejunum and the genera Paraprevotella (37) and Prevotella (38) in the cecum and colon (Fig. 3; see also Table S4). Colonization of amylolytic bacteria in the lower gut is especially important during the preweaning stage, as calves have limited ability to utilize starch postruminally. In adult ruminants, starch that escapes rumen fermentation (up to 30%) reaches the small intestine and undergoes enzymatic hydrolysis by host-derived α-amylase and maltase. However, the activity of these enzymes in the small intestine of calves is very low and age dependent (39, 40). Moreover, a recent study reported that postruminal utilization of starch in calves is largely due to microbial fermentation in the small intestine (62%) and large intestine (37%) rather than digestion by host enzymes (26). Thus, lower gut colonization by saccharolytic and amylolytic bacteria may play an important role in calf nutrient provisioning preweaning.

Concomitant with the increase of amylolytic bacteria, we also observed that the abundance of bacteria in the genera Lactobacillus (lower gut) and Faecalibacterium (large intestine) decreased in older calves. A similar observation has been made for fecal samples of older calves (11, 22) as well as for the colonic mucosae of lambs fed with milk plus starter instead of only milk (41). Taken together, these results suggest an inverse relationship between starter concentrate intake and colonization by Lactobacillus and Faecalibacterium in the lower gut. This relationship is likely the result of changes in substrate availability in the GIT as calves age.

Simple sugars, particularly lactose, become less available in the calf gut as animals age and the diet shifts toward a higher proportion of concentrate, resulting in an increase in the availability of more complex carbohydrates such as starch. Several species of Lactobacillus can ferment sugars to produce lactic acid, an intermediate of several pathways for VFA production (42). However, the ability of lactobacilli to ferment starch and its breakdown products (e.g., maltose) is limited and strain dependent (43). Thus, as calves consumed more concentrate, relative to milk, the resulting shifts in available substrates likely selected against Lactobacillus and resulted in the observed decrease in members of this genus as animals aged.

Similarly, members of the Faecalibacterium decreased in abundance as the availability of their preferred substrates changed. Faecalibacterium can metabolize acetate to produce butyrate (44), the major VFA involved in stimulating the growth and differentiation of colonocytes (45). Acetate availability in the calf GIT is mainly driven by carbohydrate fermentation by bacteria such as Blautia and Bacteroides. These bacteria also decreased with age (Fig. 3; see also Table S4), thus reducing acetate availability to Faecalibacterium, likely contributing to the observed decreases in this genus. Of note, age-related decreases in the abundance of Faecalibacterium were less pronounced than that of Lactobacillus, potentially as a result of the lower, but still relatively high, abundances of carbohydrate-fermenting organisms such as Blautia and Bacteroides in the large intestine of older calves.

In summary, we show that the calf GIT development shapes the microbiota of calves, leading to the establishment of specific microbiota in each GIT compartment. These apparent age effects are likely associated with a number of factors, including changes in nutrition, increases in solid food intake, development of the digestive system, and microbial interactions. Our work further documents microbial succession along the GIT, and we show that the development of the microbiota in each compartment reflects substrate availability as a function of diet. Importantly, our work supports a model that the calf microbiota is highly unstable and prone to shifts as the animal ages and that this property extends to all regions of the GIT. As a result, we speculate that microbial interventions, such as probiotic use, aimed at enacting permanent microbiota changes after weaning may not be effective if implemented immediately after birth. Given the transition of the GIT microbiota to a more stable community during the weaning transition (8 to 9 weeks of life), we suggest that this period may constitute a more effective window for microbiota manipulation targeting specific regions of the calf GIT, as has been suggested for the maintenance and improvement of calf health (13), or as a mechanism for increasing downstream milk production in adult cows (14).

MATERIALS AND METHODS

Animal experiments and sampling.

