Fecal microbiota dynamics and its relationship to diarrhea and health in dairy calves

Hongwei Chen; Yalu Liu; Kailang Huang; Bin Yang; Yuanyuan Zhang; Zhongtang Yu; Jiakun Wang

doi:10.1186/s40104-022-00758-4

Fecal microbiota dynamics and its relationship to diarrhea and health in dairy calves

Chen, Hongwei; Liu, Yalu; Huang, Kailang; Yang, Bin; Zhang, Yuanyuan; Yu, Zhongtang; Wang, Jiakun 2022-10-28 00:00:00 Background: Diarrhea is a major cause of morbidity and mortality in young calves, resulting in considerable eco- nomic loss for dairy farms. To determine if some gut microbes might have resistance to dysbiotic process with calf diarrhea by dictating the microbial co-occurrence patterns from birth to post-weaning, we examined the dynamic development of the gut microbiota and diarrhea status using two animal trials, with the first trial having 14 Holstein dairy calves whose fecal samples were collected 18 times over 78 d from birth to 15 d post-weaning and the second trial having 43 Holstein dairy calves whose fecal samples were collected daily from 8 to 18 days of age corresponding to the first diarrhea peak of trial 1. Results: Metataxonomic analysis of the fecal microbiota showed that the development of gut microbiota had three age periods with birth and weaning as the separatrices. Two diarrhea peaks were observed during the transi- tion of the three age periods. Fusobacteriaceae was identified as a diarrhea-associated taxon both in the early stage and during weaning, and Clostridium_sensu_stricto_1 was another increased genus among diarrheic calves in the early stage. In the neonatal calves, Prevotella_2 (ASV4 and ASV26), Prevotella_9 (ASV43), and Alloprevotella (ASV14) were negatively associated with Clostridium_sensu_stricto_1 (ASV48), the keystone taxa of the diarrhea-phase mod- ule. During weaning, unclassified Muribaculaceae (ASV28 and ASV44), UBA1819 (ASV151), Barnesiella (ASV497), and Ruminococcaceae_UCG-005 (ASV254) were identified being associated with non-diarrheic status, and they aggregated in the non-diarrhea module of co-occurrence patterns wherein unclassified Muribaculaceae (ASV28) and Barnesiella (ASV497) had a direct negative relationship with the members of the diarrhea module. Conclusions: Taken together, our results suggest that the dynamic successions of calf gut microbiota and the inter- actions among some bacteria could influence calf diarrhea, and some species of Prevotella might be the core micro - biota in both neonatal and weaning calves, while species of Muribaculaceae might be the core microbiota in weaning calves for preventing calf diarrhea. Some ASVs affiliated with Prevotella_2 (ASV4 and ASV26), Prevotella_9 (ASV43), Alloprevotella (AVS14), unclassified Muribaculaceae (ASV28 and ASV44), UBA1819 (ASV151), Ruminococcaceae_UCG-005 (ASV254), and Barnesiella (ASV497) might be proper probiotics for preventing calf diarrhea whereas Clostridium_sensu_ stricto_1 (ASV48) might be the biomarker for diarrhea risk in specific commercial farms. Keywords: Calf diarrhea, Co-occurrence pattern, Dynamic development, Fecal microbiota Background *Correspondence: jiakunwang@zju.edu.cn Diarrhea is one of the most common causes of mor- Institute of Dairy Science, College of Animal Sciences, Zhejiang University, bidity and mortality in young calves, especially dairy Hangzhou, China Full list of author information is available at the end of the article calves younger than one month [1, 2]. In the US, the © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 2 of 20 National Animal Health Monitoring System estimated weaning increase the risk of enteric diseases, particularly that more than half of the calf deaths were due to diar- diarrhea [17, 18]. rhea and related diseases [3]. A similar mortality rate Interestingly, some calves are prone to diarrhea, among dairy calves attributable to diarrhea was also whereas others are resistant to diarrhea. In the 56-day reported in Korea [1]. Calf diarrhea not only incurs experiment of Ma et. al [19], 13 out 42 milk replacer fed treatment cost but also lowers daily weight gain in calves never exhibited diarrhea, 18 calves exhibited diar- calves [4], delays first conceptions [5], decreases milk rhea and recovered after treated with electrolyte, but 11 production in the first lactation [6], consequently calves exhibited diarrhea and needed to be treated with resulting in a great economic loss for dairy farms. therapeutic antimicrobials. Ma et al. defined the lat - Many pathogens have been implicated in calf diar- ter two diarrhea status as resistant to diarrhea-induced + + rhea, including Escherichia coli K99 (E. coli K99 ), dysbiosis, and susceptible to diarrhea-induced dysbio- Salmonella spp., Clostridium perfringens, Crypto- sis, respectively. The age of first diarrhea of these calves sporidium parvum, bovine rotavirus (BRV), and bovine varied from d 8 to 19. Using the random forest machine coronavirus (BCoV) [1]. Non-infectious factors such as learning algorithm with the microbiota collected from improper colostrum management [7], weaning stress health calves at 7, 14, and 21 days of age and diarrhea [8], and poor feeding environments can also increase calves prior to the onset of diarrhea, Ma et al. suggest that the risk of calf diarrhea [9]. Moreover, oftentimes diarrhea could be predicted by the microbiota shift in uncertain causality of diarrhea occurrence and cross early life. Kim et al. [20] described the similar age (5–50 infection together make diarrhea difficult to prevent days of age) fecal microbiota transplantation could ame- and treat. liorate calf diarrhea with increasing the family Porphy- Diarrhea is much more common among young ani- romonadaceae. Therefore, we hypothesized that the gut mals, especially after birth to shortly after weaning than microbiota might strongly correlate to calf diarrhea for among adult animals because of higher risk of enteric some gut microbes might have resistance to the dysbiotic pathogens colonization in young animals than in adult process with calf diarrhea from birth to post-weaning. animals [10, 11]. The high risk of colonization by patho - To test our hypothesis, the stages with high incidence of gens in young animals stems from their underdeveloped diarrhea were identified in cohort from birth to shortly gut and gut microbiota. Indeed, Kim et al. found that the after weaning, and the fecal microbiotas between diar- absence of some species of Clostridia in the gut micro- rheic calves and non-diarrheic calves in stages of high biota of neonatal mice made them unable to resist the incidence of diarrhea, and between diarrhea phases (pre- colonization by Salmonella enterica Typhimurium or diarrhea, diarrhea and post-diarrhea) were compared, Citrobacter rodentium [12]. In addition, dysbiosis of gut and the diarrheic status- and diarrhea phase-associated microbiota induced by certain factors such as antibiot- amplicon sequence variants (ASVs) and modules were ics and diseases can also lower resistance and increase identified. enteric pathogen infection. Wu et al. demonstrated that dysbiosis of gut microbiota in rat induced by antibiot- Methods ics lowered the resistance to colonization by Salmonella Animal experiment and sample collection [13]. Compared to uninfected calves, calves infected Two animal trials were conducted on a commercial farm with rotavirus have a higher abundance of Escherichia, located in Shaoxing (more than 3800 cows in stock), Clostridium_g21, Streptococcus, and Clostridium, but Zhejiang Province, China from September to Novem- a lower abundance of Lactobacillus, Subdoligranulum, ber 2017 (trial 1) and November to December 2017 Blautia, and Coprococcus_g2 in the fecal microbiota [14]. (trial 2). In trial 1, 14 female Holstein calves (initial Weaning is a stressful process for young animals bodyweight = 38.2 ± 2.0 kg , mean ± SD) were enrolled and can lower resistance to pathogenic colonization. at birth. Immediately after birth, the calves were sepa- Haag et al. found that infant mice after weaning were rated from their dams and moved to individual pens deficient in preventing their gut from colonization by (1.0 m × 1.2 m) bedded with dry wheat straw and each Campylobacter jejuni [15]. During weaning, dairy calves offered 4 L of thawed colostrum (pooled the colostrum are stressed nutritionally, and their gut microbiota from other mothers before the trial, and stored at – 80 undergoes compositional changes [16]. Typically, dur- ºC) via an esophageal tube within 2 h after birthing. All ing weaning, the gut microbiota increases its diversity, neonatal calves in this trial were fed the same colos- and some important taxa, such as Bacteroides, Blau- trum. From 1 to 5 days of age, each calf was bottle-fed tia, Ruminococcus, and Succinivibrio, may change their 2 L of whole milk each at 0700, 1330, and 1800 h. Dur- abundance [17]. The nutritional stresses coupled with ing these 5 d, 4 g each of tylosin tartrate and sulfadimi- changes in gut microbiota during and immediately after dine soluble (both powder) was mixed with the morning Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 3 of 20 milk and fed to each calf for prophylaxis of pneumo- TAT CTAAT-3’) to prepare amplicon libraries with each nia. At d 6, all the calves were moved to one group pen being labeled with a unique barcode sequence [23]. The (8.0 m × 10.0 m) bedded with a rubber mat and dry amplicon libraries of all the samples were pooled in wheat straw. The straw was replaced every other day. The equal molar ratio and paired-end (2 × 250) sequenced calves had free access to a preset volume of whole milk on the Illumina HiSeq platform by Novogene Bioin- (Additional file 1: Fig. S1) with an automated milk feeder formatics Technology Co., Ltd. (Tianjin, China). After system (Förster-Technik, Engen, Germany). Briefly, the demultiplexing, the paired-end sequencing reads were calves get 6 L/d of whole milk from d 6 and this amount processed using the DADA2 package (version 1.8.0) increased evenly up to 8 L/d on d 22. From d 23 to 41, in R and its pipeline [24]. Briefly, barcodes and primers each calf received 8 L of whole milk daily, and from d were removed from the reads. Reads with more than 2 42 to 62, the milk allowance was decreased evenly from expected errors (maxN = 0, maxEE = c(2, 2), truncQ = 2) 8 to 2 L/d. Milk supply was denied using the automated were filtered out. Dereplication and inference were per - milk feeder system once a calf consumed 2 L of milk formed using the DADA2 pipeline. After merging the within 2 h during each meal to avoid overconsumption paired reads and chimera filtering, an ASV table was of milk. All the calves were weaned off milk at d 63. Calf constructed (to resolve bacteria at the species level [25]). starter pellets and a hay mixture of oat and alfalfa were The ASVs were taxonomically assigned based on the offered ad libitum in the group pen. All the calves had SILVA 16S rRNA gene database (version 132) [26] using free access to clean drinking water throughout the trial. a naive Bayesian classifier method [27] implemented in Fecal samples were collected directly via rectal stimula- DADA2. The sequences of each sample were rarefied to tion from every calf of the trial at d 1, 3, 5, 7, 9, 12, 15, same size with the minimum number of sequences in 18, 28, 38, 48, 58, 62, 63, 65, 68, 73, and 78 (18 times in samples (35,137 sequences/sample in trial 1, and 19,986 total). The fecal samples were placed in 2 mL nuclease- sequences/sample in trial 2) using the ‘rarefy_even_ free tubes, immediately frozen in liquid nitrogen, then depth’ function in Phyloseq package (version 1.24.2) [28], each month the 15 L liquid nitrogen tank with samples and ASVs that appeared only once among all the samples was transported to the lab and samples were stored at were removed from the dataset. Alpha diversity metrics – 80 °C. The DNA of all the stored fecal samples was including observed species and Shannon diversity index extracted in one month after the trial. When fecal sam- were calculated using the Phyloseq package. Faith’s phy- ples were collected, fecal scores were recorded based logenetic diversity (Faith’s PD) was calculated using the on fecal fluidity [21]: 1 = normal, 2 = soft, 3 = runny, or Picante package (version 1.8.2) in R, and evenness was 4 = watery. Calves that had a fecal score of 3 or 4 were calculated using the Microbiome package (version 1.12.0) considered diarrheic. No additional antibiotics were in R. To minimize individual variance of calves, only the offered to calves after the diarrhea episode. ASVs that were observed in at least 20% of calves at every In trial 2, 43 female Holstein calves (initial body- single day (3 out of 14 calves in trial 1) or every single weight = 36.6 ± 1.9 kg, mean ± SD) were enrolled at birth. diarrheic status-associated phase (trial 2) were subjected The calves were fed, nursed, and sampled exactly the to the downstream analysis. same as for those in trial 1, but fecal samples were col- lected daily from d 8 to 18, which corresponded to the Pathogen detection age when the first diarrhea peak was observed in trial 1. Major pathogens that can lead to calf diarrhea were tested in the fecal samples of trial 1 when diarrhea (fecal Metataxonomic analysis of the fecal microbiota score ≥ 3) was first noted in a calf. Salmonella spp. was DNA was extracted from individual fecal samples accord- detected by PCR with specific primers (F:5′-TCG TCA ing to the method of Zoetendal et al. with minimal modi-TTC CAT TAC CTA CC-3′ and R:5′-AAA CGT TGA AAA fications [22]. In brief, approximately 0.2 g of frozen fecal ACT GAG GA-3′ [29] using fecal DNA samples. A com- sample each was homogenized together with 1 mL of TE mercial ELISA kit (BIO K 315, Bio-X Diagnostics, Roche- buffer using bead-beating (Biospec Products; Bartlesville, fort, Belgique) each was used to detect BRV, BCoV, and OK, United States) followed by phenol:chloroform-based E. coli (through its F5 attachment factor) in the fecal DNA extraction. Agarose gel (1%) electrophoresis was samples. performed to evaluate the DNA quality, and the DNA concentrations were determined using a NanoDrop Evaluation of the effects of age, diarrheic status, 2000 spectrophotometer (Thermo Scientific, Waltham, and diarrheic phases on fecal microbiota MA, United States). The V3-V4 region of the 16S rRNA A generalized linear mixed-effects model implemented gene was amplified using primers 341F (5’-CCT AYG in the nlme package (version 3.1.137) [30] in R was used GGRBGCASCAG-3’) and 806R (5’-GGA CTA CNNGGG to evaluate the effect of age and diarrhea on the alpha Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 4 of 20 diversity metrics of the fecal microbiota, and Tukey’s all- performed to estimate the optimal number of top-rank- pair comparison test using the ‘glht’ function in the mult- ing age-associated genera required for prediction using comp package (version 1.4.8) [31] in R was used to do the rfcv function implemented in the randomForest the multiple comparisons. In trial 1, the model included package in R. The identified genera were shown in a heat - calf age and diarrheic status (diarrheic vs. non-diarrheic map with their relative abundance. These genera were calves) among the calves as fixed effects and individual considered microbial markers of the respective ages. calves as random effect: Y = μ + A + H + I + ε ijk i j k ijk Fecal microbiota comparison between diarrheic statuses, between diarrhea phases, and identification of their In trial 2, the model included diarrhea phases (pre- associated ASVs and modules diarrhea, diarrhea, and post-diarrhea, see the results for Fecal microbiotas between the diarrheic and non-diar- the delineation of diarrhea phases) as fixed effect and rheic calves in trial 1 and between diarrhea phases (pre- individual calves as random effect: diarrhea, diarrhea, and post-diarrhea) in trial 2 were Y = μ + S + I + ε mk m k mk compared using principal coordinates analysis (PCoA) and permutational multivariate analysis of variance (PER- where Y and Y represent the variable of interest; ijk mk MANOVA/adonis) based on Bray–Curtis dissimilar- A is the fixed effect of calf age; H is the fixed effect of i j ity. When the fecal microbiotas of diarrheic calves were calf diarrheic status, defined as non-diarrheic or diar - significantly different from those of age-matched non- rheic status based on fecal score; S is the fixed effect of diarrheic calves, in trial 1, and when the fecal microbio- calf diarrhea phases, defined as pre-diarrhea phase, diar - tas of the calves with diarrhea differed significantly from rhea phase, or post-diarrhea phase based on the temporal those at their pre- or post-diarrhea phase in trial 2, Lin- changes of fecal scores of the same calves; I is the ran- ear discriminant analysis Effect Size (LEfSe) [35] and sig - dom effect of individual calves; and ε and ε are the ijk mk nificance test with DESeq2 [36] were used to identify the residual error. Non-parametric Kruskal–Wallis test was ASVs that might be associated with one of the diarrheic used to assess the effects of age and diarrhea on bacterial statuses or one of the diarrhea phases. Of the ASVs with relative abundance, and Dunn’s all-pairs rank comparison an LDA score > 2 in LEfSe or an adjusted P-value < 0.05 in test with P adjusted by false discovery rate was used to DESeq2, those with a log fold change > 1 (diarrheic/non- conduct multiple comparisons. A significant change was diarrheic calves, or diarrhea/pre- or post-diarrhea phase) declared with P < 0.05. were considered to be associated with diarrheic status or phase, whereas those with a log fold change < – 1 (defined the same as above) were considered associated Fecal microbiota comparison among ages with non-diarrheic status or phase. and identification of age‑associated genera of bacteria Co-occurrence patterns of the fecal microbiotas of the In trial 1, the overall fecal microbiotas between two diarrheic and non-diarrheic calves (see the results for the ages were pairwise compared using analysis of similar- delineation of the stages) in trial 1, or the fecal microbi- ity (ANOSIM) implemented in the Vegan package (ver- ota of the calves at different diarrhea phases were exam - sion 2.5.3) [32] in R. When P < 0.05, the fecal microbiotas ined using the SparCC algorithm [37] with ASV count between two ages were considered completely different table as the input data. The pattern was visualized using (R-value > 0.75), different (0.5 < R-value < 0.75), or tended the igraph package (version 1.2.5) [38] in R, and correla- to be different (0.3 < R-value < 0.5). R-value < 0.3 was con- tions with a P < 0.05 and a co-efficient R ≥ 0.5 or ≤ – 0.5 sidered not different. being considered positive and negative correlations, Random forest regression was used to identify the fecal respectively. Modules of the co-occurrence patterns bacterial genera that were associated with the age of the were generated using the walktrap algorithm [39] imple- calves using the randomForest package (version 4.6.14) mented in igraph. Modules with less than 3 nodes were [33, 34] in R. The genus table of all the samples was the deleted from the co-occurrence patterns. The identified input data. The random forest algorithm was executed ASVs associated with a diarrheic status or diarrhea phase with the default parameters (ntree = 1000, default mtry of were highlighted in the patterns. The modules aggregated p/3, where p is the number of input genera (‘features’)). with ASVs that were associated with diarrhea in trial 1 The importance of a genus was ranked in the order of or with the diarrhea phase in trial 2 were considered as its ‘feature importance’, with feature importance being diarrhea modules and diarrhea-phase modules, respec- the decrease in prediction accuracy (in percent) of the tively, whereas the modules formed with ASVs that were model when that genus was removed. To explore the age- not with diarrhea in trial 1 or associated with pre- or associated microbiota development, cross-validation was Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 5 of 20 post-diarrhea phases in trial 2 were considered as non- Prevotellaceae_UCG-003, Rikenellaceae_RC9_gut_group, diarrhea modules or pre- or post-diarrhea modules. Ruminococcaceae_UCG-005, Ruminococcaceae_UGC-010, and Lachnospiraceae_FCS020_group increased their rela- Results tive abundance (P < 0.05) during the later days of the trial Trial 1 and maintained or decreased their relative abundance Development of fecal bacterial microbiota of calves until the end of the trial. Acinetobacter differed from all and temporal microbial successions the other genera as it lost its initial high relative abundance In total, 15,283,464 quality-filtered amplicon sequences dramatically by d 3 (P < 0.05) and never recovered. Prevo- were obtained from 251 fecal samples (the fecal sample tella_9 only increased (P < 0.05) on d 9. of calf Y03 on d 5 was not analyzed due to contamina- tion with wheat straw) with an average of 60,890 ± 7193 Composition and distribution of age‑associated bacterial (mean ± SD) sequences per sample. The sequencing genera from birth to post‑weaning depth coverage reached > 99.96% on average (99.89% to Pair-wise comparison of the fecal microbiotas at the 100.00%). In the fecal samples collected from 1 day of age, ASV level among the 18 time points using ANOSIM 345 ASVs (referred to as species hereafter) on average per revealed three age periods, with the first, second, and sample were identified with a Shannon diversity index of third age periods being from d 1 to 12, 15 to 63, and 58 4.03 (Fig. 1A). The number of observed species, Faith’s to 78, respectively. The fecal microbiotas were simi - PD, Shannon diversity index, and evenness decreased lar (R-value < 0.5) within each age period but different from d 1 to 7 but recovered at d 9. Then, observed spe - between most of the two age periods (Table 1). cies, Faith’s PD, Shannon index, and evenness gradually Thirty-five age-associated bacterial genera were identi - increased, though with fluctuation, to about 509, 28.77, fied by random forest regression (Fig. 2A and B), and they 5.03 and 0.81, respectively, at d 78 (Fig. 1A). Over this were distributed in 4 clusters (Fig. 2C) each corresponding period, age significantly (P < 0.05) affected all the diversity to one of the age periods (Table 1). Of these age-associated metrics, whereas diarrheic status did not affect (P > 0.05) genera, Klebsiella, Escherichia/Shigella, Enterococcus, Bac- any of these four metrics. teroides, Butyricicoccus and Megamonas in the first cluster Collectively, the ASVs were classified into 200 genera were predominant at the early age (d 1 to 12); Alloprevotella, within 12 phyla. Bacteroidota, Firmicutes, Proteobacte- Faecalibacterium, Intestinimonas, Paraprevotella, UBA1819, ria, and Fusobacteria each had a relative abundance > 1% Lachnospiraceae_UCG-010, Pygmaiobacter, and Subdol- in more than 60% of the fecal samples at any day (Fig. 1B). igranulum in the second cluster were predominant at d 15 to Of the 200 bacterial genera identified across all the fecal 58; Blautia, Breznakia, Agathobacter, Anaeroplasma, Rom- samples, 15 genera each had a relative abundance > 1% boutsia, Erysipelotrichaceae_UCG-004, Prevotella_9, and Suc- in at least 60% of the fecal samples at a single day. These cinivibrio in the third cluster and Candidatus_Stoquefichus, genera included Alloprevotella, Bacteroides, Escherichia/ Parasutterella, Lachnospiraceae_NK4A136_group, Oscillibac- Shigella, Faecalibacterium, Fusobacterium, Acinetobacter, ter, Prevotellaceae_UCG-003, Family_XIII_AD3011_group, Prevotellaceae_UCG-003, Rikenellaceae_RC9_gut_group, Lachnospiraceae_FCS020_group, Rikenellaceae_RC9_gut_ Ruminococcaceae_UCG-005, Ruminococcaceae_UGC-010, group, Prevotellaceae_UCG-001, Ruminococcaceae_UCG-005, Butyricicoccus, Lachnospiraceae_FCS020_group, Para- Ruminococcaceae_UCG-010, Negativibacillus, and Tyzzerella bacteroides, Prevotella_9 and Sutterella. These pre - in the fourth cluster were predominant at d 48 to 68 and d 58 dominant genera displayed temporal changes in relative to 78, respectively. abundance over the course of the trial (Fig. 1C). Allo- prevotella, Faecalibacterium, Parabacteroides, and Sut- Diarrhea characteristics of the study cohort terella increased their relative abundance (P < 0.05) Over the course of the trial 1, all the calves had fecal and maintained a higher abundance around d 15 to score ≥ 3 at least one sampling day (Fig. 3A). Based on 58 compared to d 1 and then decreasing towards the fecal scores of all the calves, the 78 d of trial was the end of the trial. Compared to d 1, Bacteroides, divided into five stages: stage 1: d 1 to 7, before the first Escherichia/Shigella, Fusobacterium, and Butyricicoc- diarrhea peak; stage 2: d 9 to 15, the first diarrhea peak; cus increased and then decreased their relative abun- stage 3: d 18 to 38, the stage after the first peak but dance sharply (P < 0.05) at around d 10. On the contrary, before the second diarrhea peak; stage 4: d 48 to 68, the (See figure on next page.) Fig. 1 Dynamic changes of fecal bacterial microbiota of calves from birth to post-weaning (trial 1). Dynamic changes of alpha diversity metrics with the red boxes indicating the diarrhea peaks (A), bacterial phyla (B), and major genera (C) across ages. Only the phyla and genera each with a relative abundance > 1% in at least 60% of samples at any single age were shown. The relative abundance significantly differing from that of d 1 is indicated with a * (P < 0.05) Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 6 of 20 Fig. 1 (See legend on previous page.) Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 7 of 20 Table 1 A matrix of R-values of pair-wise comparison of the fecal microbiota of trial one at the ASV level using ANOSIM R 1 3 5 7 9 12 15 18 28 38 48 58 62 63 65 68 73 78 1 0 3 0.43 0 * * 5 0.41 0.20 0 * * * 7 0.45 0.40 0.10 0 * * * 9 0.43 0.62 0.38 0.07 0 * * * * * 12 0.46 0.73 0.60 0.41 0.25 0 * * * * * 15 0.57 0.89 0.83 0.70 0.53 0.09 0 * * * * * * 18 0.68 0.92 0.91 0.83 0.74 0.39 0.02 0 * * * * * * * * 28 0.68 0.97 0.96 0.86 0.67 0.59 0.24 0.20 0 * * * * * * * * * 38 0.67 0.96 0.96 0.83 0.64 0.55 0.37 0.44 0.12 0 * * * * * * * * * * 48 0.66 0.95 0.93 0.87 0.73 0.59 0.28 0.18 0.10 0.11 0 * * * * * * * * * * * 58 0.64 0.91 0.89 0.76 0.63 0.56 0.49 0.58 0.48 0.29 0.27 0 * * * * * * * * * * * 62 0.61 0.88 0.84 0.78 0.60 0.48 0.45 0.60 0.51 0.30 0.30 0.03 0 * * * * * * * * * * * 63 0.62 0.90 0.86 0.76 0.58 0.47 0.42 0.56 0.53 0.34 0.31 0.00 ‑0.06 0 * * * * * * * * * * * * 65 0.68 0.88 0.83 0.77 0.56 0.57 0.57 0.75 0.73 0.57 0.63 0.27 0.08 0.04 0 * * * * * * * * * * * * * * 68 0.61 0.83 0.81 0.76 0.59 0.51 0.52 0.68 0.68 0.57 0.60 0.42 0.24 0.19 0.01 0 * * * * * * * * * * * * * * 73 0.63 0.90 0.86 0.79 0.60 0.57 0.56 0.76 0.74 0.59 0.64 0.39 0.20 0.15 0.01 ‑0.03 0 * * * * * * * * * * * * * * * 78 0.72 0.95 0.94 0.87 0.71 0.65 0.67 0.85 0.87 0.77 0.79 0.56 0.36 0.28 0.14 0.07 ‑0.05 0 Represents the pair-wise comparison with P-value < 0.05. Those with R-value < 0.5 are blod With P-value < 0.05, the fecal microbiotas of two ages were considered completely different at R-value > 0.75; different at 0.5 < R-value < 0.75; tended to be different at a 0.3 < R-value < 0.5; not different at R-value < 0.3 second diarrhea peak; and stage 5: d 75 to 78, the stage Ruminococcaceae_UCG-005 (8 ASVs), Flavonifractor (4 after the second diarrhea peak. All the 14 calves were ASVs) and Bacteroides (3 ASVs). non-diarrheic in stages 1, 3, and 5. Pathogen detection The co-occurrence pattern for stage 4 had 195 nodes, showed that all the 20 diarrheic fecal samples (9 in the 603 edges, and 17 modules (Fig. 4A, Additional file 3: first and 11 in the second diarrhea peaks) were Salmo - Table S1). Nineteen diarrhea-associated ASVs and 5 non- nella spp. and BCoV negative, but eight were E. coli diarrhea-associated ASVs showed up in the co-occur- K99 positive (2 in the first and 6 in the second diar - rence pattern. The diarrhea-associated ASVs including rhea peaks). One of the diarrheic samples (in the second Prevotellaceae_UCG-003 (ASV11, ASV43), Ruminococ- diarrhea peak) was both BRV and E. coli K99 positive caceae_UCG-010 (ASV427, ASV574), Butyricicoccus (Fig. 3A). (ASV130), Lachnospiraceae_FCS020_group (ASV234) and unclassified Ruminococcaceae (ASV557) were scat - ASVs and modules associated with diarrheic status tered without aggregation in any of the modules. Bacte- The fecal microbiotas differed among the five stages roides (ASV170, ASV206), Rikenellaceae_RC9_gut_group (P < 0.001, Fig. 3B). The fecal microbiotas of diarrheic and (ASV33), Ruminococcaceae_UCG-010 (ASV196), Lach- non-diarrheic calves did not differ (P = 0.450) in stage 2 nospiraceae_FCS020_group (ASV174), unclassified (Fig. 3C) but did differ (P = 0.004) in stage 4 (Fig. 3D). Bacteroidales (ASV177), unclassified Barnesiellaceae Based on fold change and LEfSe or DESeq2 analysis, (ASV22) and unclassified Ruminococcaceae (ASV340) 147 diarrheic status-associated ASVs were identified in were aggregated in diarrhea module 1 (D-M1), while stage 4 including 91 diarrhea-associated ASVs and 56 Bacteroides (ASV39, ASV41, ASV145, and ASV259) non-diarrhea-associated ASVs (Additional file 2: Fig. were aggregated in diarrhea module 2 (D-M2). The non- S2). The diarrhea-associated ASVs mainly consisted of diarrhea-associated Muribaculaceae (ASV28 and ASV44) Bacteroides (15 ASVs), Ruminococcaceae_UCG-005 and UBA1819 (ASV151) were aggregated in non-diar- (8 ASVs), Ruminococcaceae_UCG-010 (6 ASVs), rhea module (ND-M). The non-diarrhea-associated Ruminococcaceae_UCG-013 (4 ASVs) and Barnesiella (ASV497) and Ruminococcaceae_UCG-005 Lachnospiraceae_FCS020_group (4 ASVs). The (ASV254) were aggregated in D-M1, and they formed non-diarrhea-associated ASVs mainly consisted of negative correlations with the ASVs in D-M1. The ASVs in D-M1 and D-M2 had a higher (P < 0.05) total relative Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 8 of 20 Fig. 2 Age-associated bacterial genera from birth to post-weaning (trial 1). The top 35 age-associated bacterial genera ranked by importance to the accuracy of the random forest regression model (A). Ten-fold cross-validation error as a function of the number of input genera was used to regress against the chronologic age of calves. The dotted line indicates the 35 genera used in the model (B). Heatmap of the top 35 age-associated genera and the clusters they formed based on their relative abundance across ages (C) abundance in diarrheic calves than in non-diarrheic d 8 to 18 respectively. In which, 32 calves had one or calves (Fig. 4B). The D-M1 was mainly occupied by more episodes of diarrhea 1 to 5 d after recovering from Bacteroides, unclassified Barnesiellaceae, and Rikenel - a previous episode (Fig. 5A). Except of d 9, 10 and 11, laceae_RC9_gut_group, with 1.49%, 1.53% and 0.75% the microbial composition had no significant difference relative abundance, respectively; D-M2 was occupied by between diarrheic and non-diarrheic calves within the Fusobacterium and Bacteroides, with 1.67% and 1.58% same age (Additional file 4: Fig. S3). At the same age, relative abundance, respectively, but the ND-M was calves were in different days of diarrhea, so microbial mainly occupied by unclassified Muribaculaceae, Prevo - changes were also out of synchronization (Fig. S3). So tellaceae_Ga6A1_group, Prevotellaceae_UCG-003, and based on the temporal changes of the fecal scores, the Prevotella_9, with 1.90%, 0.69%, 0.57%, and 0.42% relative fecal samples were divided into four phases: pre-diar- abundance, respectively (Fig. 4C). rhea (when fecal score < 3), diarrhea (fecal scores ≥ 3 consecutively), post-diarrhea (fecal score falling below Trial 2 3), and volatility (fecal score rising to ≥ 3 after 1 to 5 d Diarrhea characteristics of the study cohort below 3) (Fig. 5B). To track the microbial changes from Over the course of the trial 2, all the calves had fecal the pre-diarrhea to post-diarrhea, samples of calves of score ≥ 3 at least 1 d. (Fig. 5A). In the 43 calves, 4, 20, different ages with or without diarrhea were combined 33, 31, 30, 18, 18, 16, 15, 4 and 5 calves had diarrhea on into phases for analysis. Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 9 of 20 Fig. 3 Development stages of the fecal microbiota based on fecal score and the comparison of the overall fecal microbiota from birth to post-weaning (trial 1). Development stages of the 14 calves (A). Principal coordinates analysis (PCoA) plots comparing the fecal microbiotas among the 5 stages (B) and between diarrheic and non-diarrheic calves at stage 2 (C) and stage 4 (D). Age refers to days after birth. The gradual weaning started at d 42 and ended at d 63 Changes in fecal bacterial communities of calves from pre‑ Butyricicoccus, and Lachnoclostridium maintained their to post‑diarrhea relative abundance of the diarrhea phase. Although not In total, 18,706,378 quality-filtered amplicon sequences having changed their relative abundance from the pre- were obtained from 340 fecal samples (the fecal samples diarrhea phase to the diarrhea phase, the genera Fae- of pre-diarrhea, diarrhea, and post-diarrhea phases) with calibacterium, Prevotella_2, and Alloprevotella increased an average of 55,019 ± 10,329 (mean ± SD) sequences per their relative abundance (P < 0.05), while the phylum sample. The sequencing depth coverage reached > 99.97% Epsilonbacteraeota and the genus Campylobacter on average (99.91% to 99.99%). The fecal microbiotas dif - decreased their relative abundance (P < 0.05) in the post- fered among pre-diarrhea, diarrhea, and post-diarrhea diarrhea phase. phases (P < 0.01, Fig. 5C). The lowest number of observed species (on average ASVs and modules associated with the diarrhea phase 117 ASVs per sample) and Shannon index (3.2) were Comparison of the fecal microbiotas between pre-diar- found in the fecal samples of the diarrhea phase (Fig. 6A). rhea and diarrhea phases revealed (based on fold change Both metrics increased (P < 0.05) in the post-diarrhea and analysis using LEfSe or DESeq2) 32 pre-diarrhea- phase. Of the major phyla and genera (each with a rela- associated ASVs and 29 diarrhea phase-associated ASVs tive abundance > 1% in > 60% of the fecal samples at any (Additional file 5: Fig. S4). The pre-diarrhea phase-asso - phase), the phylum Bacteroidota decreased (P < 0.05), ciated ASVs mainly consisted of Bacteroides (12 ASVs), while the phylum Fusobacteria increased (P < 0.05) from Parabacteroides (4 ASVs), Butyricicoccus (3 ASVs), and the pre-diarrhea to diarrhea phase (Fig. 6B), and the gen- Flavonifractor (3 ASVs). The diarrhea phase-associated era Bacteroides, Butyricicoccus, and Lachnoclostridium ASVs mainly consisted of Clostridium_sensu_stricto_1 decreased (P < 0.05), while Clostridium_sensu_stricto_1 (8 ASVs), unclassified Fusobacteriaceae (7 ASVs), and Fusobacterium increased (P < 0.05) during the phase Fusobacterium (6 ASVs), and unclassified Enterobac - transition (Fig. 6C). In the post-diarrhea phase, the phyla teriaceae (3 ASVs). The co-occurrence pattern of the Bacteroidota and Fusobacteria and the genus Fusobac- pre-diarrhea and diarrhea phases had 44 nodes, 78 edges, terium tended to return their relative abundance to the and 4 modules (Fig. 7A). The pre-diarrhea and diar - pre-diarrhea level (P < 0.05), while the genus Clostrid- rhea phase-associated modules (pre-D-M and D-P-M, ium_sensu_stricto_1 lost the relative abundance gained in respectively) were aggregated with six and 16 pre-diar- the diarrhea phase (P > 0.05), and the genera Bacteroides, rhea and diarrhea phase-associated ASVs, respectively, Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 10 of 20 Fig. 4 Co-occurrent pattern of the fecal microbiota at stage 4 in trial 1. Pattern showing the diarrhea status-associated ASVs and modules (A). Mean relative abundance of diarrhea status-associated modules in diarrheic and non-diarrheic calves (B). ASVs relative abundance at genus level in different modules (C). ASV254 and ASV497 in module 2 were not included in plots (B) or (C) and both modules were independent. Module 3 and formed a negative correlation with the D-P-M through module 4 were aggregated with 14 and 5 ASVs, respec- Prevotella_2 (ASV26) and Clostridium_sensu_stricto_1 tively. Both module 3 and module 4 had no diarrhea (ASV48). The ASVs in the D-P-M had a higher (P < 