All animal procedures were conducted according to protocols approved by the Animal Care and Use Committee of the Universidade Federal de Viçosa, Minas Gerais, Brazil (protocol number 27/2013). The study was conducted at the Experimental Field Station Embrapa Gado de Leite, located in Coronel Pacheco, Minas Gerais, Brazil, from October 2013 to April 2014. A total of 26 newborn male crossbred (3/4 to 15/16 Holstein × Gyr) dairy calves from a fixed-time insemination protocol that proportioned an amplitude maximum of 8 days between all births were locally obtained shortly after birth (within 24 h). The calves were weighed (36.4 ± 3.7 kg), identified, and housed in individual stalls with ad libitum access to water. Colostrum from their dams was offered at 10% of body weight at birth (BWB), fractionated into two daily meals (morning and afternoon) until the 3rd day of life. During the experiment, calf health (i.e., incidence of diarrhea and respiratory diseases) was monitored according to guidelines proposed by Larson et al. (59). Death and respiratory diseases were not observed throughout the study. However, 5 calves presented with diarrhea between 15 and 22 days of age. Calves were considered diarrheic when they showed fecal scores of 3 (runny) and 4 (watery) for 3 consecutive days. Diarrheic calves in our study did not display alterations with respect to rectal temperature, appetite, or attitude that justified the administration of antibiotic. Diarrheic calves were fed normally (milk, starter concentrate, and fresh water were not discontinued) and treated exclusively with oral electrolyte solution (OES) to replace fluids and electrolytes. The OES was composed of d-glucose (80 g), sodium chloride (20 g), sodium bicarbonate (16 g), and potassium chloride (4 g) diluted in 4 liters of water. The OES was offered daily, 3 h after the first and second meal, until fecal consistency was observed to be normal (2 to 4 days).

For our study, calves were randomly distributed according to birth order to one of four age groups—7 (n = 6), 28 (n = 6), 49 (n = 8), and 63 (n = 6) days—and fed raw milk to 10% of BWB plus starter concentrate (see Table S1 in the supplemental material) ad libitum until sacrifice. Given that starter concentrate intake is negligible during the first week of life, calves sacrificed at 7 days were fed exclusively with colostrum (0 to 3 days) and raw milk (4 to 6 days). Calves were euthanized according to their assigned age group with an injection of acepromazine (0.013 mg/kg of body weight), thiopental (0.125 mg/kg), and potassium chloride (80 to 120 ml). After euthanasia, the body cavity was opened and the rumen, jejunum, cecum, and colon were isolated with polyethylene seals (zip ties) to avoid reflux of ingesta among compartments. Aliquots of 5 to 50 ml of ingesta from each GIT region was collected, immediately transported on wet ice, and stored at −80°C prior to DNA extraction.

DNA extraction and sequencing.

Total genomic DNA was extracted from fluid samples by following the procedures described in reference 46. Briefly, microbial cells were collected by centrifugation and lysed by heating and mechanical disruption. DNA was purified by phenol and phenol-chloroform-isoamyl alcohol extraction and resuspended in Tris-EDTA (TE) buffer. DNA was quantified with a NanoDrop spectrophotometer (Thermo Scientific, Wilmington, DE) and sequenced at the University of Wisconsin-Madison. The V3–V4 hypervariable regions of bacterial 16S rRNA (47) were amplified along with Illumina sequencing primers. PCR mixtures consisted of 50 ng of template DNA, 0.4 μM each primer, 1× Kapa Hifi HotStart ReadyMix (KAPA Biosystems), and water to 25 μl. The PCR was performed at 95°C for 3 min, 95°C for 30 s, 55°C for 30 s, and 72°C for 30 s (25 cycles), with a final extension step at 72°C for 5 min. PCR products were purified with a PureLink Pro 96 PCR purification kit (Invitrogen), and a second PCR was performed to attach Illumina sequencing adapters and unique dual indices. The second PCR was similar to the first except that 5 μl of unquantified PCR product was used as the template DNA and 8 amplification cycles were performed. PCR products were recovered by gel extraction in AquaPōr LM low-melt agarose (National Diagnostics, Atlanta, GA) using a Zymoclean gel DNA recovery kit (Zymo Research, Irvine, CA). Purified PCR products were quantified with a Qubit fluorometer (Invitrogen) and pooled in equimolar amounts. Sequencing was performed using the 2 × 300-bp paired-end method on an Illumina MiSeq by following the manufacturer's guidelines (Illumina, Inc., San Diego, CA).