0.05) or pre-diarrhea phase-associated ASVs, but module 4 total relative abundance in the diarrhea phase than in the Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 11 of 20 Fig. 5 Transition phase division of the 43 calves based on fecal score in trial 2. Fecal score of the 43 calves from d 8 to 18 (A). Phase division based on samples fecal score (B). Principal coordinates analysis (PCoA) plot of fecal microbiotas among pre-diarrhea, diarrhea, and post-diarrhea phases (C) pre-diarrhea phase, whereas the opposite was true for both belonging to Prevotella_2), both of which had a the ASVs in pre-D-M (Fig. 7B). The D-P-M was occu - negative relationship with Clostridium_sensu_stricto_1 pied by ASVs assigned to Clostridium_sensu_stricto_1 (ASV48), a diarrhea phase-associated ASV and the key- and Fusobacterium, with a relative abundance of 29.11% stone of D-P-M. Two post-diarrhea phase-associated and 5.42%, respectively. The pre-D-M was only occupied modules (post-D-M1 and post-D-M2) were identified in by ASVs of Bacteroides, with a relative abundance about the co-occurrence network, and post-D-M1 and post- 11.77%. The ASVs of module 3 mainly consisted of All - D-M2 were aggregated with 11 and 5 post-diarrhea prevotella, Bacteroides and Prevotella_9, with 2.94%, phase-associated ASVs, respectively. The composition 1.65% and 0.55% relative abundance, respectively. The of post-D-M1 was similar to that of module 3 in Fig. 7A, ASVs of module 4 were classified to Prevotella_2 and but post-D-M1 had more betweenness and close cen- Lachnospiraceae_UCG-004, with 4.48% and 1.05% rela- trality than module 3 and formed a negative correlation tive abundance, respectively (Fig. 7C). with D-P-M through Alloprevotella (ASV14) and Prevo- When compared the ASVs between diarrhea and tella_9 (ASV43), both of which negatively associated with post-diarrhea phases, 44 post-diarrhea phase-associated Clostridium_sensu_stricto_1 (ASV48) in the D-P-M. The ASVs and 23 diarrhea phase-associated ASVs were iden- ASV composition of module 2 was the same as that of tified based on fold change and analysis using LEfSe or pre-D-M in Fig. 7A, and module 2 was standalone from DESeq2 (Additional file 4: Fig. S4). The post-diarrhea the other four modules (Fig. 7D). Module 5 consisted of phase-associated ASVs mainly consisted of Prevotella_9 six ASVs. Although having no diarrhea and post-diarrhea (13 ASVs), Alloprevotella (8 ASVs), Bacteroides (4 ASVs), phase-associated ASVs, module 5 positively connected and Collinsella (3 ASVs). The diarrhea phase-associated with D-P-M through Tyzzerella_4 (ASV23) and Clostrid- ASVs mainly consisted of Clostridium_sensu_stricto_1 ium_sensu_stricto_1 (ASV48). The D-P-M was occupied (6 ASVs), unclassified Fusobacteriaceae (7 ASVs), and by Fusobacterium, Prevotella_2 and Clostridium_sensu_ unclassified Enterobacteriaceae (4 ASVs). The co-occur - stricto_1 with a relative abundance of 30.94%, 5.34% and rence pattern constructed of the fecal microbiotas at 5.19%, respectively (Fig. 7F). The post-D-M1 was mainly post-diarrhea and diarrhea phases had 57 nodes, 116 occupied by Alloprevotella, Bacteroides and Prevotella_9, edges, and 5 modules (Fig. 7D). The D-P-M was aggre - with a combined relative abundance of 4.80%, 2.32%, and gated with 11 diarrhea phase-associated ASVs and two 0.88%, respectively. The post-D-M2 was only occupied by post-diarrhea phase-associated ASVs (ASV4 and ASV26, Prevotella_9, with a relative abundance of 0.21%. Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 12 of 20 Fig. 6 Fecal microbiota changes from pre-diarrhea, diarrhea, and post-diarrhea phases in trial 2. Two alpha diversity metrics of the fecal microbiota (A), Relative abundance of predominant bacterial phyla (B) and genera (C). Only the phyla and genera each with a relative abundance > 1% in at least 60% of samples in a single phase were shown. Significant differences between two phases were indicated with a * (P < 0.05) Discussion supplementation [40], feeding waste milk containing A better understanding of the fecal microbiota in diar- antibiotic residues [41] or supplemented with sodium rheic and non-diarrheic calves can inform improved humate and glutamine combination [42], or single spe- treatment and prevention strategies. Fecal microbio- cies [43] or multispecies probiotics [44]. These interven - tas between diarrheic and non-diarrheic calves have tions affected the developing process of gut microbiota been compared after interventions, such as trehalose and increased the abundance of Bifidobacterium and Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 13 of 20 Fig. 7 Co-occurrent pattern of the fecal microbiota among three phases in trial 2. Pattern among pre-diarrhea and diarrhea phases (A). Mean relative abundance of phase-associated modules in pre-diarrhea and diarrhea (B). ASVs relative abundance at genus level in pre-diarrhea and diarrhea modules (C). Pattern among diarrhea and post-diarrhea phases (D). Mean relative abundance of phase-associated modules in diarrhea and post-diarrhea (E). ASVs relative abundance at genus level in diarrhea and post-diarrhea modules (F). ASV4 and ASV26 in diarrhea-M were divided into post-D-M1 in plot (E) and (F) Lactobacillus, which might conceal the natural devel- we first examined the dynamic development of the gut opment of resistance to pathogenic colonization in pre- microbiota (represented by fecal microbiota) and diar- weaned calves. Without these types of interventions, rhea occurrence in dairy calves from birth to 15 d post- Kim et al. [20] described the ability of a fecal microbiota weaning with frequently fecal sampling in trial 1, which transplantation (inclusion was collected from the health allowed us to identify two diarrhea peaks. Then we ana - calves of the similar age to diarrheic calves) could ame- lyzed the fecal microbiota and diarrhea occurrence of liorate diarrhea and restore gut microbial composition another group of dairy calves over 11 d corresponding to in pre-weaning calves. Most of these comparative stud- the first diarrhea peak with fecal samples collected daily. ies focused on pre-weaning calves. In the present study, Trial 1 helped test our hypothesis that some gut microbes Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 14 of 20 might have resistance to dysbiotic process with calf diar- diarrhea peak, but it should be given special attention in rhea by dictating the microbial co-occurrence patterns calf industry because of its pathogenic significance. during weaning, while trial 2 allowed us to examine in Bacteroides was the most abundant genus through details as the calves transitioned from pre-diarrhea to the whole period, but its relative abundance increased diarrhea and then to post-diarrhea phases shortly after sharply to 41.96%, 49.66%, and 40.38% at d 5, 7, and 9, birth. respectively, so it could be an age-associated bacterial The development of the gut microbiota appeared to genus for the first two weeks after birth. Its high abun - have three age periods with birth and weaning as the dance was attributed to its stronger saccharolytic ability separatrices [45, 46]. As antibiotics had been used from than Prevotella in the gut of young calves [58, 59]. The d 1 to 5, the drastically decreasing of bacterial richness acetate produced by Bacteroides could be consumed by after birth (Fig. 1A) might be the effects of tylosin tartrate other bacteria, such as Butyricicoccus and Megamonas, to and sulfadimidine. However, in the recent study of neo- produce butyrate and propionate [60], both of which are natal dairy calves, without antibiotics, a similar decrease the main source of energy for intestinal epithelial cells, of bacterial richness was reported by Kim et al. [47] and and butyrate can also inhibit the signaling pathways of Klein-Jöbstl et al. [48]. In the first two weeks after birth, pro-inflammatory cytokines [61], enhance intestinal bar - the underdeveloped gut was colonized primarily by fac- rier function by increasing mucin secretion and enhanc- ultatively anaerobic microbes, especially Escherichia/ ing the tight-junctions [62]. Thus, the colonization by Shigella, to render the intestinal environment suitable for these microbes in the gut of young calves might facili- anaerobic intestinal microbes to colonize, so the decrease tate the establishment of a functional gut. From the third of bacterial richness might be a result of bacterial adapta- week onward, weaning separated the microbiota char- tion. Escherichia/Shigella had a low relative abundance in acteristic of the two periods. Prior to weaning for more the fecal samples at any day after birth to 15 d after wean- than 40 d, the microbiotas remained similar, suggesting ing, but its relative abundance increased up to 45.36% that the gut might have established a stable functional and 31.51% on d 3 and 5, respectively, and then decreased microbiota over that period, with limited recruitment. to around 2% since d 15. The increased colonization by However, with the transition from liquid to total solid Escherichia/Shigella in young calves has been described feed, some of the gut bacteria displayed considerable previously [49], and it might explain the susceptibil- changes, as exemplified by the replacement of Alloprevo - ity of calves to diarrhea caused by E. coli. Klebsiella, tella, Faecalibacterium, and Parabacteroides by Prevo- which belongs to the same family, Enterobacteriaceae, as tellaceae_UCG-003, Rikenellaceae_RC9_gut_group, Escherichia/Shigella, also appeared to be an age-associ- Ruminococcaceae_UCG-005, Ruminococcaceae_UGC- ated bacterial genus for this period. Klebsiella pneumo- 010, and Lachnospiraceae_FCS020_group. These five niae [50] and Klebsiella oxytoca [51] are opportunistic genera are within families that contain species capable pathogenic in humans and are associated with increased of utilizing structural polysaccharides, and this replace- infection mortality rate, particularly in immunocompro- ment might facilitate the degradation of structural poly- mised individuals, neonates, and the elderly. However, saccharides and the stability of the gut microbiota. Future infections in calves caused by Klebsiella have not been research can help identify the species of these five gen - reported frequently. Glantz and Jacks reported that Kleb- era and characterize their functions. Two diarrhea peaks siella spp. occurred naturally in calves, and they might were observed during the three developing periods of the be responsible for some mortality [52]. Komatsu et al. gut microbiota, and less than half of the tested diarrheic reported fatal suppurative meningoencephalitis caused fecal samples were pathogen positive, which suggests that by K. pneumoniae in two calves [53]. Aslan et al. reported microbiota homeostasis may be more important in pre- that K. pneumoniae could be isolated in calves suffering venting diarrhea than directly killing pathogens. Com- from respiratory tract infection, which was not cured paring fecal microbiota transplantation and antibiotic by florfenicol [54]. In the present study, we observed treatment for ameliorating calf diarrhea, Kim et al. [20] changes of relative abundance of Klebsiella, decreas- confirmed that gut microbial manipulation could offer ing from 1.07% and 2.09% on d 1 and 3, respectively, to another therapeutic paradigm, beyond antibiotic based less than 0.1% at d 5. Tylosin tartrate and sulfadimine are therapies. Our data also suggest that prophylaxis/pre- both broad spectrum antibiotics exerting their antimi- ventions with probiotics should be better administered crobial action by inhibiting the bacterial protein synthe- in d 1 to 7 (stage 1, before the first diarrhea peak) and d sis [55], and competing with para-aminobenzoic acid for 18 to 38 (stage 3, the stage after the first peak but before dihydropteroate synthase [56], respectively. The decrease the second diarrhea peak). Supplementation of newborn in Klebsiella might be a result of tylosin tartrate [57]. The calves with Lactobacillus and Bifidobacterium [63] or peak of Klebsiella abundance did not correspond to a Faecalibacterium prausnitzii [64] within the first 7 d of Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 15 of 20 life decreased diarrhea, but no study was found in the associated with diarrhea, both ASV151 and ASV254 literature that had tested probiotic supplementation in (both assigned to Ruminococcaceae) were associated stage 3. To promote the natural development of the gut with non-diarrhea. So Muribaculaceae (ASV28 and microbiotas during the transition, the potential probiot- ASV44), UBA1819 (ASV151), Barnesiella (ASV497), and ics in the gut of calves should be identified. Ruminococcaceae_UCG-005 (ASV254) might be used as Comparison of the fecal microbiota using LEfSe and potential probiotics for this specific commercial farm, the subsequent identification of the correlations between with this specific microbial colonization patterns. The the differential bacterial genera and the core bacterial sequences of these ASVs were included in Table 2, and genera in the gut is a well-trodden path to reveal the these ASVs can be verified in future studies. Fusobac - effects of diarrhea on the microbiota and find potential teriaceae dominated D-M2 (Fig. 4C). Fusobacteriaceae probiotics in neonatal dairy calves for preventing diar- was reported to have high relative abundances in dairy rhea [65]. But many standard correlation analyses may calves suffering from diarrhea, either infected [72] or lead to misleading results because 16S rRNA gene pro- uninfected [65], which indicates that module analysis can filing data are sparse and compositional [37]. SparCC, help identify bacteria associated with diarrhea or oth- which is tailored to the compositional and sparse features erwise. With this module analysis, Muribaculaceae and of genomic survey data and allows for inference of corre- Prevotella were identified as the core microbiota resist - lations between genes or species, has been used to eluci- ing diarrhea in weaning calves. Kim et al. reported that date the networks of interaction among microbial species substituting fermented soybean meal (FSBM) for soybean living in or on the human body [37, 66]. Therefore, to meal (SBM) at 5% level in calf starter reduced the inci- reduce the incidence of false positive results, three levels dence of diarrhea and improved immunocompetence in of evaluation criteria including co-occurrence patterns neonatal calves after microbial infection [73]. The rea - examined using the SparCC algorithm (correlations with son for the positive effects of FSBM on immunocompe - a P < 0.05 and a co-efficient R ≥ 0.5 or ≤ – 0.5 being con- tence was not reported, but in a recent study, Feizi et al. sidered positive and negative correlations, respectively), found that FSBM increased the abundance of Prevotella |log fold change|> 1, LDA score > 2 in LEfSe or adjusted ruminicola in the rumen of dairy calves [74]. Essential P-value < 0.05 in DESeq2 were used in the present study. oils showed similar effects on dairy calves, increasing the During the weaning, some ASVs and modules were asso- Prevotellaceae abundance in the rumen [75] and decreas- ciated with diarrhea, while some were associated with ing the morbidity of neonatal diarrhea among pre-wean- non-diarrhea (Fig. 4 and Fig. S2). ing calves [76]. Furthermore, a recent study reported With the three levels of evaluation criteria, the identi- that sodium humate and glutamine in combination also fied diarrhea-associated ASVs were aggregated in Bac - elevated the abundance of P. ruminicola in the rectum teroides (ASV39, ASV41, ASV145, ASV170, ASV206, while reducing diarrhea incidence among dairy calves and ASV259). These members might be biomarkers of during the weaning period [42]. Tap et al. reported that diarrhea risk. Muribaculaceae (ASV28 and ASV44), Prevotellaceae enterotype was less susceptible to irrita- UBA1819 (ASV151), Barnesiella (ASV497), and Rumino- ble bowel syndrome (IBS) compared with Bacteroidaceae coccaceae_UCG-005 (ASV254) were non-diarrhea-asso- enterotype [77]. Therefore, future research is warranted ciated ASVs in the co-occurrence pattern, and ASV28 to investigate the relationship between calf diarrhea and and ASV497 had a direct inhibitory relationship with Prevotella as a genus and its species. Prevotella may also the members of D-M1 (Fig. 4A). Muribaculaceae, which be explored for its preventative ability to reduce calf was previously assigned as family S24-7 or Homeother- diarrhea. maceae, is a common and abundant family of symbiotic Consistent changes in relative abundance of Bacte- bacteria in the gut and specialized in fermenting com- roides, Butyricicoccus, Faecalibacterium, Alloprevotella, plex carbohydrates [67]. It responded most positively to and Fusobacterium were observed in both trial 1 and acarbose treatment for diabetes [68] and was linked to trial 2 (Fig. 1, 2 and 6). When the fecal microbiota was longevity [69]. Barnesiella belongs to the family Porphy- examined in detail as the calves transitioned from pre- romonadaceae within the phylum Bacteroidota. It was diarrhea to diarrhea and then to post-diarrhea phases found to suppress the growth of intestinal vancomycin- in trial 2, the peak of both Clostridium_sensu_stricto_1 resistant Enterococcus [70]. Members of Ruminococ- and Fusobacterium coincided with the peak of diarrhea. caceae are mostly butyrate-producing bacteria. Weese It has been reported that Clostridium_sensu_stricto_1 et al. suggested that Firmicutes (particularly Lachno- might cause epithelial inflammation in piglets [78] and spiraceae and Ruminococcaceae)/Proteobacteria ratio stunting in infants (defined as height-for-age Z score might be used to potentially predict and prevent colic equal to or lower than – 2, [79]). Therefore, research [71]. Although some members of Ruminococcacea were is needed to further investigate these two genera with Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 16 of 20 Table 2 The sequences of ASVs with potentials being probiotics and biomarker of diarrhea risk ASV ID Taxonomy Sequence ASV4 Prevotella_2TGA GGA ATA TTG GTC AAT GGA CGG GAG TCT GAA CCA GCC AAG TAG CGT GCA GGA TGA CGG CCC TAT GGG TTG TAA ACT GCT TTT ATA GGG GGA TAA AGT GTG CCA CGT GTG GCA TAT TGC AGG TAC CCT ATG AAT AAG GAC CGG CTA ATT CCG TGC CAG CAG CCG CGG TAA TAC GGA AGG TCC GGG CGT TAT CCG GAT TTA TTG GGT TTA AAG GGA GCG TAG GCC GTC TTA TAA GCG TGT TGT GAA ATG TCG GGG CTC AAC CTG GGC ATT GCA GCG CGA ACT GTG AGA CTT GAG TGC GCA GGA AGT AGG CGG AAT TCG TCG TGT AGC GGT GAA ATG CTT AGA TAT GAC GAA GAA CTC CGA TTG CGA AGG CAG CCT GCT GTA GCG CAA CTG ACG CTG AAG CTC GAA AGC GTG GGT ATC GAA CAGG ASV14 AlloprevotellaTGA GGA ATA TTG GTC AAT GGA CGC AAG TCT GAA CCA GCC AAG TAG CGT GCA GGA CGA CGG CCC TCC GGG TTG TAA ACT GCT TTT AGT TGG GAA TAA AGT GCA GCT CGT GAG CTG TTT TGT ATG TAC CAT CAG AAA AAG GAC CGG CTA ATT CCG TGC CAG CAG CCG CGG TAA TAC GGA AGG TCC GGG CGT TAT CCG GAT TTA TTG GGT TTA AAG GGA GCG CAG GCG GAC TCT TAA GTC AGT TGT GAA ATA CGG CGG CTC AAC CGT CGG ACT GCA GTT GAT ACT GGG AGT CTT GAG TGC ACA CAG GGA TGC TGG AAT TCA TGG TGT AGC GGT GAA ATG CTC AGA TAT CAT GAA GAA CTC CGA TCG CGA AGG CAG GTA TCC GGG GTG CAA CTG ACG CTG AGG CTC GAA AGT GCG GGT ATC AAA CAGG ASV26 Prevotella_2TGA GGA ATA TTG GTC AAT GGA CGA GAG TCT GAA CCA GCC AAG TAG CGT GCA GGA CGA CGG CCC TAT GGG TTG TAA ACT GCT TTT ATA GGG GGA TAA AGT GTG CCA CGT GTG GCA TAT TGC AGG TAC CCT ATG AAT AAG GAC CGG CTA ATT CCG TGC CAG CAG CCG CGG TAA TAC GGA AGG TCC GGG CGT TAT CCG GAT TTA TTG GGT TTA AAG GGA GCG TAG GCC GTC TTA TAA GCG TGT TGT GAA ATG TCG GGG CTC AAC CTG GGC ATT GCA GCG CGA ACT GTG AGA CTT GAG TGC GCA GGA AGT AGG CGG AAT TCG TCG TGT AGC GGT GAA ATG CTT AGA TAT GAC GAA GAA CTC CGA TTG CGA AGG CAG CCT GCT GTA GCG CAA CTG ACG CTG AAG CTC GAA AGC GTG GGT ATC GAA CAGG ASV43 Prevotella_9TGA GGA ATA TTG GTC AAT GGA CGA GAG TCT GAA CCA GCC AAG TAG CGT GCA GGA AGA CGG CCC TAT GGG TTG TAA ACT GCT TTT ATA AGG GAA TAA AGT GAG TCT CGT GAG ACT TTT TGC ATG TAC CTT ATG AAT AAG GAC CGG CTA ATT CCG TGC CAG CAG CCG CGG TAA TAC GGA AGG TCC GGG CGT TAT CCG GAT TTA TTG GGT TTA AAG GGA GCG TAG GCC GGA GAT TAA GCG TGT TGT GAA ATG TAG ACG CTC AAC GTC TGC ACT GCA GCG CGA ACT GGT TTC CTT GAG TAC GCA CAA AGT GGG CGG AAT TCG TGG TGT AGC GGT GAA ATG CTT AGA TAT CAC GAA GAA CTC CGA TTG CGA AGG CAG CTC ACT GGA GCG CAA CTG ACG CTG AAG CTC GAA AGT GCG GGT ATC GAA CAGG ASV28 MuribaculaceaeTGA GGA ATA TTG GTC AAT GGG CGC AGG CCT GAA CCA GCC AAG TCG CGT GAG GGA GGA CGG TCC TAC GGA TTG TAA ACC TCT TTT GTC GGG GAG TAA CGT GCG GGA CGC GTC CCG TAT TGA GAG TAC CCG AAG AAA AAG CAT CGG CTA ACT CCG TGC CAG CAG CCG CGG TAA TAC GGA GGA TGC GAG CGT TAT CCG GAT TTA TTG GGT TTA AAG GGT GCG CAG GCG GCG CGC CAA GTC AGC GGT CAA AGT TCC GGG CTC AAC CCG GTG TCG CCG TTG AAA CTG GCG TGC TCG AGT GCG TGC GAG GAA GGC GGA ATG CGT TGT GTA GCG GTG AAA TGC ATA GAT ATG ACG CAG AAC TCC GAT TGC GAA GGC AGC TTT CCA GCG CGC TAC TGA CGC TGA GGC ACG AAA GCG TGG GGA TCG AAC AGG ASV44 MuribaculaceaeTGA GGA ATA TTG GTC AAT GGG CGC AGG CCT GAA CCA GCC AAG TCG CGT GAG GGA AGA CGG TCC TAC GGA TTG TAA ACC TCT TTT GTC GGG GAG TAA CGT GCG GGA CGC GTC CCG TAT TGA GAG TAC CCG AAG AAA AAG CAT CGG CTA ACT CCG TGC CAG CAG CCG CGG TAA TAC GGA GGA TGC GAG CGT TAT CCG GAT TTA TTG GGT TTA AAG GGT GCG CAG GCG GCG CGC CAA GTC AGC GGT CAA AGT TCC GGG CTC AAC CCG GTG TCG CCG TTG AAA CTG GCG TGC TCG AGT GCG TGC GAG GAA GGC GGA ATG CGT TGT GTA GCG GTG AAA TGC ATA GAT ATG ACG CAG AAC TCC GAT TGC GAA GGC AGC TTT CCA GCG CGC TAC TGA CGC TGA GGC ACG AAA GCG TGG GGA TCG AAC AGG ASV151 UBA1819TGG GGA ATA TTG CAC AAT GGG GGA AAC CCT GAT GCA GCG ACG CCG CGT GGA GGA AGA AGG TTT TCG GAT TGT AAA CTC CTG TCT TCG GGG ACG ATA ATG ACG GTA CCC GAG GAG GAA GCC ACG GCT AAC TAC GTG CCA GCA GCC GCG GTA AAA CGT AGG TGG CAA GCG TTG TCC GGA ATT ACT GGG TGT AAA GGG AGC GCA GGC GGG TCG GCA AGT TGG AGG TGA AAG CTG TGG GCT CAA CCC ACA AAC TGC CTT CAA AAC TGC CGA TCT TGA GTG GTG TAG AGG TAG GCG GAA TTC CCG GTG TAG CGG TGG AAT GCG TAG ATA TCG GGA GGA ACA CCA GTG GCG AAG GCG GCC TAC TGG GCA CTA ACT GAC GCT GAG GCT CGA AAG CAT GGG TAG CAA ACA GG ASV497 BarnesiellaTGA GGA ATA TTG GTC AAT GGT CGG CAG ACT GAA CCA GCC AAG TCG CGT GAG GGA AGA CGG CCC TAC GGG TTG TAA ACC TCT TTT GTC GGA GAG TAA AGT ACG CTA CGT GTA GTG TAT TGC AAG TAT CCG AAG AAA AAG CAT CGG CTA ACT CCG TGC CAG CAG CCG CGG TAA TAC GGA GGA TGC AAG CGT TAT CCG GAT TTA TTG GGT TTA AAG GGT GCG TAG GCG GCA CGC CAA GTC AGC GGT GAA ATT TCC GGG CTC AAC CCG GAC TGT GCC GTT GAA ACT GGC GAG CTA GAG TAC ACA AGA GGC AGG CGG AAT GCG TGG TGT AGC GGT GAA ATG CAT AGA TAT CAC GCA GAA CCC CGA TTG CGA AGG CAG CCT GCT AGG GTG AAA CAG ACG CTG AGG CAC GAA AGC GTG GGT ATC GAA CAGG ASV254 Ruminococcaceae UCG-005TGG GGA ATA TTG GGC AAT GGG GGA AAC CCT GAC CCA GCA ACG CCG CGT GAA GGA AGA AGG CCC TCG GGT TGT AAA CTT CTT TTA CCA GGG ACG AAG GAC GTG ACG GTA CCT GGA GAA AAA GCA ACG GCT AAC TAC GTG CCA GCA GCC GCG GTA ATA CGT AGG TTG CAA GCG TTG TCC GGA TTT ACT GGG TGT AAA GGG CGT GTA GGC GGA GCT GCA AGT CAG ATG TGA AAT CCC GGG GCT CAA CCC CGG AAC TGC ATT TGA AAC TGT AGC CCT TGA GTA TCG GAG AGG CAA GCG GAA TTC CTA GTG TAG CGG TGA AAT GCG TAG ATA TTA GGA GGA ACA CCA GTG GCG AAG GCG GCT TGC TGG ACG ACA ACT GAC GCT GAG GCG CGA AAG CGT GGG GAG CAA ACAGG ASV48 Clostridium sensu_stricto_1TGG GGA ATA TTG CAC AAT GGG GGA AAC CCT GAT GCA GCA ACG CCG CGT GAG TGA TGA CGG CCT TCG GGT TGT AAA GCT CTG TCT TTG GGG ACG ATA ATG ACG GTA CCC AAG GAG GAA GCC ACG GCT AAC TAC GTG CCA GCA GCC GCG GTA ATA CGT AGG TGG CAA GCG TTG TCC GGA TTT ACT GGG CGT AAA GGG AGC GTA GGC GGA TTT TTA AGT GGG ATG TGA AAT ACC CGG GCT CAA CCT GGG TGC TGC ATT CCA AAC TGG AAA TCT AGA GTG CAG GAG GGG AAA GTG GAA TTC CTA GTG TAG CGG TGA AAT GCG TAG AGA TTA GGA AGA ACA CCA GTG GCG AAG GCG ACT TTC TGG ACT GTA ACT GAC GCT GAG GCT CGA AAG CGT GGG GAG CAA ACA GG Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 17 of 20 Table 2 (continued) ASV ID Taxonomy Sequence ASV39 BacteroidesTGA GGA ATA TTG GTC AAT GGA CGA GAG TCT GAA CCA GCC AAG TAG CGT GAA GGA TGA AGG TCC TAC GGA TTG TAA ACT TCT TTT ATA AGG GAA TAA ACC CTC CCA CGT GTG GGA GCT TGT ATG TAC CTT ATG AAT AAG CAT CGG CTA ACT CCG TGC CAG CAG CCG CGG TAA TAC GGA GGA TGC GAG CGT TAT CCG GAT TTA TTG GGT TTA AAG GGA GCG CAG ACG GGT CGT TAA GTC AGC TGT GAA AGT TTG GGG CTC AAC CTT AAA ATT GCA GTT GAT ACT GGC GTC CTT GAG TGC GGT TGA GGT GTG CGG AAT TCG TGG TGT AGC GGT GAA ATG CTT AGA TAT CAC GAA GAA CTC CGA TTG CGA AGG CAG CAC ACT AAG CCG TAA CTG ACG TTC ATG CTC GAA AGT GTG GGT ATC AAA CAGG ASV41 BacteroidesTGA GGA ATA TTG GTC AAT GGG CGA GAG CCT GAA CCA GCC AAG TAG CGT GAA GGA TGA AGG TCC TAC GGA TTG TAA ACT TCT TTT ATA AGG GAA TAA AAC GCT CCA CGT GTG GAG CCT TGT ATG TAC CTT ATG AAT AAG CAT CGG CTA ACT CCG TGC CAG CAG CCG CGG TAA TAC GGA GGA TGC GAG CGT TAT CCG GAT TTA TTG GGT TTA AAG GGA GCG CAG ACG GGA TGT TAA GTC AGC TGT GAA AGT TTG CGG CTC AAC CGT AAA ATT GCA GTT GAT ACT GGC GTT CTT GAG TGC AGT TGA GGT GTG CGG AAT TCG TGG TGT AGC GGT GAA ATG CTT AGA TAT CAC GAA GAA CTC CGA TTG CGA AGG CAG CTC ACT AAA CTG TAA CTG ACG TTC ATG CTC GAA AGT GTG GGT ATC AAA CAGG ASV145 BacteroidesTGA GGA ATA TTG GTC AAT GGG CGA GAG CCT GAA CCA GCC AAG TAG CGT GAA GGA TGA AGG TCC TAT GGA TTG TAA ACT TCT TTT ATA AGG GAA TAA AAC GCT CCA CGT GTG GAG CCT TGT ATG TAC CTT ATG AAT AAG CAT CGG CTA ACT CCG TGC CAG CAG CCG CGG TAA TAC GGA GGA TGC GAG CGT TAT CCG GAT TTA TTG GGT TTA AAG GGA GCG CAG ACG GGA TGT TAA GTC AGC TGT GAA AGT TTG CGG CTC AAC CGT AAA ATT GCA GTT GAT ACT GGC GTT CTT GAG TGC AGT TGA GGT GTG CGG AAT TCG TGG TGT AGC GGT GAA ATG CTT AGA TAT CAC GAA GAA CTC CGA TTG CGA AGG CAG CTC ACT AAA CTG TAA CTG ACG TTC ATG CTC GAA AGT GTG GGT ATC AAA CAGG ASV170 BacteroidesTGA GGA ATA TTG GTC AAT GGT CGG AAG ACT GAA CCA GCC AAG TAG CGT GAA GGA TGA AGG TTC TAT GGA TTG TAA ACT TCT TTT ATA CGG GAA TAA AAC CAC CTA CGT GTA GGT GCT TGT ATG TAC CGT ATG AAT AAG CAT CGG CTA ACT CCG TGC CAG CAG CCG CGG TAA TAC GGA GGA TGC GAG CGT TAT CCG GAT TTA TTG GGT TTA AAG GGA GCG TAG ACG GGG GAT TAA GTC AGT TGT GAA AGG CTG CGG CTC AAC CGC AGC ACT GCA GTT GAT ACT GGT TTC CTT GAG TGC GGT TGA GGT GTA TGG AAT TCG TGG TGT AGC GGT GAA ATG CTT AGA TAT CAC GAA GAA CTC CGA TTG CGA AGG CAG TAC ACT AAG CCG TAA CTG ACG TTG AGG CTC GAA AGT GTG GGT ATC AAA CAGG ASV206 BacteroidesTGA GGA ATA TTG GTC AAT GGC CGG AAG GCT GAA CCA GCC AAG TAG CGT GAA GGA TGA AGG TTC TAT GGA TTG TAA ACT TCT TTT ATA CGG GAA TAA AAC CAC CTA CGT GTA GGT GCT TGT ATG TAC CGT ATG AAT AAG CAT CGG CTA ACT CCG TGC CAG CAG CCG CGG TAA TAC GGA GGA TGC GAG CGT TAT CCG GAT TTA TTG GGT TTA AAG GGA GCG TAG ACG GGA TGT TAA GTC AGT TGT GAA AGG CTG CGG CTC AAC CGC AGC ACT GCA GTT GAT ACT GGC GTC CTT GAG TGC GGT TGA GGT ATG TGG AAT TCG TGG TGT AGC GGT GAA ATG CTT AGA TAT CAC GAA GAA CTC CGA TTG CGA AGG CAG CAT ACT AAG CCG CTA CTG ACG TTG AGG CTC GAA AGT GTG GGT ATC AAA CAGG ASV259 BacteroidesTGA GGA ATA TTG GTC AAT GGA CGA GAG TCT GAA CCA GCC AAG TAG CGT GAA GGA TGA AGG TCC TAC GGA TTG TAA ACT TCT TTT ATA AGG GAA TAA AAC CTC CCA CGT GTG GGA GCT TGT ATG TAC CTT ATG AAT AAG CAT CGG CTA ACT CCG TGC CAG CAG CCG CGG TAA TAC GGA GGA TGC GAG CGT TAT CCG GAT TTA TTG GGT TTA AAG GGA GCG CAG ACG GGA TGT TAA GTC AGC TGT GAA AGT TTG CGG CTC AAC CGT AAA ATT GCA GTT GAT ACT GGC GTT CTT GAG TGC AGT TGA GGT GTG CGG AAT TCG TGG TGT AGC GGT GAA ATG CTT AGA TAT CAC GAA GAA CTC CGA TTG CGA AGG CAG CTC ACT AAA CTG TAA CTG ACG TTC ATG CTC GAA AGT GTG GGT ATC AAA CAGG respect to their role in calf diarrhea. Our co-occurrence These potential probiotics may be supplemented in the analysis (Fig. 7) showed that the post-D-M1 might first week after birth to prevent diarrhea, and fiber diets be a driver of diarrhea recovery because of the close [80] or FSBM [71] may improve their efficacy. Clostrid - interaction between its constituent members and the ium_sensu_stricto_1 (ASV48, Fig. 7, Table 2), was nega- inhibitory relationship with D-P-M. Prevotella_2 and tively related with Prevotella_2 (ASV4 and ASV26), Alloprevotella increased their relative abundance in the Alloprevotella (ASV14) and Prevotella_9 (ASV43). All post-diarrhea phase (Fig. 6C), and they dominated the of the four ASVs established the negative relationship post-D-M1 (Fig. 7F), which supports the importance between the post-D-M1 and D-P-M. Thus, Clostrid - of Prevotellaceae in resisting calf diarrhea. In particu- ium_sensu_stricto_1 (ASV48) might be a biomarker of lar, Prevotella_2 (ASV4 and ASV26), Alloprevotella diarrhea risk in the early stage. (ASV14) and Prevotella_9 (ASV43), their sequences were included in Table 2, might be potential probiot- Conclusions ics for preventing diarrhea in early stage. It should be In conclusion, microbial successions of the gut microbiome noted that although most of the constituent members in dairy calves were rapid, and daily sampling is needed to of post-D-M1 were detected before diarrhea (module 3 capture the rapid dynamic gut microbial successions. Pro- and 4 in Fig. 7A), their betweenness and close centrality moting indigenous Prevotella and Muribaculaceae might increased in post-D-M1. This suggests that the interac - be a new strategy to reduce the incidence of diarrhea in tions among different bacteria might play an important neonatal calves and help calves to go through the wean- role in maintaining intestinal homeostasis in the gut. ing transition smoothly. Prevotella_2 (ASV4 and ASV26), Prevotella_9 (ASV43), Alloprevotella (AVS14), unclassified Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 18 of 20 Declarations Muribaculaceae (ASV28 and ASV44), UBA1819 (ASV151), Ruminococcaceae_UCG-005 (ASV254), and Barnesiella Ethics approval and consent to participate (ASV497) might be used as probiotics to reduce or prevent All experimental protocols used in the current study were approved by the Animal Care and Use Committee of Zhejiang University (Protocol number: calf diarrhea; Clostridium_sensu_stricto_1 (ASV48) might ZJU-20262), and all experimental procedures were performed following the be a useful biomarker of diarrhea risk in this large-scale approved protocols. dairy farm locating in subtropical monsoon climate zone Consent for publication with automated milk feeder system. Not applicable. Competing interests Abbreviations The authors declare that they have no competing interests. + + E. coli K99 : Escherichia coli K99 ; BRV: Bovine rotavirus; BCoV: Bovine coro- navirus; ASV: Amplicon sequence variant; Faith’s PD: Faith’s phylogenetic Author details diversity; ANOSIM: Aanalysis of similarity; PCoA: Principal coordinates analysis; Institute of Dairy Science, College of Animal Sciences, Zhejiang University, PERMANOVA: Permutational multivariate analysis of variance; LEfSe: Linear Hangzhou, China. MoE Key Laboratory of Molecular Animal Nutrition, Zheji- discriminant analysis Eec ff t Size; D-M1: Diarrhea module 1; D-M2: Diarrhea ang University, Hangzhou, China. Department of Animal Sciences, The Ohio module 2; ND-M: Non-diarrhea module; pre-D-M: Pre-diarrhea phase- State University, Columbus, OH, USA. associated module; D-P-M: Diarrhea phase-associated module; post-D-M1/2: Post-diarrhea phase-associated modules. Received: 15 March 2022 Accepted: 13 July 2022 Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1186/ s40104- 022- 00758-4. References 1. Cho YI, Yoon KJ. An overview of calf diarrhea - infectious etiology, diagno- Additional file 1: Fig. S1. A schematic showing the experimental design, sis, and intervention. J Vet Sci. 2014;15(1):1–17. https:// doi. org/ 10. 4142/ milk feeding, and fecal sample collection of the two trials. jvs. 2014. 15.1.1. 2. Meganck V, Hoflack G, Opsomer G. Advances in prevention and therapy Additional file 2: Fig. S2. Heatmap of the ASVs associated with diarrheic of neonatal dairy calf diarrhoea: a systematical review with emphasis on status in stage 4 of trial 1. The ASVs were identified based on fold change colostrum management and fluid therapy. Acta Vet Scand. 2014;56(1):75. and analysis using LEfSe or DESeq2. https:// doi. org/ 10. 1186/ s13028- 014- 0075-x. Additional file 3: Table S1. The ASVs in co-occurrence pattern of trial 1 3. Castro JJ, Gomez A, White BA, Mangian HJ, Loften JR, Drackley JK. stage 4. Changes in the intestinal bacterial community, short-chain fatty acid Additional file 4: Fig. S3. Principal coordinates analysis (PCoA) plot of profile, and intestinal development of preweaned Holstein calves. 1. fecal microbiotas among ages in trial 2. The fecal microbiotas differences Eec ff ts of prebiotic supplementation depend on site and age. J Dairy Sci. (P values) between diarrheic and non-diarrheic calves within the same 2016;99(12):9682–702. https:// doi. org/ 10. 3168/ jds. 2016- 11006. age were compared. 4. Windeyer MC, Leslie KE, Godden SM, Hodgins DC, Lissemore KD, LeBlanc SJ. Factors associated with morbidity, mortality, and growth of dairy Additional file 5: Fig. S4. Heatmap of the ASVs that identified to be diar - heifer calves up to 3 months of age. Prev Vet Med. 2014;113(2):231–40. rheic status transition-associated in trial 2. The ASVs were identified based https:// doi. org/ 10. 1016/j. preve tmed. 2013. 10. 019. on fold change and analysis using LEfSe or DESeq2. 5. Aghakeshmiri F, Azizzadeh M, Farzaneh N, Gorjidooz M. Eec ff ts of neona- tal diarrhea and other conditions on subsequent productive and repro- ductive performance of heifer calves. Vet Res Commun. 2017;41(2):107– Acknowledgements 12. https:// doi. org/ 10. 1007/ s11259- 017- 9678-9. We thank the College Experimental Teaching Center, College of Animal 6. Heinrichs AJ, Heinrichs BS, Harel O, Rogers GW, Place NT. A prospective Sciences, Zhejiang University for experimental equipment surport study of calf factors affecting age, body size, and body condition score and assistance. We also thank our colleagues Guangyu Zhang, Lei Cao, at first calving of holstein dairy heifers. J Dairy Sci. 2005;88(8):2828–35. Weibing Shi, Shaobo Yu, Zhibo Liu and Peng Wu at the Institute of https:// doi. org/ 10. 3168/ jds. S0022- 0302(05) 72963-5. Dair y Science, College of Animal Sciences, Zhejiang University for their 7. Kargar S, Roshan M, Ghoreishi SM, Akhlaghi A, Kanani M, Abedi Shams- assistance with sampling. Abadi AR, et al. Extended colostrum feeding for 2 weeks improves growth performance and reduces the susceptibility to diarrhea and pneumonia Authors’ contributions in neonatal Holstein dairy calves. J Dairy Sci. 2020;103(9):8130–42. https:// JW and HC designed the study and drafted the manuscript. HC, YL and KH doi. org/ 10. 3168/ jds. 2020- 18355. conducted the animal trials and collected the fecal samples. HC performed 8. Khan MA, Lee HJ, Lee WS, Kim HS, Kim SB, Ki KS, et al. Pre- and postwean- the DNA extraction and pathogen detection. HC and BY performed the data ing performance of holstein female calves fed milk through step-down analyses and visualization. YZ provided experiment assistance. ZY provided and conventional methods. J Dairy Sci. 2007;90(2):876–85. https:// doi. critical guidance in the interpretation of results and revised the manuscript. org/ 10. 3168/ jds. S0022- 0302(07) 71571-0. JW supervised the study and revised the manuscript. The authors read and 9. Klein-Jöbstl D, Iwersen M, Drillich M. Farm characteristics and calf approved the final manuscript. management practices on dairy farms with and without diarrhea: a case-control study to investigate risk factors for calf diarrhea. J Dairy Sci. Funding 2014;97(8):5110–9. https:// doi. org/ 10. 3168/ jds. 2013- 7695. This work was supported by the National Key Research and Development 10. Caballero S, Kim S, Carter RA, Leiner IM, Sušac B, Miller L, et al. Cooperat- Program of China (2017YFD0500502). ing commensals restore colonization resistance to vancomycin-resistant Enterococcus faecium. Cell Host Microbe. 2017;21(5):592–602. https:// doi. Availability of data and material org/ 10. 1016/j. chom. 2017. 04. 002. The raw sequencing data generated in this study are publicly available in NCBI 11. Jacobson A, Lam L, Rajendram M, Tamburini F, Honeycutt J, Pham T, et al. Sequence Read Archive (http:// www. ncbi. nim. nih. gov/ sra) under the acces- A gut commensal-produced metabolite mediates colonization resistance sion number PRJNA716761. Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 19 of 20 to Salmonella infection. Cell Host Microbe. 2018;24(2):296–307. https:// 30. Pinheiro J, Bates D, DebRoy S, Sarkar D, Heisterkamp S, Van Willigen B, doi. org/ 10. 1016/j. chom. 2018. 07. 