Bioinformatics analysis.

The sequences were processed using mothur (v1.35.0) (48) with procedures modified from reference 20. Briefly, sequences with a length shorter than 250 bp or longer than 600 bp containing ambiguous characters or exhibiting homopolymers greater than 8 bp were removed. Sequences were aligned using the SILVA 16S rRNA gene reference database (49) and preclustered to remove sequencing errors. The Uchime algorithm was used to detect chimeric sequences (50), and sequences that did not align to the correct region or were chimeric were removed. The sequences were classified using the GreenGenes database (51) and grouped into operational taxonomic units (OTUs) by uncorrected pairwise distances clustered by the nearest-neighbor method, with a similarity cutoff of 97%. Coverage was assessed by Good's coverage, and the OTU counts were normalized to equal sequence counts using the normalize.shared function in mothur (by default, the smallest number of sequences in our samples, which was 3,003 sequences) due to different sequencing depths. Normalized OTU counts were used to determine alpha diversity (Chao1 richness and Shannon's and inverse Simpson's diversity) and the relative abundances of taxa (sequences summarized at the phylum, family, and genus levels) in each sample.

Statistical analysis.

Differences in the alpha diversity indexes (Chao1 and inverse Simpson's and Shannon's diversity indexes) and relative abundance of bacterial taxa (abundance of ≥0.1% in at least one sample and detected in at least 50% of all samples) according to GIT region, age group, and their interaction were analyzed using the package MASS (52) in R with a repeated-measures mixed model (ANOVA, type III error) reporting the within-subject effect of the GIT (as determined by a computed variance-covariance matrix, which assumes a diagonal structure type). The variance homogeneity was further evaluated and verified using Bartlett's test (53). The resulting means were then compared using the multicomp package (54) in R, and in the presence of significant interaction effects, the means of the age group were compared within GIT regions. The resulting P values were adjusted for false discovery rate (FDR) using the Benjamini-Hochberg method, and values of ≤0.05 were considered significant.

Beta diversity was assessed by Venn diagrams and canonical analysis of principal coordinates (CAP). Venn diagrams were generated using OTUs with relative sequence abundances of >0.1% in at least one sample and that were detected in at least two samples in each group. CAP was performed with OTUs (at >0.1% relative abundance in at least one sample) clustered by the Bray-Curtis dissimilarity index and corrected according to reference 55. A permutation test (nperm = 999) was performed to assess the significance of constraints for each factor (GIT, age, and their interaction) as well as for each constrained axis (CAP1 and CAP2). The dissimilarities ordinated by CAP were plotted with ellipses defined by standard deviation with a 95% confidence limit. These analyses were performed using the R packages VennDiagram (56), vegan (57), OTUtable (24), and ggplot2 (58).

Accession number(s).

All DNA sequences have been deposited in the NCBI's Sequence Read Archive under BioProject accession number PRJNA381944 (34) for rumen samples and BioProject accession number PRJNA399771 for jejunum, cecum, and colon samples.

Supplementary Material

Supplemental material

ACKNOWLEDGMENTS

J.D. acknowledges the CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Ministério da Educação do Brasil, grant no. PE 99999.002493/2014-04) and CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brasil, grant no. 157168/2012-3) for doctorate scholarships supporting this work. H.C.M. acknowledges funding from the CNPq (grant no. PVE 313792/2014-3). This work was also supported, in part, by both U.S. Department of Agriculture National Institute of Food and Agriculture HATCH grant WIS01729 and foundational grant 2015-67015-23246 to G.S.

J.D., F.S.M., and M.I.M. designed the experiment. F.S.M., H.C.M., and G.S. provided experimental and laboratorial resources. J.D. conducted the research, sample collection, and DNA extraction and with K.A.D.-M. performed the sequencing. J.D., K.A.D.-M., M.F.N., R.T.R., S.M.D.S., and B.C.D.M. conducted data analyses and interpretation of results. J.D. wrote the manuscript. K.A.D.-M., G.S., M.I.M., F.S.M., H.C.M., S.M.D.S., and B.C.D.M. carefully reviewed the manuscript. All authors read and approved the final manuscript.