002. et al. nlme: Linear and nonlinear mixed effects models. R package version. 12. Kim YG, Sakamoto K, Seo SU, Pickard JM, Gillilland MG, Pudlo NA, et al. 2022;3.1–155. Available from: https:// cran.r- proje ct. org/ packa ge= nlme. Neonatal acquisition of Clostridia species protects against colonization 31. Hothorn T, Bretz F, Westfall P. Simultaneous inference in general paramet- by bacterial pathogens. Science. 2017;356(6335):315–9. https:// doi. org/ ric models. Biom J. 2008;50(3):346–63. https:// doi. org/ 10. 1002/ bimj. 20081 10. 1126/ scien ce. aag20 29. 0425. 13. Wu H, Ma Y, Peng X, Qiu W, Kong L, Ren B, et al. Antibiotic-induced 32. Oksanen J, Blanchet FG, Friendly M, Kindt R. vegan: Community ecology dysbiosis of the rat oral and gut microbiota and resistance to Salmonella. package 2019. Available from: https:// cran.r- proje ct. org/ packa ge= vegan. Arch Oral Biol. 2020;114:104730. https:// doi. org/ 10. 1016/j. archo ralbio. 33. Breiman L. Random Forests. Mach Learn. 2001;45(1):5–32. https:// doi. org/ 2020. 104730.10. 1023/A: 10109 33404 324. 14. Jang JY, Kim S, Kwon MS, Lee J, Yu DH, Song RH, et al. Rotavirus-mediated 34. Liaw A, Wiener M. Classification and regression by randomForest. R News. alteration of gut microbiota and its correlation with physiological char- 2002;2:18–22. Available from: https:// cran.r- proje ct. org/ doc/ Rnews/ acteristics in neonatal calves. J Microbiol. 2019;57(2):113–21. https:// doi. Rnews_ 2002-3. pdf org/ 10. 1007/ s12275- 019- 8549-1. 35. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. 15. Haag LM, Fischer A, Otto B, Plickert R, Kühl AA, Göbel UB, et al. Intestinal Metagenomic biomarker discovery and explanation. Genome Biol. microbiota shifts towards elevated commensal Escherichia coli loads abro- 2011;12(6):R60. https:// doi. org/ 10. 1186/ gb- 2011- 12-6- r60. gate colonization resistance against Campylobacter jejuni in mice. PLoS 36. Love MI, Huber W, Anders S. Moderated estimation of fold change and ONE. 2012;7(5): e35988. https:// doi. org/ 10. 1371/ journ al. pone. 00359 88. dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. 16. Kim YH, Nagata R, Ohtani N, Ichijo T, Ikuta K, Sato S. Eec ff ts of dietary https:// doi. org/ 10. 1186/ s13059- 014- 0550-8. forage and calf starter diet on ruminal pH and bacteria in Holstein calves 37. Friedman J, Alm EJ. Inferring correlation networks from genomic survey during weaning transition. Front Microbiol. 2016;7:1575. https:// doi. org/ data. PLoS Comput Biol. 2012;8(9):e1002687. https:// doi. org/ 10. 1371/ 10. 3389/ fmicb. 2016. 01575.journ al. pcbi. 10026 87. 17. Meale SJ, Chaucheyras-Durand F, Berends H, Guan LL, Steele MA. From 38. Csárdi G, Nepusz T. The igraph software package for complex network pre- to postweaning: Transformation of the young calf ’s gastrointesti- research. Int J Complex Syst. 2006;1695(5):1–9. Available from: https:// nal tract. J Dairy Sci. 2017;100(7):5984–95. https:// doi. org/ 10. 3168/ jds. igraph. org. 2016- 12474. 39. Pons P, Latapy M. Computing communities in large networks using random 18. Karasova D, Crhanova M, Babak V, Jerabek M, Brzobohaty L, Matesova Z, walks. In: Yolum P, Güngör T, Gürgen F, Özturan C, editors. Computer and et al. Development of piglet gut microbiota at the time of weaning influ- Information Sciences - ISCIS 2005. Lecture Notes in Computer Science, vol ences development of postweaning diarrhea - A field study. Res Vet Sci. 3733. Berlin Heidelberg: Springer; 2005. p. 284–93. https:// doi. org/ 10. 1007/ 2021;135:59–65. https:// doi. org/ 10. 1016/j. rvsc. 2020. 12. 022.11569 596_ 31. 19. Ma T, Villot C, Renaud D, Skidmore A, Chevaux E, Steele M, et al. Linking 40. Miura H, Mukai K, Sudo K, Haga S, Suzuki Y, Kobayashi Y, et al. Eec ff t of perturbations to temporal changes in diversity, stability, and composi- trehalose supplementation in milk replacer on the incidence of diarrhea tions of neonatal calf gut microbiota: prediction of diarrhea. ISME J. and fecal microbiota in preweaned calves. J Anim Sci. 2021;99(1):skab012. 2020;14(9):2223–35. https:// doi. org/ 10. 1038/ s41396- 020- 0678-3.https:// doi. org/ 10. 1093/ jas/ skab0 12. 20. Kim HS, Whon TW, Sung H, Jeong YS, Jung ES, Shin NR, et al. Longitudinal 41. Penati M, Sala G, Biscarini F, Boccardo A, Bronzo V, Castiglioni B, et al. evaluation of fecal microbiota transplantation for ameliorating calf diar- Feeding pre-weaned calves with waste milk containing antibiotic rhea and improving growth performance. Nat Commun. 2021;12(1):161. residues is related to a higher incidence of diarrhea and alterations in the https:// doi. org/ 10. 1038/ s41467- 020- 20389-5. fecal microbiota. Front Vet Sci. 2021;8:650150. https:// doi. org/ 10. 3389/ 21. Lesmeister KE, Heinrichs AJ. Eec ff ts of corn processing on growth fvets. 2021. 650150. characteristics, rumen development, and rumen parameters in neonatal 42. Wang D, Du Y, Wang S, You Z, Liu Y. Eec ff ts of sodium humate and dairy calves. J Dairy Sci. 2004;87(10):3439–50. https:// doi. org/ 10. 3168/ jds. glutamine combined supplementation on growth performance, diarrhea S0022- 0302(04) 73479-7. incidence, blood parameters, and intestinal microflora of weaned calves. 22. Zoetendal EG, Heilig HG, Klaassens ES, Booijink CC, Kleerebezem M, Smidt Anim Sci J. 2021;92(1): e13584. https:// doi. org/ 10. 1111/ asj. 13584. H, et al. Isolation of DNA from bacterial samples of the human gastroin- 43. Fernández-Ciganda S, Fraga M, Zunino P. Probiotic Lactobacilli admin- testinal tract. Nat Protoc. 2006;1(2):870–3. https:// doi. org/ 10. 1038/ nprot. istration induces changes in the fecal microbiota of preweaned dairy 2006. 142. calves. Probiotics Antimicrob Proteins. 2021. https:// doi. org/ 10. 1007/ 23. Yu Y, Lee C, Kim J, Hwang S. Group-specific primer and probe sets to s12602- 021- 09834-z. detect methanogenic communities using quantitative real-time poly- 44. Wu Y, Wang L, Luo R, Chen H, Nie C, Niu J, et al. Eec ff t of a multispecies merase chain reaction. Biotechnol Bioeng. 2005;89(6):670–9. https:// doi. probiotic mixture on the growth and incidence of diarrhea, immune org/ 10. 1002/ bit. 20347. function, and fecal microbiota of pre-weaning dairy calves. Front Micro- 24. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. biol. 2021;12:681014. https:// doi. org/ 10. 3389/ fmicb. 2021. 681014. DADA2: High-resolution sample inference from Illumina amplicon data. 45. Meale SJ, Li SC, Azevedo P, Derakhshani H, DeVries TJ, Plaizier JC, et al. Nat Methods. 2016;13(7):581–3. https:// doi. org/ 10. 1038/ nmeth. 3869. Weaning age influences the severity of gastrointestinal microbiome 25 Strube ML. RibDif: can individual species be differentiated by 16S shifts in dairy calves. Sci Rep. 2017;7(1):198. https:// doi. org/ 10. 1038/ sequencing? Bioinform adv. 2021;1(1):vbab020. https:// doi. org/ 10. 1093/ s41598- 017- 00223-7. bioadv/ vbab0 20. 46. Amin N, Schwarzkopf S, Kinoshita A, Tröscher-Mußotter J, Dänicke S, Cam- 26. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA arinha-Silva A, et al. Evolution of rumen and oral microbiota in calves is ribosomal RNA gene database project: improved data processing and influenced by age and time of weaning. Anim Microbiome. 2021;3(1):31. web-based tools. Nucleic Acids Res. 2013;41:D590–6. https:// doi. org/ 10. https:// doi. org/ 10. 1186/ s42523- 021- 00095-3. 1093/ nar/ gks12 19. 47. Kim ET, Lee SJ, Kim TY, Lee HG, Atikur RM, Gu BH, et al. Dynamic changes 27. Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid in fecal microbial communities of neonatal dairy calves by aging and assignment of rRNA sequences into the new bacterial taxonomy. Appl diarrhea. Animals (Basel). 2021;11(4):1113. https:// doi. org/ 10. 3390/ ani11 Environ Microbiol. 2007;73(16):5261–7. https:// doi. org/ 10. 1128/ AEM. 041113. 00062- 07. 48. Klein-Jöbstl D, Quijada NM, Dzieciol M, Feldbacher B, Wagner M, Drillich 28. McMurdie PJ, Holmes S. phyloseq: an R package for reproducible M, et al. Microbiota of newborn calves and their mothers reveals possible interactive analysis and graphics of microbiome census data. PLoS ONE. transfer routes for newborn calves’ gastrointestinal microbiota. PLoS ONE. 2013;8(4):e61217. https:// doi. org/ 10. 1371/ journ al. pone. 00612 17. 2019;14(8):e0220554. https:// doi. org/ 10. 1371/ journ al. pone. 02205 54. 29 Nam HM, Srinivasan V, Gillespie BE, Murinda SE, Oliver SP. Application 49. Mayer M, Abenthum A, Matthes JM, Kleeberger D, Ege MJ, Holzel C, of SYBR green real-time PCR assay for specific detection of Salmo - et al. Development and genetic influence of the rectal bacterial flora of nella spp. in dairy farm environmental samples. Int J Food Microbiol. newborn calves. Vet Microbiol. 2012;161(1–2):179–85. https:// doi. org/ 10. 2005;102(2):161–71. https:// doi. org/ 10. 1016/j. ijfoo dmicro. 2004. 12. 020.1016/j. vetmic. 2012. 07. 023. Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 20 of 20 50 Podschun R, Ullmann U. Klebsiella spp. as nosocomial pathogens: epi- 69. Sibai M, Altuntaş E, Yıldırım B, Öztürk G, Yıldırım S, Demircan T. Micro- demiology, taxonomy, typing methods, and pathogenicity factors. Clin biome and longevity: High abundance of longevity-linked muribacu- Microbiol Rev. 1998;11(4):589–603. https:// doi. org/ 10. 1128/ CMR. 11.4. 589. laceae in the gut of the long-living rodent Spalax leucodon. OMICS. 51. Broberg CA, Palacios M, Miller VL. Klebsiella: a long way to go towards 2020;24(10):592–601. https:// doi. org/ 10. 1089/ omi. 2020. 0116. understanding this enigmatic jet-setter. F1000Prime Rep. 2014;6:64. 70. Ubeda C, Bucci V, Caballero S, Djukovic A, Toussaint NC, Equinda https:// doi. org/ 10. 12703/ P6- 64. M, et al. Intestinal microbiota containing Barnesiella species cures 52. Glantz PJ, Jacks TM. A bacteriological and serological study of vancomycin-resistant Enterococcus faecium colonization. Infect Immun. experimental escherichia coli infection of calves. Can J Comp Med. 2013;81(3):965–73. https:// doi. org/ 10. 1128/ IAI. 01197- 12. 1969;33(2):128–33. 71. Weese JS, Holcombe SJ, Embertson RM, Kurtz KA, Roessner HA, Jalali M, 53. Komatsu T, Yoshida E, Shigenaga A, Yasuie N, Uchiyama S, Takamura Y, et al. Changes in the faecal microbiota of mares precede the develop- et al. Fatal suppurative meningoencephalitis caused by Klebsiella pneu- ment of post partum colic. Equine Vet J. 2015;47(6):641–9. https:// doi. moniae in two calves. J Vet Med Sci. 2021;83(7):1113–9. https:// doi. org/ 10. org/ 10. 1111/ evj. 12361. 1292/ jvms. 21- 0166. 72. Ichikawa-Seki M, Motooka D, Kinami A, Murakoshi F, Takahashi Y, Aita 54. Aslan V, Maden M, Erganis O, Birdane FM, Corlu M. Clinical efficacy of J, et al. Specific increase of Fusobacterium in the faecal microbiota florfenicol in the treatment of calf respiratory tract infections. Vet Q. of neonatal calves infected with Cryptosporidium parvum. Sci Rep. 2002;24(1):35–9. https:// doi. org/ 10. 1080/ 01652 176. 2002. 96951 22. 2019;9(1):12517. https:// doi. org/ 10. 1038/ s41598- 019- 48969-6. 55. Brisson-Noël A, Trieu-Cuot P, Courvalin P. Mechanism of action of spiramy- 73. Kim MH, Yun CH, Kim HS, Kim JH, Kang SJ, Lee CH, et al. Eec ff ts of cin and other macrolides. J Antimicrob Chemother. 1988;22(Suppl. fermented soybean meal on growth performance, diarrheal incidence B):13–23. https:// doi. org/ 10. 1093/ jac/ 22. suppl ement_b. 13. and immune-response of neonatal calves. Anim Sci J. 2010;81(4):475–81. 56. Roland S, Ferone R, Harvey RJ, Styles VL, Morrison RW. The characteristics https:// doi. org/ 10. 1111/j. 1740- 0929. 2010. 00760.x. and significance of sulfonamides as substrates for Escherichia coli dihy- 74. Feizi LK, Zad SS, Jalali SAH, Rafiee H, Jazi MB, Sadeghi K, et al. Fermented dropteroate synthase. J Biol Chem. 1979;254(20):10337–45. https:// doi. soybean meal affects the ruminal fermentation and the abundance of org/ 10. 1016/ s0021- 9258(19) 86714-5. selected bacterial species in Holstein calves: a multilevel analysis. Sci Rep. 57. Akhter S, Ansari MS, Andrabi S, Ullah N, Qayyum M. Eec ff t of antibiot - 2020;10(1):12062. https:// doi. org/ 10. 1038/ s41598- 020- 68778-6. ics in extender on bacterial and spermatozoal quality of cooled buffalo 75. Poudel P, Froehlich K, Casper DP, St-Pierre B. Feeding essential oils to (Bubalus bubalis) bull semen. Reprod Domest Anim. 2008;43(3):272–8. neonatal Holstein dairy calves results in increased ruminal Prevotel- https:// doi. org/ 10. 1111/j. 1439- 0531. 2007. 00890.x. laceae abundance and propionate concentrations. Microorganisms. 58. Shah HN, Collins MD. Proposal to restrict the genus Bacteroides (Castellani 2019;7(5):120. https:// doi. org/ 10. 3390/ micro organ isms7 050120. and Chalmers) to Bacteroides fragilis and closely related species. Int J Syst 76. Campolina JP, Coelho SG, Belli AL, Machado FS, Pereira LGR, Tomich TR, Bacteriol. 1989;39(1):85–7. https:// doi. org/ 10. 1099/ 00207 713- 39-1- 85. et al. Eec ff ts of a blend of essential oils in milk replacer on performance, 59. Willems A, Collins MD. 16S rRNA gene similarities indicate that Hallella rumen fermentation, blood parameters, and health scores of dairy heifers. seregens (Moore and Moore) and Mitsuokella dentalis (Haapsalo et al.) are PLoS ONE. 2021;16(3):e0231068. https:// doi. org/ 10. 1371/ journ al. pone. genealogically highly related and are members of the genus Prevo-02310 68. tella: emended description of the genus Prevotella (Shah and Collins) 77. Tap J, Derrien M, Tornblom H, Brazeilles R, Cools-Portier S, Dore J, et al. and description of Prevotella dentalis comb. nov. Int J Syst Bacteriol. Identification of an intestinal microbiota signature associated with sever - 1995;45(4):832–6. https:// doi. org/ 10. 1099/ 00207 713- 45-4- 832. ity of Irritable Bowel Syndrome. Gastroenterology. 2017;152(1):111-23.e8. 60. Trachsel J, Humphrey S, Allen HK. Butyricicoccus porcorum sp. nov., a https:// doi. org/ 10. 1053/j. gastro. 2016. 09. 049. butyrate-producing bacterium from swine intestinal tract. Int J Syst Evol 78. Wang J, Ji H, Wang S, Liu H, Zhang W, Zhang D, et al. Probiotic Lactobacil- Microbiol. 2018;68(5):1737–42. https:// doi. org/ 10. 1099/ ijsem.0. 002738. lus plantarum promotes intestinal barrier function by strengthening the 61. Morgan XC, Tickle TL, Sokol H, Gevers D, Devaney KL, Ward DV, et al. epithelium and modulating gut microbiota. Front Microbiol. 2018;9:1953. Dysfunction of the intestinal microbiome in inflammatory bowel disease https:// doi. org/ 10. 3389/ fmicb. 2018. 01953. and treatment. Genome Biol. 2012;13(9):R79. https:// doi. org/ 10. 1186/ 79. Zambruni M, Ochoa TJ, Somasunderam A, Cabada MM, Morales ML, gb- 2012- 13-9- r79. Mitreva M, et al. Stunting is preceded by intestinal mucosal damage and 62. Jonkers D, Penders J, Masclee A, Pierik M. Probiotics in the management microbiome changes and is associated with systemic inflammation in of inflammatory bowel disease: a systematic review of intervention stud- a cohort of Peruvian infants. Am J Trop Med Hyg. 2019;101(5):1009–17. ies in adult patients. Drugs. 2012;72(6):803–23. https:// doi. org/ 10. 2165/ https:// doi. org/ 10. 4269/ ajtmh. 18- 0975. 11632 710- 00000 0000- 00000. 80. Wu GD, Chen J, Hoffmann C, Bittinger K, Chen YY, Keilbaugh SA, et al. 63. Abe F, Ishibashi N, Shimamura S. Eec ff t of administration of bifidobac- Linking long-term dietary patterns with gut microbial enterotypes. Sci- teria and lactic acid bacteria to newborn calves and piglets. J Dairy Sci. ence. 2011;334(6052):105–8. https:// doi. org/ 10. 1126/ scien ce. 12083 44. 1995;78(12):2838–46. https:// doi. org/ 10. 3168/ jds. S0022- 0302(95) 76914-4. 64. Foditsch C, Pereira RV, Ganda EK, Gomez MS, Marques EC, Santin T, et al. Oral administration of Faecalibacterium prausnitzii decreased the incidence of severe diarrhea and related mortality rate and increased weight gain in preweaned dairy heifers. PLoS ONE. 2015;10(12):e0145485. https:// doi. org/ 10. 1371/ journ al. pone. 01454 85. 65. Fan P, Kim M, Liu G, Zhai Y, Liu T, Driver JD, et al. The gut microbiota of newborn calves and influence of potential probiotics on reducing diar - Re Read ady y to to submit y submit your our re researc search h ? Choose BMC and benefit fr ? Choose BMC and benefit from om: : rheic disease by inhibition of pathogen colonization. Front Microbiol. 2021;12:772863. https:// doi. org/ 10. 3389/ fmicb. 2021. 772863. fast, convenient online submission 66. Carr A, Diener C, Baliga NS, Gibbons SM. Use and abuse of correlation thorough peer review by experienced researchers in your ﬁeld analyses in microbial ecology. ISME J. 2019;13(11):2647–55. https:// doi. org/ 10. 1038/ s41396- 019- 0459-z. rapid publication on acceptance 67. Lagkouvardos I, Lesker TR, Hitch TCA, Galvez EJC, Smit N, Neuhaus K, et al. support for research data, including large and complex data types Sequence and cultivation study of Muribaculaceae reveals novel species, • gold Open Access which fosters wider collaboration and increased citations host preference, and functional potential of this yet undescribed family. Microbiome. 2019;7(1):28. https:// doi. org/ 10. 1186/ s40168- 019- 0637-2. maximum visibility for your research: over 100M website views per year 68. Smith BJ, Miller RA, Schmidt TM. Muribaculaceae genomes assembled from metagenomes suggest genetic drivers of differential response to At BMC, research is always in progress. acarbose treatment in mice. mSphere. 2021;6(6):e0085121. https:// doi. Learn more biomedcentral.com/submissions org/ 10. 1128/ msphe re. 00851- 21. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Animal Science and Biotechnology Springer Journals http://www.deepdyve.com/lp/springer-journals/fecal-microbiota-dynamics-and-its-relationship-to-diarrhea-and-health-I10BRJGJMV

Loading next page...

References (96)

L. Haag, A. Fischer, B. Otto, R. Plickert, A. Kühl, U. Göbel, S. Bereswill, M. Heimesaat (2012)
Intestinal Microbiota Shifts towards Elevated Commensal Escherichia coli Loads Abrogate Colonization Resistance against Campylobacter jejuni in Mice
PLoS ONE, 7
Dong Wang, Yuanyi Du, Shuang Wang, Zhendong You, Yun Liu (2021)
Effects of sodium humate and glutamine combined supplementation on growth performance, diarrhea incidence, blood parameters, and intestinal microflora of weaned calves.
Animal science journal = Nihon chikusan Gakkaiho, 92 1
J. Castro, A. Gomez, B. White, H. Mangian, J. Loften, J. Drackley (2016)
Changes in the intestinal bacterial community, short-chain fatty acid profile, and intestinal development of preweaned Holstein calves. 1. Effects of prebiotic supplementation depend on site and age.
Journal of dairy science, 99 12
M. Kim, C. Yun, Hyun Kim, Jong Kim, S. Kang, Changgyu Lee, J. Ko, J. Ha (2010)
Effects of fermented soybean meal on growth performance, diarrheal incidence and immune-response of neonatal calves.
Animal science journal = Nihon chikusan Gakkaiho, 81 4
Leila Feizi, Sabihe Zad, S. Jalali, H. Rafiee, M. Jazi, Khaled Sadeghi, R. Kowsar (2020)
Fermented soybean meal affects the ruminal fermentation and the abundance of selected bacterial species in Holstein calves: a multilevel analysis
Scientific Reports, 10
Yun-Gi Kim, Kei Sakamoto, Sang-Uk Seo, J. Pickard, Merritt Gillilland, Nicholas Pudlo, Matthew Hoostal, Xue Li, Thomas Wang, Taylor Feehley, A. Stefka, T. Schmidt, E. Martens, S. Fukuda, N. Inohara, C. Nagler, G. Núñez (2017)
Neonatal acquisition of Clostridia species protects against colonization by bacterial pathogens
Science, 356
S. Roland, R. Ferone, R. Harvey, V. Styles, R. Morrison (1979)
The characteristics and significance of sulfonamides as substrates for Escherichia coli dihydropteroate synthase.
The Journal of biological chemistry, 254 20
Julian Trachsel, S. Humphrey, Heather Allen (2018)
Butyricicoccus porcorum sp. nov., a butyrate-producing bacterium from swine intestinal tract.
International journal of systematic and evolutionary microbiology, 68 5
K. Lesmeister, A. Heinrichs (2004)
Effects of corn processing on growth characteristics, rumen development, and rumen parameters in neonatal dairy calves.
Journal of dairy science, 87 10
M. Mayer, A. Abenthum, J. Matthes, D. Kleeberger, M. Ege, C. Hölzel, Johann Bauer, Karin Schwaiger (2012)
Development and genetic influence of the rectal bacterial flora of newborn calves.
Veterinary microbiology, 161 1-2
AJ Heinrichs (2005)
2828
J Dairy Sci, 88
Gábor Csárdi, T. Nepusz (2006)
The igraph software package for complex network research
, 1695
vegan : Community ecology package 2019
A Willems , MD Collins (1995)
16S rRNA gene similarities indicate thatHallella seregens(Moore and Moore) andMitsuokella dentalis(Haapsalo et al.) are genealogically highly related and are members of the genusPrevotella: emended description of the genusPrevotella(Shah and Collins) and description ofPrevotella dentaliscomb. nov
Int J Syst Bacteriol, 45
P. Glantz, T. Jacks (1969)
A bacteriological and serological study of experimental escherichia coli infection of calves.
Canadian journal of comparative medicine : Revue canadienne de medecine comparee, 33 2
Paul McMurdie, S. Holmes (2013)
phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data
PLoS ONE, 8
M. Penati, G. Sala, F. Biscarini, A. Boccardo, V. Bronzo, B. Castiglioni, P. Cremonesi, P. Moroni, D. Pravettoni, M. Addis (2021)
Feeding Pre-weaned Calves With Waste Milk Containing Antibiotic Residues Is Related to a Higher Incidence of Diarrhea and Alterations in the Fecal Microbiota
Frontiers in Veterinary Science, 8
MC Windeyer (2014)
231
Prev Vet Med, 113
J. Weese, S. Holcombe, RM Emberston, K. Kurtz, H. Roessner, Mohammad Jalali, S. Wismer (2015)
Changes in the faecal microbiota of mares precede the development of post partum colic.
Equine veterinary journal, 47 6
T. Hothorn, F. Bretz, P. Westfall (2008)
Simultaneous Inference in General Parametric Models
Biometrical Journal, 50
Madoka Ichikawa-Seki, D. Motooka, A. Kinami, F. Murakoshi, Yoko Takahashi, J. Aita, Kei Hayashi, Atsushi Tashibu, S. Nakamura, T. Iida, T. Horii, Y. Nishikawa (2019)
Specific increase of Fusobacterium in the faecal microbiota of neonatal calves infected with Cryptosporidium parvum
Scientific Reports, 9
P. Poudel, K. Froehlich, D. Casper, B. St-Pierre (2019)
Feeding Essential Oils to Neonatal Holstein Dairy Calves Results in Increased Ruminal Prevotellaceae Abundance and Propionate Concentrations
Microorganisms, 7
Qiong Wang, G. Garrity, J. Tiedje, J. Cole (2007)
Naïve Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy
Applied and Environmental Microbiology, 73
R. Podschun, U. Ullmann (1998)
Klebsiella spp. as Nosocomial Pathogens: Epidemiology, Taxonomy, Typing Methods, and Pathogenicity Factors
Clinical Microbiology Reviews, 11
D. Karasova, M. Crhanova, V. Babak, Martin Jeřábek, L. Brzobohatý, Zuzana Matesova, I. Rychlík (2020)
Development of piglet gut microbiota at the time of weaning influences development of postweaning diarrhea - A field study.
Research in veterinary science, 135
S Caballero (2017)
592
Cell Host Microbe, 21
D Jonkers, J Penders, A Masclee, M Pierik (2012)
Probiotics in the management of inflammatory bowel disease: a systematic review of intervention studies in adult patients
Drugs, 72
Euntae Kim, Sang-Jin Lee, Tae-Yong Kim, H. Lee, R. Atikur, B. Gu, Dong‐Hyeon Kim, Beom-Young Park, J. Son, M. Kim (2021)
Dynamic Changes in Fecal Microbial Communities of Neonatal Dairy Calves by Aging and Diarrhea
Animals : an Open Access Journal from MDPI, 11
T. Komatsu, Erina Yoshida, Ayumi Shigenaga, Nozomi Yasuie, Shintaro Uchiyama, Yuji Takamura, Kennosuke Sugie, K. Kimura, M. Haritani, T. Shibahara (2021)
Fatal suppurative meningoencephalitis caused by Klebsiella pneumoniae in two calves
The Journal of Veterinary Medical Science, 83
J. Castro, Andres Gomez, Bryan White, J. Loften, J. Drackley (2016)
Changes in the intestinal bacterial community, short-chain fatty acid profile, and intestinal development of preweaned Holstein calves. 2. Effects of gastrointestinal site and age.
Journal of dairy science, 99 12
M. Windeyer, K. Leslie, S. Godden, D. Hodgins, K. Lissemore, S. LeBlanc (2014)
Factors associated with morbidity, mortality, and growth of dairy heifer calves up to 3 months of age.
Preventive veterinary medicine, 113 2
Christian Quast, E. Pruesse, Pelin Yilmaz, Jan Gerken, Timmy Schweer, P. Yarza, J. Peplies, F. Glöckner (2012)
The SILVA ribosomal RNA gene database project: improved data processing and web-based tools
Nucleic Acids Research, 41
J. Pinheiro, D. Bates, R-core (1998)
LINEAR AND NONLINEAR MIXED-EFFECTS MODELS
D. Klein-Jöbstl, M. Iwersen, M. Drillich (2014)
Farm characteristics and calf management practices on dairy farms with and without diarrhea: A case-control study to investigate risk factors for calf diarrhea
Journal of Dairy Science, 97
H. Miura, K. Mukai, Keigo Sudo, S. Haga, Yutaka Suzuki, Y. Kobayashi, S. Koike (2021)
Effect of trehalose supplementation in milk replacer on the incidence of diarrhea and fecal microbiota in preweaned calves.
Journal of animal science
H. Nam, V. Srinivasan, B. Gillespie, S. Murinda, S. Oliver (2005)
Application of SYBR green real-time PCR assay for specific detection of Salmonella spp. in dairy farm environmental samples.
International journal of food microbiology, 102 2
HN Shah, MD Collins (1989)
Proposal to restrict the genus Bacteroides (Castellani and Chalmers) to Bacteroides fragilis and closely related species
Int J Syst Bacteriol, 39
JJ Castro (2016)
9682
J Dairy Sci., 99
Hongle Wu, Yue Ma, X. Peng, W. Qiu, Lixin Kong, B. Ren, Mingyun Li, Guo Cheng, Xuedong Zhou, Lei Cheng (2020)
Antibiotic-induced dysbiosis of the rat oral and gut microbiota and resistance to Salmonella.
Archives of oral biology, 114
D. Klein-Jöbstl, N. Quijada, M. Dzieciol, Benjamin Feldbacher, M. Wagner, M. Drillich, S. Schmitz-Esser, E. Mann (2019)
Microbiota of newborn calves and their mothers reveals possible transfer routes for newborn calves’ gastrointestinal microbiota
PLoS ONE, 14
N. Amin, S. Schwarzkopf, A. Kinoshita, Johanna Tröscher-Mußotter, S. Dänicke, A. Camarinha-Silva, K. Huber, J. Frahm, J. Seifert (2021)
Evolution of rumen and oral microbiota in calves is influenced by age and time of weaning
Animal Microbiome, 3
Byron Smith, Richard Miller, T. Schmidt (2020)
Muribaculaceae Genomes Assembled from Metagenomes Suggest Genetic Drivers of Differential Response to Acarbose Treatment in Mice
mSphere, 6
V. Aslan, M. Maden, O. Erganis, F. Bi̇rdane, M. Corlu (2002)
Clinical efficacy of florfenicol in the treatment of calf respiratory tract infections
Veterinary Quarterly, 24
Youngseob Yu, Changsoo Lee, Jaai Kim, Seokhwan Hwang (2005)
Group-specific primer and probe sets to detect methanogenic communities using quantitative real-time polymerase chain reaction.
Biotechnology and bioengineering, 89 6
M. Love, W. Huber, S. Anders (2014)
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Genome Biology, 15
N. Segata, J. Izard, L. Waldron, D. Gevers, L. Miropolsky, W. Garrett, C. Huttenhower (2011)
Metagenomic biomarker discovery and explanation
Genome Biology, 12
E. Zoetendal, H. Heilig, E. Klaassens, C. Booijink, M. Kleerebezem, H. Smidt, W. Vos (2006)
Isolation of DNA from bacterial samples of the human gastrointestinal tract
Nature Protocols, 1
H. Kim, T. Whon, Hojun Sung, Yun-Seok Jeong, E. Jung, N. Shin, Dong-Wook Hyun, P. Kim, June-Young Lee, C. Lee, J. Bae (2021)
Longitudinal evaluation of fecal microbiota transplantation for ameliorating calf diarrhea and improving growth performance
Nature Communications, 12
A. Jacobson, L. Lam, Manohary Rajendram, F. Tamburini, J. Honeycutt, Trung Pham, William Treuren, Kali Pruss, S. Stabler, Kyler Lugo, D. Bouley, J. Vilches-Moure, Mark Smith, J. Sonnenburg, A. Bhatt, K. Huang, D. Monack (2018)
A Gut Commensal-Produced Metabolite Mediates Colonization Resistance to Salmonella Infection.
Cell host & microbe, 24 2
YI Cho (2014)
1
J Vet Sci, 15
Fatemeh Aghakeshmiri, M. Azizzadeh, N. Farzaneh, M. Gorjidooz (2017)
Effects of neonatal diarrhea and other conditions on subsequent productive and reproductive performance of heifer calves
Veterinary Research Communications, 41
S. Akhter, Ansari, S. Andrabi, N. Ullah, Mazhar Qayyum (2008)
Effect of antibiotics in extender on bacterial and spermatozoal quality of cooled buffalo (Bubalus bubalis) bull semen.
Reproduction in domestic animals = Zuchthygiene, 43 3
Anne Brisson-Noel, P. Trieu-Cuot, P. Courvalin (1988)
Mechanism of action of spiramycin and other macrolides.
The Journal of antimicrobial chemotherapy, 22 Suppl B
V. Meganck, G. Hoflack, G. Opsomer (2014)
Advances in prevention and therapy of neonatal dairy calf diarrhoea: a systematical review with emphasis on colostrum management and fluid therapy
Acta Veterinaria Scandinavica, 56
J. Jang, Suhee Kim, Min-Sung Kwon, Jieun Lee, Dohyeon Yu, R. Song, Hak-Jong Choi, Jinho Park (2018)
Rotavirus-mediated alteration of gut microbiota and its correlation with physiological characteristics in neonatal calves
Journal of Microbiology (Seoul, Korea), 57
Alex Carr, C. Diener, N. Baliga, S. Gibbons (2019)
Use and abuse of correlation analyses in microbial ecology
The ISME Journal, 13
P. Fan, Miju Kim, Grace Liu, Yuting Zhai, Ting Liu, J. Driver, K. Jeong (2021)
The Gut Microbiota of Newborn Calves and Influence of Potential Probiotics on Reducing Diarrheic Disease by Inhibition of Pathogen Colonization
Frontiers in Microbiology, 12
D Klein-Jöbstl (2014)
5110
J Dairy Sci, 97
Joana Campolina, Sandra Coelho, A. Belli, Fernanda Machado, Luiz Pereira, Thierry Tomich, Wanessa Carvalho, Rodrigo Silva, Alessandra Voorsluys, David Jacob, Mariana Campos (2021)
Effects of a blend of essential oils in milk replacer on performance, rumen fermentation, blood parameters, and health scores of dairy heifers
PLoS ONE, 16
V Meganck (2014)
75
Acta Vet Scand, 56
A Jacobson (2018)
296
Cell Host Microbe, 24
Gary Wu, Jun Chen, C. Hoffmann, K. Bittinger, Ying-Yu Chen, S. Keilbaugh, M. Bewtra, D. Knights, W. Walters, R. Knight, R. Sinha, Erin Gilroy, Kernika Gupta, R. Baldassano, Lisa Nessel, Hongzhe Li, F. Bushman, J. Lewis (2011)
Linking Long-Term Dietary Patterns with Gut Microbial Enterotypes
Science, 334
S. Kargar, M. Roshan, S. Ghoreishi, A. Akhlaghi, M. Kanani, A. Shams-Abadi, M. Ghaffari (2020)
Extended colostrum feeding for 2 weeks improves growth performance and reduces the susceptibility to diarrhea and pneumonia in neonatal Holstein dairy calves.
Journal of dairy science
P. Pons, Matthieu Latapy (2004)
Computing Communities in Large Networks Using Random Walks
J. Graph Algorithms Appl., 10
Yong-il Cho, K. Yoon (2014)
An overview of calf diarrhea - infectious etiology, diagnosis, and intervention
Journal of Veterinary Science, 15
Yanyan Wu, Lili Wang, Ruiqing Luo, Hongli Chen, C. Nie, J. Niu, Cheng Chen, Yongping Xu, Xiaoyu Li, Wenju Zhang (2021)
Effect of a Multispecies Probiotic Mixture on the Growth and Incidence of Diarrhea, Immune Function, and Fecal Microbiota of Pre-weaning Dairy Calves
Frontiers in Microbiology, 12
D. Jonkers, J. Penders, A. Masclee, M. Pierik (2012)
Probiotics in the Management of Inflammatory Bowel Disease
Drugs, 72
Jonathan Friedman, E. Alm (2012)
Inferring Correlation Networks from Genomic Survey Data
PLoS Computational Biology, 8
YG Kim (2017)
315
Science, 356
C. Foditsch, R. Pereira, E. Ganda, M. Gomez, E. Marques, T. Santín, R. Bicalho (2015)
Oral Administration of Faecalibacterium prausnitzii Decreased the Incidence of Severe Diarrhea and Related Mortality Rate and Increased Weight Gain in Preweaned Dairy Heifers
PLoS ONE, 10
M. Zambruni, T. Ochoa, A. Somasunderam, M. Cabada, M. Morales, M. Mitreva, B. Rosa, Gonzalo Acosta, Natalia Vigo, M. Riveros, Sara Arango, D. Durand, Maitreyee Berends, P. Melby, N. Utay (2019)
Stunting Is Preceded by Intestinal Mucosal Damage and Microbiome Changes and Is Associated with Systemic Inflammation in a Cohort of Peruvian Infants.
The American journal of tropical medicine and hygiene
Christopher Broberg, Michelle Palacios, V. Miller (2014)
Klebsiella: a long way to go towards understanding this enigmatic jet-setter
F1000Prime Reports, 6
M. Strube (2021)
RibDif: can individual species be differentiated by 16S sequencing?
Bioinformatics Advances, 1
F. Abe, N. Ishibashi, S. Shimamura (1995)
Effect of administration of bifidobacteria and lactic acid bacteria to newborn calves and piglets.
Journal of dairy science, 78 12
S Kargar (2020)
8130
J Dairy Sci, 103
I. Lagkouvardos, T. Lesker, T. Hitch, Eric Gálvez, N. Smit, K. Neuhaus, Jun Wang, J. Baines, B. Abt, B. Stecher, J. Overmann, T. Strowig, T. Clavel (2019)
Sequence and cultivation study of Muribaculaceae reveals novel species, host preference, and functional potential of this yet undescribed family
Microbiome, 7
Muhammad Khan, H. Leè, W. Lee, Hasuck Kim, S. Kim, K. Ki, J. Ha, H. Lee, Yun-Jaie Choi (2007)
Pre- and postweaning performance of holstein female calves fed milk through step-down and conventional methods.
Journal of dairy science, 90 2
H. Shah, M. Collins (1989)
Proposal To Restrict the Genus Bacteroides (Castellani and Chalmers) to Bacteroides fragilis and Closely Related Species
International Journal of Systematic and Evolutionary Microbiology, 39
F Aghakeshmiri (2017)
107
Vet Res Commun, 41
Jing Wang, H. Ji, Sixin Wang, Hui Liu, Wei Zhang, Dongyan Zhang, Yamin Wang (2018)
Probiotic Lactobacillus plantarum Promotes Intestinal Barrier Function by Strengthening the Epithelium and Modulating Gut Microbiota
Frontiers in Microbiology, 9
J. Tap, M. Derrien, H. Törnblom, R. Brazeilles, Stéphanie Cools-Portier, J. Doré, S. Störsrud, B. Nevé, L. Öhman, M. Simrén (2017)
Identification of an Intestinal Microbiota Signature Associated With Severity of Irritable Bowel Syndrome.
Gastroenterology, 152 1
S. Meale, S. Li, P. Azevedo, H. Derakhshani, T. DeVries, J. Plaizier, M. Steele, E. Khafipour (2017)
Weaning age influences the severity of gastrointestinal microbiome shifts in dairy calves
Scientific Reports, 7
MA Khan (2007)
876
J Dairy Sci, 90
J. Campolina, S. Coelho, A. Belli, F. Machado, L. Pereira, T. Tomich, W. Carvalho, R. Silva, Alessandra Voorsluys, D. Jacob, M. Campos (2020)
Effects of a blend of essential oils in milk replacer on performance, rumen fermentation, blood parameters and health scores of dairy heifers
bioRxiv
B. Callahan, Paul McMurdie, Michael Rosen, Andrew Han, A. Johnson, S. Holmes (2016)
DADA2: High resolution sample inference from Illumina amplicon data
Nature methods, 13
Sofía Fernández-Ciganda, M. Fraga, P. Zunino (2021)
Probiotic Lactobacilli Administration Induces Changes in the Fecal Microbiota of Preweaned Dairy Calves
Probiotics and Antimicrobial Proteins, 14
T. Ma, C. Villot, David Renaud, A. Skidmore, E. Chevaux, M. Steele, L. Guan (2020)
Linking perturbations to temporal changes in diversity, stability, and compositions of neonatal calf gut microbiota: prediction of diarrhea
The ISME Journal, 14
L. Breiman (2001)
Random Forests
Machine Learning, 45
S. Caballero, Sohn Kim, R. Carter, Ingrid Leiner, Bože Susac, Liza Miller, Grace Kim, Lilan Ling, E. Pamer (2017)
Cooperating Commensals Restore Colonization Resistance to Vancomycin-Resistant Enterococcus faecium.