We declare no competing financial interests.

Footnotes

Supplemental material for this article may be found at https://doi.org/10.1128/AEM.02675-17.

REFERENCES

  • 1.Fonty G, Gouet P, Jouany J-P, Senaud J. 1987. Establishment of the microflora and anaerobic fungi in the rumen of lambs. Microbiology 133:1835–1843. doi: 10.1099/00221287-133-7-1835. [DOI] [Google Scholar]
  • 2.Rey M, Enjalbert F, Combes S, Cauquil L, Bouchez O, Monteils V. 2014. Establishment of ruminal bacterial community in dairy calves from birth to weaning is sequential. J Appl Microbiol 116:245–257. doi: 10.1111/jam.12405. [DOI] [PubMed] [Google Scholar]
  • 3.Guzman CE, Bereza-Malcolm LT, De Groef B, Franks AE. 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. doi: 10.1371/journal.pone.0133048. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Taschuk R, Griebel PJ. 2012. Commensal microbiome effects on mucosal immune system development in the ruminant gastrointestinal tract. Anim Health Res Rev 13:129–141. doi: 10.1017/S1466252312000096. [DOI] [PubMed] [Google Scholar]
  • 5.Warner RG, Flatt WP, Loosli JK. 1956. Dietary factors influencing the development of the ruminant stomach. J Agric Food Chem 4:788–792. doi: 10.1021/jf60067a003. [DOI] [Google Scholar]
  • 6.Bauer E, Williams BA, Smidt H, Verstegen MWA, Mosenthin R. 2006. Influence of the gastrointestinal microbiota on development of the immune system in young animals. Curr Issues Intest Microbiol 7:35–51. [PubMed] [Google Scholar]
  • 7.Baldwin RL, McLeod KR, Klotz JL, Heitmann RN. 2004. Rumen development, intestinal growth and hepatic metabolism in the pre- and postweaning ruminant. J Dairy Sci 87:E55–E65. doi: 10.3168/jds.S0022-0302(04)70061-2. [DOI] [Google Scholar]
  • 8.Guilloteau P, Zabielski R, Blum JW. 2009. Gastrointestinal tract and digestion in the young ruminant: ontogenesis, adaptations, consequences and manipulations. J Physiol Pharmacol 60:37–46. [PubMed] [Google Scholar]
  • 9.Khan MA, Bach A, Weary DM, von Keyserlingk MAG. 2016. Invited review: transitioning from milk to solid feed in dairy heifers. J Dairy Sci 99:885–902. doi: 10.3168/jds.2015-9975. [DOI] [PubMed] [Google Scholar]
  • 10.Jami E, Israel A, Kotser A, Mizrahi I. 2013. Exploring the bovine rumen bacterial community from birth to adulthood. ISME J 7:1069–1079. doi: 10.1038/ismej.2013.2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Uyeno Y, Sekiguchi Y, Kamagata Y. 2010. rRNA-based analysis to monitor succession of faecal bacterial communities in Holstein calves. Lett Appl Microbiol 51:570–577. doi: 10.1111/j.1472-765X.2010.02937.x. [DOI] [PubMed] [Google Scholar]
  • 12.Dill-McFarland KA, Breaker JB, Suen G. 2017. Microbial succession in the gastrointestinal tract of dairy cows from 2 weeks to first lactation. Sci Rep 7:40864. doi: 10.1038/srep40864. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Malmuthuge N, Griebel PJ, Guan LL. 2015. The gut microbiome and its potential role in the development and function of newborn calf gastrointestinal tract. Front Vet Sci 2:36. doi: 10.3389/fvets.2015.00036. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Weimer PJ. 2015. Redundancy, resilience, and host specificity of the ruminal microbiota: implications for engineering improved ruminal fermentations. Front Microbiol 6:296. doi: 10.3389/fmicb.2015.00296. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Jewell KA, McCormick CA, Odt CL, Weimer PJ, Suen G. 2015. Ruminal bacterial community composition in dairy cows is dynamic over the course of two lactations and correlates with feed efficiency. Appl Environ Microbiol 81:4697–4710. doi: 10.1128/AEM.00720-15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Shabat Ben SK, Sasson G, Doron-Faigenboim A, Durman T, Yaacoby S, Berg Miller ME, White BA, Shterzer N, Mizrahi I. 2016. Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants. ISME J 10:2958–2972. doi: 10.1038/ismej.2016.62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Weimer PJ, Cox MS, Vieira de Paula T, Lin M, Hall MB, Suen G. 2017. Transient changes in milk production efficiency and bacterial community composition resulting from near-total exchange of ruminal contents between high- and low-efficiency Holstein cows. J Dairy Sci 100:7165–7182. doi: 10.3168/jds.2017-12746. [DOI] [PubMed] [Google Scholar]
  • 18.Malmuthuge N, Griebel PJ, Guan LL. 2014. Taxonomic identification of commensal bacteria associated with the mucosa and digesta throughout the gastrointestinal tracts of preweaned calves. Appl Environ Microbiol 80:2021–2028. doi: 10.1128/AEM.03864-13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Guzman CE, Bereza-Malcolm LT, de Groef B, Franks AE. 2016. Uptake of milk with and without solid feed during the monogastric phase: effect on fibrolytic and methanogenic microorganisms in the gastrointestinal tract of calves. Anim Sci J 87:378–388. doi: 10.1111/asj.12429. [DOI] [PubMed] [Google Scholar]
  • 20.Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. 2013. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the miseq illumina sequencing platform. Appl Environ Microbiol 79:5112–5120. doi: 10.1128/AEM.01043-13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Li RW, Connor EE, Li C, Baldwin RL VI, Sparks ME. 2012. Characterization of the rumen microbiota of pre-ruminant calves using metagenomic tools. Environ Microbiol 14:129–139. doi: 10.1111/j.1462-2920.2011.02543.x. [DOI] [PubMed] [Google Scholar]
  • 22.Oikonomou G, Teixeira AGV, Foditsch C, Bicalho ML, Machado VS, Bicalho RC. 2013. Fecal microbial diversity in pre-weaned dairy calves as described by pyrosequencing of metagenomic 16S rDNA. Associations of Faecalibacterium species with health and growth. PLoS One 8(4):e63157. doi: 10.1371/journal.pone.0063157. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Tapio I, Shingfield KJ, McKain N, Bonin A, Fischer D, Bayat AR, Vilkki J, Taberlet P, Snelling TJ, Wallace RJ. 2016. Oral samples as non-invasive proxies for assessing the composition of the rumen microbial community. PLoS One 11:e0151220. doi: 10.1371/journal.pone.0151220. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Linz AA. 2016. OTUtable: north temperate lakes—microbial observatory 16S time series data and functions. R package version 1.0.0. https://CRAN.R-project.org/package=OTUtable.
  • 25.Shah H. 1992. The genus Bacteroides and related taxa, p 3593–3607. In Balows A, Trüper H, Dworkin M, Harder W, Schleifer K (ed), The prokaryotes: a handbook on the biology of bacteria, isolation, identification, application, 2nd ed Springer Verlag, New York, NY. [Google Scholar]
  • 26.Rainey F. 2009. Family VIII. Ruminococcaceae fam. nov., p 1016–1043. In Vos P, Garrity G, Jones D, Krieg NR, Ludwig W, Rainey FA, Schleifer K-H, Whitman W (ed), Bergey's manual of systematic bacteriology, 2nd ed, vol 3 The Firmicutes. Springer Verlag, New York, NY. [Google Scholar]
  • 27.Rosenberg E. 2014. The Family Prevotellaceae, p 825–827. In Rosenberg E, DeLong E, Lory S, Stackenbrandt E, Thomson F (ed), The prokaryotes, 4th ed Springer Verlag, Berlin, Germany. [Google Scholar]