Cell host & microbe, 21 5
C. Ubeda, V. Bucci, S. Caballero, Ana Djukovic, Nora Toussaint, Michele Equinda, Lauren Lipuma, Lilan Ling, Asia Gobourne, Daniel No, Y. Taur, R. Jenq, M. Brink, J. Xavier, E. Pamer (2013)
Intestinal Microbiota Containing Barnesiella Species Cures Vancomycin-Resistant Enterococcus faecium Colonization
Infection and Immunity, 81
S. Meale, F. Chaucheyras-Durand, H. Berends, L. Guan, M. Steele (2017)
From pre- to postweaning: Transformation of the young calf's gastrointestinal tract.
Journal of dairy science, 100 7
A. Heinrichs, B. Heinrichs, Ofer Harel, G. Rogers, N. Place (2005)
A prospective study of calf factors affecting age, body size, and body condition score at first calving of holstein dairy heifers.
Journal of dairy science, 88 8
Mustafa Sibai, Ebru Altuntaş, Berna Yıldırım, G. Öztürk, S. Yildirim, T. Demircan (2020)
Microbiome and Longevity: High Abundance of Longevity-Linked Muribaculaceae in the Gut of the Long-Living Rodent Spalax leucodon.
Omics : a journal of integrative biology
Yo-Han Kim, R. Nagata, Natsuki Ohtani, T. Ichijo, K. Ikuta, Shigeru Sato (2016)
Effects of Dietary Forage and Calf Starter Diet on Ruminal pH and Bacteria in Holstein Calves during Weaning Transition
Frontiers in Microbiology, 7
Andy Liaw, M. Wiener (2007)
Classification and Regression by randomForest
X. Morgan, Timothy Tickle, H. Sokol, D. Gevers, K. Devaney, D. Ward, Joshua Reyes, Samir Shah, N. LeLeiko, S. Snapper, A. Bousvaros, J. Korzenik, B. Sands, R. Xavier, C. Huttenhower (2012)
Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment
Genome Biology, 13

Publisher: Springer Journals
Copyright: Copyright © The Author(s) 2022
eISSN: 2049-1891
DOI: 10.1186/s40104-022-00758-4
Publisher site: See Article on Publisher Site

Abstract

Background: Diarrhea is a major cause of morbidity and mortality in young calves, resulting in considerable eco- nomic loss for dairy farms. To determine if some gut microbes might have resistance to dysbiotic process with calf diarrhea by dictating the microbial co-occurrence patterns from birth to post-weaning, we examined the dynamic development of the gut microbiota and diarrhea status using two animal trials, with the first trial having 14 Holstein dairy calves whose fecal samples were collected 18 times over 78 d from birth to 15 d post-weaning and the second trial having 43 Holstein dairy calves whose fecal samples were collected daily from 8 to 18 days of age corresponding to the first diarrhea peak of trial 1. Results: Metataxonomic analysis of the fecal microbiota showed that the development of gut microbiota had three age periods with birth and weaning as the separatrices. Two diarrhea peaks were observed during the transi- tion of the three age periods. Fusobacteriaceae was identified as a diarrhea-associated taxon both in the early stage and during weaning, and Clostridium_sensu_stricto_1 was another increased genus among diarrheic calves in the early stage. In the neonatal calves, Prevotella_2 (ASV4 and ASV26), Prevotella_9 (ASV43), and Alloprevotella (ASV14) were negatively associated with Clostridium_sensu_stricto_1 (ASV48), the keystone taxa of the diarrhea-phase mod- ule. During weaning, unclassified Muribaculaceae (ASV28 and ASV44), UBA1819 (ASV151), Barnesiella (ASV497), and Ruminococcaceae_UCG-005 (ASV254) were identified being associated with non-diarrheic status, and they aggregated in the non-diarrhea module of co-occurrence patterns wherein unclassified Muribaculaceae (ASV28) and Barnesiella (ASV497) had a direct negative relationship with the members of the diarrhea module. Conclusions: Taken together, our results suggest that the dynamic successions of calf gut microbiota and the inter- actions among some bacteria could influence calf diarrhea, and some species of Prevotella might be the core micro - biota in both neonatal and weaning calves, while species of Muribaculaceae might be the core microbiota in weaning calves for preventing calf diarrhea. Some ASVs affiliated with Prevotella_2 (ASV4 and ASV26), Prevotella_9 (ASV43), Alloprevotella (AVS14), unclassified Muribaculaceae (ASV28 and ASV44), UBA1819 (ASV151), Ruminococcaceae_UCG-005 (ASV254), and Barnesiella (ASV497) might be proper probiotics for preventing calf diarrhea whereas Clostridium_sensu_ stricto_1 (ASV48) might be the biomarker for diarrhea risk in specific commercial farms. Keywords: Calf diarrhea, Co-occurrence pattern, Dynamic development, Fecal microbiota Background *Correspondence: jiakunwang@zju.edu.cn Diarrhea is one of the most common causes of mor- Institute of Dairy Science, College of Animal Sciences, Zhejiang University, bidity and mortality in young calves, especially dairy Hangzhou, China Full list of author information is available at the end of the article calves younger than one month [1, 2]. In the US, the © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 2 of 20 National Animal Health Monitoring System estimated weaning increase the risk of enteric diseases, particularly that more than half of the calf deaths were due to diar- diarrhea [17, 18]. rhea and related diseases [3]. A similar mortality rate Interestingly, some calves are prone to diarrhea, among dairy calves attributable to diarrhea was also whereas others are resistant to diarrhea. In the 56-day reported in Korea [1]. Calf diarrhea not only incurs experiment of Ma et. al [19], 13 out 42 milk replacer fed treatment cost but also lowers daily weight gain in calves never exhibited diarrhea, 18 calves exhibited diar- calves [4], delays first conceptions [5], decreases milk rhea and recovered after treated with electrolyte, but 11 production in the first lactation [6], consequently calves exhibited diarrhea and needed to be treated with resulting in a great economic loss for dairy farms. therapeutic antimicrobials. Ma et al. defined the lat - Many pathogens have been implicated in calf diar- ter two diarrhea status as resistant to diarrhea-induced + + rhea, including Escherichia coli K99 (E. coli K99 ), dysbiosis, and susceptible to diarrhea-induced dysbio- Salmonella spp., Clostridium perfringens, Crypto- sis, respectively. The age of first diarrhea of these calves sporidium parvum, bovine rotavirus (BRV), and bovine varied from d 8 to 19. Using the random forest machine coronavirus (BCoV) [1]. Non-infectious factors such as learning algorithm with the microbiota collected from improper colostrum management [7], weaning stress health calves at 7, 14, and 21 days of age and diarrhea [8], and poor feeding environments can also increase calves prior to the onset of diarrhea, Ma et al. suggest that the risk of calf diarrhea [9]. Moreover, oftentimes diarrhea could be predicted by the microbiota shift in uncertain causality of diarrhea occurrence and cross early life. Kim et al. [20] described the similar age (5–50 infection together make diarrhea difficult to prevent days of age) fecal microbiota transplantation could ame- and treat. liorate calf diarrhea with increasing the family Porphy- Diarrhea is much more common among young ani- romonadaceae. Therefore, we hypothesized that the gut mals, especially after birth to shortly after weaning than microbiota might strongly correlate to calf diarrhea for among adult animals because of higher risk of enteric some gut microbes might have resistance to the dysbiotic pathogens colonization in young animals than in adult process with calf diarrhea from birth to post-weaning. animals [10, 11]. The high risk of colonization by patho - To test our hypothesis, the stages with high incidence of gens in young animals stems from their underdeveloped diarrhea were identified in cohort from birth to shortly gut and gut microbiota. Indeed, Kim et al. found that the after weaning, and the fecal microbiotas between diar- absence of some species of Clostridia in the gut micro- rheic calves and non-diarrheic calves in stages of high biota of neonatal mice made them unable to resist the incidence of diarrhea, and between diarrhea phases (pre- colonization by Salmonella enterica Typhimurium or diarrhea, diarrhea and post-diarrhea) were compared, Citrobacter rodentium [12]. In addition, dysbiosis of gut and the diarrheic status- and diarrhea phase-associated microbiota induced by certain factors such as antibiot- amplicon sequence variants (ASVs) and modules were ics and diseases can also lower resistance and increase identified. enteric pathogen infection. Wu et al. demonstrated that dysbiosis of gut microbiota in rat induced by antibiot- Methods ics lowered the resistance to colonization by Salmonella Animal experiment and sample collection [13]. Compared to uninfected calves, calves infected Two animal trials were conducted on a commercial farm with rotavirus have a higher abundance of Escherichia, located in Shaoxing (more than 3800 cows in stock), Clostridium_g21, Streptococcus, and Clostridium, but Zhejiang Province, China from September to Novem- a lower abundance of Lactobacillus, Subdoligranulum, ber 2017 (trial 1) and November to December 2017 Blautia, and Coprococcus_g2 in the fecal microbiota [14]. (trial 2). In trial 1, 14 female Holstein calves (initial Weaning is a stressful process for young animals bodyweight = 38.2 ± 2.0 kg , mean ± SD) were enrolled and can lower resistance to pathogenic colonization. at birth. Immediately after birth, the calves were sepa- Haag et al. found that infant mice after weaning were rated from their dams and moved to individual pens deficient in preventing their gut from colonization by (1.0 m × 1.2 m) bedded with dry wheat straw and each Campylobacter jejuni [15]. During weaning, dairy calves offered 4 L of thawed colostrum (pooled the colostrum are stressed nutritionally, and their gut microbiota from other mothers before the trial, and stored at – 80 undergoes compositional changes [16]. Typically, dur- ºC) via an esophageal tube within 2 h after birthing. All ing weaning, the gut microbiota increases its diversity, neonatal calves in this trial were fed the same colos- and some important taxa, such as Bacteroides, Blau- trum. From 1 to 5 days of age, each calf was bottle-fed tia, Ruminococcus, and Succinivibrio, may change their 2 L of whole milk each at 0700, 1330, and 1800 h. Dur- abundance [17]. The nutritional stresses coupled with ing these 5 d, 4 g each of tylosin tartrate and sulfadimi- changes in gut microbiota during and immediately after dine soluble (both powder) was mixed with the morning Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 3 of 20 milk and fed to each calf for prophylaxis of pneumo- TAT CTAAT-3’) to prepare amplicon libraries with each nia. At d 6, all the calves were moved to one group pen being labeled with a unique barcode sequence [23]. The (8.0 m × 10.0 m) bedded with a rubber mat and dry amplicon libraries of all the samples were pooled in wheat straw. The straw was replaced every other day. The equal molar ratio and paired-end (2 × 250) sequenced calves had free access to a preset volume of whole milk on the Illumina HiSeq platform by Novogene Bioin- (Additional file 1: Fig. S1) with an automated milk feeder formatics Technology Co., Ltd. (Tianjin, China). After system (Förster-Technik, Engen, Germany). Briefly, the demultiplexing, the paired-end sequencing reads were calves get 6 L/d of whole milk from d 6 and this amount processed using the DADA2 package (version 1.8.0) increased evenly up to 8 L/d on d 22. From d 23 to 41, in R and its pipeline [24]. Briefly, barcodes and primers each calf received 8 L of whole milk daily, and from d were removed from the reads. Reads with more than 2 42 to 62, the milk allowance was decreased evenly from expected errors (maxN = 0, maxEE = c(2, 2), truncQ = 2) 8 to 2 L/d. Milk supply was denied using the automated were filtered out. Dereplication and inference were per - milk feeder system once a calf consumed 2 L of milk formed using the DADA2 pipeline. After merging the within 2 h during each meal to avoid overconsumption paired reads and chimera filtering, an ASV table was of milk. All the calves were weaned off milk at d 63. Calf constructed (to resolve bacteria at the species level [25]). starter pellets and a hay mixture of oat and alfalfa were The ASVs were taxonomically assigned based on the offered ad libitum in the group pen. All the calves had SILVA 16S rRNA gene database (version 132) [26] using free access to clean drinking water throughout the trial. a naive Bayesian classifier method [27] implemented in Fecal samples were collected directly via rectal stimula- DADA2. The sequences of each sample were rarefied to tion from every calf of the trial at d 1, 3, 5, 7, 9, 12, 15, same size with the minimum number of sequences in 18, 28, 38, 48, 58, 62, 63, 65, 68, 73, and 78 (18 times in samples (35,137 sequences/sample in trial 1, and 19,986 total). The fecal samples were placed in 2 mL nuclease- sequences/sample in trial 2) using the ‘rarefy_even_ free tubes, immediately frozen in liquid nitrogen, then depth’ function in Phyloseq package (version 1.24.2) [28], each month the 15 L liquid nitrogen tank with samples and ASVs that appeared only once among all the samples was transported to the lab and samples were stored at were removed from the dataset. Alpha diversity metrics – 80 °C. The DNA of all the stored fecal samples was including observed species and Shannon diversity index extracted in one month after the trial. When fecal sam- were calculated using the Phyloseq package. Faith’s phy- ples were collected, fecal scores were recorded based logenetic diversity (Faith’s PD) was calculated using the on fecal fluidity [21]: 1 = normal, 2 = soft, 3 = runny, or Picante package (version 1.8.2) in R, and evenness was 4 = watery. Calves that had a fecal score of 3 or 4 were calculated using the Microbiome package (version 1.12.0) considered diarrheic. No additional antibiotics were in R. To minimize individual variance of calves, only the offered to calves after the diarrhea episode. ASVs that were observed in at least 20% of calves at every In trial 2, 43 female Holstein calves (initial body- single day (3 out of 14 calves in trial 1) or every single weight = 36.6 ± 1.9 kg, mean ± SD) were enrolled at birth. diarrheic status-associated phase (trial 2) were subjected The calves were fed, nursed, and sampled exactly the to the downstream analysis. same as for those in trial 1, but fecal samples were col- lected daily from d 8 to 18, which corresponded to the Pathogen detection age when the first diarrhea peak was observed in trial 1. Major pathogens that can lead to calf diarrhea were tested in the fecal samples of trial 1 when diarrhea (fecal Metataxonomic analysis of the fecal microbiota score ≥ 3) was first noted in a calf. Salmonella spp. was DNA was extracted from individual fecal samples accord- detected by PCR with specific primers (F:5′-TCG TCA ing to the method of Zoetendal et al. with minimal modi-TTC CAT TAC CTA CC-3′ and R:5′-AAA CGT TGA AAA fications [22]. In brief, approximately 0.2 g of frozen fecal ACT GAG GA-3′ [29] using fecal DNA samples. A com- sample each was homogenized together with 1 mL of TE mercial ELISA kit (BIO K 315, Bio-X Diagnostics, Roche- buffer using bead-beating (Biospec Products; Bartlesville, fort, Belgique) each was used to detect BRV, BCoV, and OK, United States) followed by phenol:chloroform-based E. coli (through its F5 attachment factor) in the fecal DNA extraction. Agarose gel (1%) electrophoresis was samples. performed to evaluate the DNA quality, and the DNA concentrations were determined using a NanoDrop Evaluation of the effects of age, diarrheic status, 2000 spectrophotometer (Thermo Scientific, Waltham, and diarrheic phases on fecal microbiota MA, United States). The V3-V4 region of the 16S rRNA A generalized linear mixed-effects model implemented gene was amplified using primers 341F (5’-CCT AYG in the nlme package (version 3.1.137) [30] in R was used GGRBGCASCAG-3’) and 806R (5’-GGA CTA CNNGGG to evaluate the effect of age and diarrhea on the alpha Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 4 of 20 diversity metrics of the fecal microbiota, and Tukey’s all- performed to estimate the optimal number of top-rank- pair comparison test using the ‘glht’ function in the mult- ing age-associated genera required for prediction using comp package (version 1.4.8) [31] in R was used to do the rfcv function implemented in the randomForest the multiple comparisons. In trial 1, the model included package in R. The identified genera were shown in a heat - calf age and diarrheic status (diarrheic vs. non-diarrheic map with their relative abundance. These genera were calves) among the calves as fixed effects and individual considered microbial markers of the respective ages. calves as random effect: Y = μ + A + H + I + ε ijk i j k ijk Fecal microbiota comparison between diarrheic statuses, between diarrhea phases, and identification of their In trial 2, the model included diarrhea phases (pre- associated ASVs and modules diarrhea, diarrhea, and post-diarrhea, see the results for Fecal microbiotas between the diarrheic and non-diar- the delineation of diarrhea phases) as fixed effect and rheic calves in trial 1 and between diarrhea phases (pre- individual calves as random effect: diarrhea, diarrhea, and post-diarrhea) in trial 2 were Y = μ + S + I + ε mk m k mk compared using principal coordinates analysis (PCoA) and permutational multivariate analysis of variance (PER- where Y and Y represent the variable of interest; ijk mk MANOVA/adonis) based on Bray–Curtis dissimilar- A is the fixed effect of calf age; H is the fixed effect of i j ity. When the fecal microbiotas of diarrheic calves were calf diarrheic status, defined as non-diarrheic or diar - significantly different from those of age-matched non- rheic status based on fecal score; S is the fixed effect of diarrheic calves, in trial 1, and when the fecal microbio- calf diarrhea phases, defined as pre-diarrhea phase, diar - tas of the calves with diarrhea differed significantly from rhea phase, or post-diarrhea phase based on the temporal those at their pre- or post-diarrhea phase in trial 2, Lin- changes of fecal scores of the same calves; I is the ran- ear discriminant analysis Effect Size (LEfSe) [35] and sig - dom effect of individual calves; and ε and ε are the ijk mk nificance test with DESeq2 [36] were used to identify the residual error. Non-parametric Kruskal–Wallis test was ASVs that might be associated with one of the diarrheic used to assess the effects of age and diarrhea on bacterial statuses or one of the diarrhea phases. Of the ASVs with relative abundance, and Dunn’s all-pairs rank comparison an LDA score > 2 in LEfSe or an adjusted P-value < 0.05 in test with P adjusted by false discovery rate was used to DESeq2, those with a log fold change > 1 (diarrheic/non- conduct multiple comparisons. A significant change was diarrheic calves, or diarrhea/pre- or post-diarrhea phase) declared with P < 0.05. were considered to be associated with diarrheic status or phase, whereas those with a log fold change < – 1 (defined the same as above) were considered associated Fecal microbiota comparison among ages with non-diarrheic status or phase. and identification of age‑associated genera of bacteria Co-occurrence patterns of the fecal microbiotas of the In trial 1, the overall fecal microbiotas between two diarrheic and non-diarrheic calves (see the results for the ages were pairwise compared using analysis of similar- delineation of the stages) in trial 1, or the fecal microbi- ity (ANOSIM) implemented in the Vegan package (ver- ota of the calves at different diarrhea phases were exam - sion 2.5.3) [32] in R. When P < 0.05, the fecal microbiotas ined using the SparCC algorithm [37] with ASV count between two ages were considered completely different table as the input data. The pattern was visualized using (R-value > 0.75), different (0.5 < R-value < 0.75), or tended the igraph package (version 1.2.5) [38] in R, and correla- to be different (0.3 < R-value < 0.5). R-value < 0.3 was con- tions with a P < 0.05 and a co-efficient R ≥ 0.5 or ≤ – 0.5 sidered not different. being considered positive and negative correlations, Random forest regression was used to identify