  • 28.de Oliveira MNV, Jewell KA, Freitas FS, Benjamin LA, Tótola MR, Borges AC, Moraes CA, Suen G. 2013. Characterizing the microbiota across the gastrointestinal tract of a Brazilian Nelore steer. Vet Microbiol 164:307–314. doi: 10.1016/j.vetmic.2013.02.013. [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. Sci Rep 5:16116. doi: 10.1038/srep16116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Myer PR, Wells JE, Smith TPL, Kuehn LA, Freetly HC. 2015. Microbial community profiles of the colon from steers differing in feed efficiency. Springerplus 4:454. doi: 10.1186/s40064-015-1201-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Myer PR, Wells JE, Smith TPL, Kuehn LA, Freetly HC. 2016. Microbial community profiles of the jejunum from steers differing in feed efficiency. J Anim Sci 94:327–338. doi: 10.2527/jas.2015-9839. [DOI] [PubMed] [Google Scholar]
  • 32.Myer PR, Smith TPL, Wells JE, Kuehn LA, Freetly HC. 2015. Rumen microbiome from steers differing in feed efficiency. PLoS One 10(6):e0129174. doi: 10.1371/journal.pone.0129174. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Shapira M. 2016. Gut microbiotas and host evolution: scaling up symbiosis. Trends Ecol Evol 31:539–549. doi: 10.1016/j.tree.2016.03.006. [DOI] [PubMed] [Google Scholar]
  • 34.Dias J, Marcondes MI, Noronha MF, Resende RT, Machado FS, Mantovani HC, Dill-McFarland KA, Suen G. 2017. Effect of pre-weaning diet on the ruminal archaeal, bacterial, and fungal communities of dairy calves. Front Microbiol 8:1553. doi: 10.3389/fmicb.2017.01553. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Downes J, Olsvik B, Hiom SJ, Spratt DA, Cheeseman SL, Olsen I, Weightman AJ, Wade WG. 2000. Bulleidia extructa gen. nov., sp. nov., isolated from the oral cavity. Int J Syst Evol Microbiol 50(Part 3):979–983. doi: 10.1099/00207713-50-3-979. [DOI] [PubMed] [Google Scholar]
  • 36.Bosshard PP, Zbinden R, Altwegg M. 2002. Turicibacter sanguinis gen. nov., sp. nov., a novel anaerobic, Gram-positive bacterium. Int J Syst Evol Microbiol 52:1263–1266. doi: 10.1099/00207713-52-4-1263. [DOI] [PubMed] [Google Scholar]
  • 37.Morotomi M, Nagai F, Sakon H, Tanaka R. 2009. Paraprevotella clara gen. nov., sp. nov. and Paraprevotella xylaniphila sp. nov., members of the family “Prevotellaceae” isolated from human faeces. Int J Syst Evol Microbiol 59:1895–1900. doi: 10.1099/ijs.0.008169-0. [DOI] [PubMed] [Google Scholar]
  • 38.Purushe J, Fouts DE, Morrison M, White BA, Mackie RI, Coutinho PM, Henrissat B, Nelson KE. 2010. Comparative genome analysis of Prevotella ruminicola and Prevotella bryantii: insights into their environmental niche. Microb Ecol 60:721–729. doi: 10.1007/s00248-010-9692-8. [DOI] [PubMed] [Google Scholar]
  • 39.Le Huerou I, Guilloteau P, Wicker C, Mouats a, Chayvialle J a, Bernard C, Burton J, Toullec R, Puigserver A. 1992. Activity distribution of seven digestive enzymes along small intestine in calves during development and weaning. Dig Dis Sci 37:40–46. doi: 10.1007/BF01308340. [DOI] [PubMed] [Google Scholar]
  • 40.Gilbert MS, Pantophlet AJ, Berends H, Pluschke AM, van den Borne JJ, Hendriks WH, Schols HA, Gerrits WJ. 2015. Fermentation in the small intestine contributes substantially to intestinal starch disappearance in calves. J Nutr 145:1147–1155. doi: 10.3945/jn.114.208595. [DOI] [PubMed] [Google Scholar]
  • 41.Liu J, Bian G, Sun D, Zhu W, Mao S. 2017. Starter feeding supplementation alters colonic mucosal bacterial communities and modulates mucosal immune homeostasis in newborn lambs. Front Microbiol 8:429. doi: 10.3389/fmicb.2017.00429. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Satter LD, Esdale WJ. 1968. In vitro lactate metabolism by ruminal ingesta. Appl Microbiol 16:680–688. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Gänzle MG, Follador R. 2012. Metabolism of oligosaccharides and starch in lactobacilli: a review. Front Microbiol 3:340. doi: 10.3389/fmicb.2012.00340. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Foditsch C, Santos TMA, Teixeira AGV, Pereira RVV, Dias JM, Gaeta N, Bicalho RC. 2014. Isolation and characterization of Faecalibacterium prausnitzii from calves and piglets. PLoS One 9(12):e116465. doi: 10.1371/journal.pone.0116465. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Guilloteau P, Martin L, Eeckhaut V, Ducatelle R, Zabielski R, Van Immerseel F. 2010. From the gut to the peripheral tissues: the multiple effects of butyrate. Nutr Res Rev 23:366–384. doi: 10.1017/S0954422410000247. [DOI] [PubMed] [Google Scholar]
  • 46.Stevenson DM, Weimer PJ. 2007. Dominance of Prevotella and low abundance of classical ruminal bacterial species in the bovine rumen revealed by relative quantification real-time PCR. Appl Microbiol Biotechnol 75:165–174. doi: 10.1007/s00253-006-0802-y. [DOI] [PubMed] [Google Scholar]
  • 47.Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, Glöckner FO. 2013. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res 41:e1. doi: 10.1093/nar/gks808. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, Sahl JW, Stres B, Thallinger GG, Van Horn DJ, Weber CF. 2009. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75:7537–7541. doi: 10.1128/AEM.01541-09. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J, Glockner FO. 2007. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res 35:7188–7196. doi: 10.1093/nar/gkm864. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. 2011. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27:2194–2200. doi: 10.1093/bioinformatics/btr381. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL. 2006. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 72:5069–5072. doi: 10.1128/AEM.03006-05. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Venables WN, Ripley BD. 2002. Modern applied statistics with S, 4th ed Springer, New York, NY. [Google Scholar]
  • 53.Snedecor GW, Cochran WG. 1989. Statistical methods, 8th ed Iowa State University Press, Ames, IA. [Google Scholar]
  • 54.Hothorn T, Bretz F, Westfall P. 2008. Simultaneous inference in general parametric models. Biom J 50:346–363. doi: 10.1002/bimj.200810425. [DOI] [PubMed] [Google Scholar]
  • 55.Legendre P, Legendre L. 1998. Numerical ecology: developments in environmental modelling, 2nd English ed Elsevier, Amsterdam, Netherlands. [Google Scholar]
  • 56.Chen H. 2015. VennDiagram: generate high-resolution Venn and Euler plots. R package version 1.6.18. https://CRAN.R-project.org/package=VennDiagram.
  • 57.Oksanen J, Guillaume Blanchet F, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O'Hara RB, Simpson GL, Solymos P, Stevens MHM, Szoecs E, Wagner H. 2016. vegan: community ecology package. R package version 2.3-4. https://CRAN.R-project.org/package=vegan.
  • 58.Wickham H. 2009. ggplot2: elegant graphics for data analysis. Springer-Verlag, New York, NY: https://cran.r-project.org/package=ggplot2. [Google Scholar]
  • 59.Larson LL, Owen FG, Albright JL, Appleman RD, Lamb RC, Muller LD. 1977. Guidelines toward more uniformity in measuring and reporting calf experimental data. J Dairy Sci 60:989–991. doi: 10.3168/jds.S0022-0302(77)83975-1. [DOI] [Google Scholar]

Associated Data

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

Supplementary Materials

Supplemental material

Articles from Applied and Environmental Microbiology are provided here courtesy of American Society for Microbiology (ASM)

RESOURCES