the fecal respectively. Modules of the co-occurrence patterns bacterial genera that were associated with the age of the were generated using the walktrap algorithm [39] imple- calves using the randomForest package (version 4.6.14) mented in igraph. Modules with less than 3 nodes were [33, 34] in R. The genus table of all the samples was the deleted from the co-occurrence patterns. The identified input data. The random forest algorithm was executed ASVs associated with a diarrheic status or diarrhea phase with the default parameters (ntree = 1000, default mtry of were highlighted in the patterns. The modules aggregated p/3, where p is the number of input genera (‘features’)). with ASVs that were associated with diarrhea in trial 1 The importance of a genus was ranked in the order of or with the diarrhea phase in trial 2 were considered as its ‘feature importance’, with feature importance being diarrhea modules and diarrhea-phase modules, respec- the decrease in prediction accuracy (in percent) of the tively, whereas the modules formed with ASVs that were model when that genus was removed. To explore the age- not with diarrhea in trial 1 or associated with pre- or associated microbiota development, cross-validation was Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 5 of 20 post-diarrhea phases in trial 2 were considered as non- Prevotellaceae_UCG-003, Rikenellaceae_RC9_gut_group, diarrhea modules or pre- or post-diarrhea modules. Ruminococcaceae_UCG-005, Ruminococcaceae_UGC-010, and Lachnospiraceae_FCS020_group increased their rela- Results tive abundance (P < 0.05) during the later days of the trial Trial 1 and maintained or decreased their relative abundance Development of fecal bacterial microbiota of calves until the end of the trial. Acinetobacter differed from all and temporal microbial successions the other genera as it lost its initial high relative abundance In total, 15,283,464 quality-filtered amplicon sequences dramatically by d 3 (P < 0.05) and never recovered. Prevo- were obtained from 251 fecal samples (the fecal sample tella_9 only increased (P < 0.05) on d 9. of calf Y03 on d 5 was not analyzed due to contamina- tion with wheat straw) with an average of 60,890 ± 7193 Composition and distribution of age‑associated bacterial (mean ± SD) sequences per sample. The sequencing genera from birth to post‑weaning depth coverage reached > 99.96% on average (99.89% to Pair-wise comparison of the fecal microbiotas at the 100.00%). In the fecal samples collected from 1 day of age, ASV level among the 18 time points using ANOSIM 345 ASVs (referred to as species hereafter) on average per revealed three age periods, with the first, second, and sample were identified with a Shannon diversity index of third age periods being from d 1 to 12, 15 to 63, and 58 4.03 (Fig. 1A). The number of observed species, Faith’s to 78, respectively. The fecal microbiotas were simi - PD, Shannon diversity index, and evenness decreased lar (R-value < 0.5) within each age period but different from d 1 to 7 but recovered at d 9. Then, observed spe - between most of the two age periods (Table 1). cies, Faith’s PD, Shannon index, and evenness gradually Thirty-five age-associated bacterial genera were identi - increased, though with fluctuation, to about 509, 28.77, fied by random forest regression (Fig. 2A and B), and they 5.03 and 0.81, respectively, at d 78 (Fig. 1A). Over this were distributed in 4 clusters (Fig. 2C) each corresponding period, age significantly (P < 0.05) affected all the diversity to one of the age periods (Table 1). Of these age-associated metrics, whereas diarrheic status did not affect (P > 0.05) genera, Klebsiella, Escherichia/Shigella, Enterococcus, Bac- any of these four metrics. teroides, Butyricicoccus and Megamonas in the first cluster Collectively, the ASVs were classified into 200 genera were predominant at the early age (d 1 to 12); Alloprevotella, within 12 phyla. Bacteroidota, Firmicutes, Proteobacte- Faecalibacterium, Intestinimonas, Paraprevotella, UBA1819, ria, and Fusobacteria each had a relative abundance > 1% Lachnospiraceae_UCG-010, Pygmaiobacter, and Subdol- in more than 60% of the fecal samples at any day (Fig. 1B). igranulum in the second cluster were predominant at d 15 to Of the 200 bacterial genera identified across all the fecal 58; Blautia, Breznakia, Agathobacter, Anaeroplasma, Rom- samples, 15 genera each had a relative abundance > 1% boutsia, Erysipelotrichaceae_UCG-004, Prevotella_9, and Suc- in at least 60% of the fecal samples at a single day. These cinivibrio in the third cluster and Candidatus_Stoquefichus, genera included Alloprevotella, Bacteroides, Escherichia/ Parasutterella, Lachnospiraceae_NK4A136_group, Oscillibac- Shigella, Faecalibacterium, Fusobacterium, Acinetobacter, ter, Prevotellaceae_UCG-003, Family_XIII_AD3011_group, Prevotellaceae_UCG-003, Rikenellaceae_RC9_gut_group, Lachnospiraceae_FCS020_group, Rikenellaceae_RC9_gut_ Ruminococcaceae_UCG-005, Ruminococcaceae_UGC-010, group, Prevotellaceae_UCG-001, Ruminococcaceae_UCG-005, Butyricicoccus, Lachnospiraceae_FCS020_group, Para- Ruminococcaceae_UCG-010, Negativibacillus, and Tyzzerella bacteroides, Prevotella_9 and Sutterella. These pre - in the fourth cluster were predominant at d 48 to 68 and d 58 dominant genera displayed temporal changes in relative to 78, respectively. abundance over the course of the trial (Fig. 1C). Allo- prevotella, Faecalibacterium, Parabacteroides, and Sut- Diarrhea characteristics of the study cohort terella increased their relative abundance (P < 0.05) Over the course of the trial 1, all the calves had fecal and maintained a higher abundance around d 15 to score ≥ 3 at least one sampling day (Fig. 3A). Based on 58 compared to d 1 and then decreasing towards the fecal scores of all the calves, the 78 d of trial was the end of the trial. Compared to d 1, Bacteroides, divided into five stages: stage 1: d 1 to 7, before the first Escherichia/Shigella, Fusobacterium, and Butyricicoc- diarrhea peak; stage 2: d 9 to 15, the first diarrhea peak; cus increased and then decreased their relative abun- stage 3: d 18 to 38, the stage after the first peak but dance sharply (P < 0.05) at around d 10. On the contrary, before the second diarrhea peak; stage 4: d 48 to 68, the (See figure on next page.) Fig. 1 Dynamic changes of fecal bacterial microbiota of calves from birth to post-weaning (trial 1). Dynamic changes of alpha diversity metrics with the red boxes indicating the diarrhea peaks (A), bacterial phyla (B), and major genera (C) across ages. Only the phyla and genera each with a relative abundance > 1% in at least 60% of samples at any single age were shown. The relative abundance significantly differing from that of d 1 is indicated with a * (P < 0.05) Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 6 of 20 Fig. 1 (See legend on previous page.) Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 7 of 20 Table 1 A matrix of R-values of pair-wise comparison of the fecal microbiota of trial one at the ASV level using ANOSIM R 1 3 5 7 9 12 15 18 28 38 48 58 62 63 65 68 73 78 1 0 3 0.43 0 * * 5 0.41 0.20 0 * * * 7 0.45 0.40 0.10 0 * * * 9 0.43 0.62 0.38 0.07 0 * * * * * 12 0.46 0.73 0.60 0.41 0.25 0 * * * * * 15 0.57 0.89 0.83 0.70 0.53 0.09 0 * * * * * * 18 0.68 0.92 0.91 0.83 0.74 0.39 0.02 0 * * * * * * * * 28 0.68 0.97 0.96 0.86 0.67 0.59 0.24 0.20 0 * * * * * * * * * 38 0.67 0.96 0.96 0.83 0.64 0.55 0.37 0.44 0.12 0 * * * * * * * * * * 48 0.66 0.95 0.93 0.87 0.73 0.59 0.28 0.18 0.10 0.11 0 * * * * * * * * * * * 58 0.64 0.91 0.89 0.76 0.63 0.56 0.49 0.58 0.48 0.29 0.27 0 * * * * * * * * * * * 62 0.61 0.88 0.84 0.78 0.60 0.48 0.45 0.60 0.51 0.30 0.30 0.03 0 * * * * * * * * * * * 63 0.62 0.90 0.86 0.76 0.58 0.47 0.42 0.56 0.53 0.34 0.31 0.00 ‑0.06 0 * * * * * * * * * * * * 65 0.68 0.88 0.83 0.77 0.56 0.57 0.57 0.75 0.73 0.57 0.63 0.27 0.08 0.04 0 * * * * * * * * * * * * * * 68 0.61 0.83 0.81 0.76 0.59 0.51 0.52 0.68 0.68 0.57 0.60 0.42 0.24 0.19 0.01 0 * * * * * * * * * * * * * * 73 0.63 0.90 0.86 0.79 0.60 0.57 0.56 0.76 0.74 0.59 0.64 0.39 0.20 0.15 0.01 ‑0.03 0 * * * * * * * * * * * * * * * 78 0.72 0.95 0.94 0.87 0.71 0.65 0.67 0.85 0.87 0.77 0.79 0.56 0.36 0.28 0.14 0.07 ‑0.05 0 Represents the pair-wise comparison with P-value < 0.05. Those with R-value < 0.5 are blod With P-value < 0.05, the fecal microbiotas of two ages were considered completely different at R-value > 0.75; different at 0.5 < R-value < 0.75; tended to be different at a 0.3 < R-value < 0.5; not different at R-value < 0.3 second diarrhea peak; and stage 5: d 75 to 78, the stage Ruminococcaceae_UCG-005 (8 ASVs), Flavonifractor (4 after the second diarrhea peak. All the 14 calves were ASVs) and Bacteroides (3 ASVs). non-diarrheic in stages 1, 3, and 5. Pathogen detection The co-occurrence pattern for stage 4 had 195 nodes, showed that all the 20 diarrheic fecal samples (9 in the 603 edges, and 17 modules (Fig. 4A, Additional file 3: first and 11 in the second diarrhea peaks) were Salmo - Table S1). Nineteen diarrhea-associated ASVs and 5 non- nella spp. and BCoV negative, but eight were E. coli diarrhea-associated ASVs showed up in the co-occur- K99 positive (2 in the first and 6 in the second diar - rence pattern. The diarrhea-associated ASVs including rhea peaks). One of the diarrheic samples (in the second Prevotellaceae_UCG-003 (ASV11, ASV43), Ruminococ- diarrhea peak) was both BRV and E. coli K99 positive caceae_UCG-010 (ASV427, ASV574), Butyricicoccus (Fig. 3A). (ASV130), Lachnospiraceae_FCS020_group (ASV234) and unclassified Ruminococcaceae (ASV557) were scat - ASVs and modules associated with diarrheic status tered without aggregation in any of the modules. Bacte- The fecal microbiotas differed among the five stages roides (ASV170, ASV206), Rikenellaceae_RC9_gut_group (P < 0.001, Fig. 3B). The fecal microbiotas of diarrheic and (ASV33), Ruminococcaceae_UCG-010 (ASV196), Lach- non-diarrheic calves did not differ (P = 0.450) in stage 2 nospiraceae_FCS020_group (ASV174), unclassified (Fig. 3C) but did differ (P = 0.004) in stage 4 (Fig. 3D). Bacteroidales (ASV177), unclassified Barnesiellaceae Based on fold change and LEfSe or DESeq2 analysis, (ASV22) and unclassified Ruminococcaceae (ASV340) 147 diarrheic status-associated ASVs were identified in were aggregated in diarrhea module 1 (D-M1), while stage 4 including 91 diarrhea-associated ASVs and 56 Bacteroides (ASV39, ASV41, ASV145, and ASV259) non-diarrhea-associated ASVs (Additional file 2: Fig. were aggregated in diarrhea module 2 (D-M2). The non- S2). The diarrhea-associated ASVs mainly consisted of diarrhea-associated Muribaculaceae (ASV28 and ASV44) Bacteroides (15 ASVs), Ruminococcaceae_UCG-005 and UBA1819 (ASV151) were aggregated in non-diar- (8 ASVs), Ruminococcaceae_UCG-010 (6 ASVs), rhea module (ND-M). The non-diarrhea-associated Ruminococcaceae_UCG-013 (4 ASVs) and Barnesiella (ASV497) and Ruminococcaceae_UCG-005 Lachnospiraceae_FCS020_group (4 ASVs). The (ASV254) were aggregated in D-M1, and they formed non-diarrhea-associated ASVs mainly consisted of negative correlations with the ASVs in D-M1. The ASVs in D-M1 and D-M2 had a higher (P < 0.05) total relative Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 8 of 20 Fig. 2 Age-associated bacterial genera from birth to post-weaning (trial 1). The top 35 age-associated bacterial genera ranked by importance to the accuracy of the random forest regression model (A). Ten-fold cross-validation error as a function of the number of input genera was used to regress against the chronologic age of calves. The dotted line indicates the 35 genera used in the model (B). Heatmap of the top 35 age-associated genera and the clusters they formed based on their relative abundance across ages (C) abundance in diarrheic calves than in non-diarrheic d 8 to 18 respectively. In which, 32 calves had one or calves (Fig. 4B). The D-M1 was mainly occupied by more episodes of diarrhea 1 to 5 d after recovering from Bacteroides, unclassified Barnesiellaceae, and Rikenel - a previous episode (Fig. 5A). Except of d 9, 10 and 11, laceae_RC9_gut_group, with 1.49%, 1.53% and 0.75% the microbial composition had no significant difference relative abundance, respectively; D-M2 was occupied by between diarrheic and non-diarrheic calves within the Fusobacterium and Bacteroides, with 1.67% and 1.58% same age (Additional file 4: Fig. S3). At the same age, relative abundance, respectively, but the ND-M was calves were in different days of diarrhea, so microbial mainly occupied by unclassified Muribaculaceae, Prevo - changes were also out of synchronization (Fig. S3). So tellaceae_Ga6A1_group, Prevotellaceae_UCG-003, and based on the temporal changes of the fecal scores, the Prevotella_9, with 1.90%, 0.69%, 0.57%, and 0.42% relative fecal samples were divided into four phases: pre-diar- abundance, respectively (Fig. 4C). rhea (when fecal score < 3), diarrhea (fecal scores ≥ 3 consecutively), post-diarrhea (fecal score falling below Trial 2 3), and volatility (fecal score rising to ≥ 3 after 1 to 5 d Diarrhea characteristics of the study cohort below 3) (Fig. 5B). To track the microbial changes from Over the course of the trial 2, all the calves had fecal the pre-diarrhea to post-diarrhea, samples of calves of score ≥ 3 at least 1 d. (Fig. 5A). In the 43 calves, 4, 20, different ages with or without diarrhea were combined 33, 31, 30, 18, 18, 16, 15, 4 and 5 calves had diarrhea on into phases for analysis. Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 9 of 20 Fig. 3 Development stages of the fecal microbiota based on fecal score and the comparison of the overall fecal microbiota from birth to post-weaning (trial 1). Development stages of the 14 calves (A). Principal coordinates analysis (PCoA) plots comparing the fecal microbiotas among the 5 stages (B) and between diarrheic and non-diarrheic calves at stage 2 (C) and stage 4 (D). Age refers to days after birth. The gradual weaning started at d 42 and ended at d 63 Changes in fecal bacterial communities of calves from pre‑ Butyricicoccus, and Lachnoclostridium maintained their to post‑diarrhea relative abundance of the diarrhea phase. Although not In total, 18,706,378 quality-filtered amplicon sequences having changed their relative abundance from the pre- were obtained from 340 fecal samples (the fecal samples diarrhea phase to the diarrhea phase, the genera Fae- of pre-diarrhea, diarrhea, and post-diarrhea phases) with calibacterium, Prevotella_2, and Alloprevotella increased an average of 55,019 ± 10,329 (mean ± SD) sequences per their relative abundance (P < 0.05), while the phylum sample. The sequencing depth coverage reached > 99.97% Epsilonbacteraeota and the genus Campylobacter on average (99.91% to 99.99%). The fecal microbiotas dif - decreased their relative abundance (P < 0.05) in the post- fered among pre-diarrhea, diarrhea, and post-diarrhea diarrhea phase. phases (P < 0.01, Fig. 5C). The lowest number of observed species (on average ASVs and modules associated with the diarrhea phase 117 ASVs per sample) and Shannon index (3.2) were Comparison of the fecal microbiotas between pre-diar- found in the fecal samples of the diarrhea phase (Fig. 6A). rhea and diarrhea phases revealed (based on fold change Both metrics increased (P < 0.05) in the post-diarrhea and analysis using LEfSe or DESeq2) 32 pre-diarrhea- phase. Of the major phyla and genera (each with a rela- associated ASVs and 29 diarrhea phase-associated ASVs tive abundance > 1% in > 60% of the fecal samples at any (Additional file 5: Fig. S4). The pre-diarrhea phase-asso - phase), the phylum Bacteroidota decreased (P < 0.05), ciated ASVs mainly consisted of Bacteroides (12 ASVs), while the phylum Fusobacteria increased (P < 0.05) from Parabacteroides (4 ASVs), Butyricicoccus (3 ASVs), and the pre-diarrhea to diarrhea phase (Fig. 6B), and the gen- Flavonifractor (3 ASVs). The diarrhea phase-associated era Bacteroides, Butyricicoccus, and Lachnoclostridium ASVs mainly consisted of Clostridium_sensu_stricto_1 decreased (P < 0.05), while Clostridium_sensu_stricto_1 (8 ASVs), unclassified Fusobacteriaceae (7 ASVs), and Fusobacterium increased (P < 0.05) during the phase Fusobacterium (6 ASVs), and unclassified Enterobac - transition (Fig. 6C). In the post-diarrhea phase, the phyla teriaceae (3 ASVs). The co-occurrence pattern of the Bacteroidota and Fusobacteria and the genus Fusobac- pre-diarrhea and diarrhea phases had 44 nodes, 78 edges, terium tended to return their relative abundance to the and 4 modules (Fig. 7A). The pre-diarrhea and diar - pre-diarrhea level (P < 0.05), while the genus Clostrid- rhea phase-associated modules (pre-D-M and D-P-M, ium_sensu_stricto_1 lost the relative abundance gained in respectively) were aggregated with six and 16 pre-diar- the diarrhea phase (P > 0.05), and the genera Bacteroides, rhea and diarrhea phase-associated ASVs, respectively, Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 10 of 20 Fig. 4 Co-occurrent pattern of the fecal microbiota at stage 4 in trial 1. Pattern showing the diarrhea status-associated ASVs and modules (A). Mean relative abundance of diarrhea status-associated modules in diarrheic and non-diarrheic calves (B). ASVs relative abundance at genus level in different modules (C). ASV254 and ASV497 in module 2 were not included in plots (B) or (C) and both modules were independent. Module 3 and formed a negative correlation with the D-P-M through module 4 were aggregated with 14 and 5 ASVs, respec- Prevotella_2 (ASV26) and Clostridium_sensu_stricto_1 tively. Both module 3 and module 4 had no diarrhea (ASV48). The ASVs in the D-P-M had a higher (P < 0.05) or pre-diarrhea phase-associated ASVs, but module 4 total relative abundance in the diarrhea phase than in the Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 11 of 20 Fig. 5 Transition phase division of the 43 calves based on fecal score in trial 2. Fecal score of the 43 calves from d 8 to 18 (A). Phase division based on samples fecal score (B). Principal coordinates analysis (PCoA) plot of fecal microbiotas among pre-diarrhea, diarrhea, and post-diarrhea phases (C) pre-diarrhea phase, whereas the opposite was true for both belonging to Prevotella_2), both of which had a the ASVs in pre-D-M (Fig. 7B). The D-P-M was occu - negative relationship with Clostridium_sensu_stricto_1 pied by ASVs assigned to Clostridium_sensu_stricto_1 (ASV48), a diarrhea phase-associated ASV and the key- and Fusobacterium, with a relative abundance of 29.11% stone of D-P-M. Two post-diarrhea phase-associated and 5.42%, respectively. The pre-D-M was only occupied modules (post-D-M1 and post-D-M2) were identified in by ASVs of Bacteroides, with a relative abundance about the co-occurrence network, and post-D-M1 and post- 11.77%. The ASVs of module 3 mainly consisted of All - D-M2 were aggregated with 11 and 5 post-diarrhea prevotella, Bacteroides and Prevotella_9, with 2.94%, phase-associated ASVs, respectively. The composition 1.65% and 0.55% relative abundance, respectively. The of post-D-M1 was similar to that of module 3 in Fig. 7A, ASVs of module 4 were classified to Prevotella_2 and but post-D-M1 had more betweenness and close cen- Lachnospiraceae_UCG-004, with 4.48% and 1.05% rela- trality than module 3 and formed a negative correlation tive abundance, respectively (Fig. 7C). with D-P-M through Alloprevotella (ASV14) and Prevo- When compared the ASVs between diarrhea and tella_9 (ASV43), both of which negatively associated with post-diarrhea phases, 44 post-diarrhea phase-associated Clostridium_sensu_stricto_1 (ASV48) in the D-P-M. The ASVs and 23 diarrhea phase-associated ASVs were iden- ASV composition of module 2 was the same as that of tified based on fold change and analysis using LEfSe or pre-D-M in Fig. 7A, and module 2 was standalone from DESeq2 (Additional file 4: Fig. S4). The post-diarrhea the other four modules (Fig. 7D). Module 5 consisted of phase-associated ASVs mainly consisted of Prevotella_9 six ASVs. Although having no diarrhea and post-diarrhea (13 ASVs), Alloprevotella (8 ASVs), Bacteroides (4 ASVs), phase-associated ASVs, module 5 positively connected and Collinsella (3 ASVs). The diarrhea phase-associated with D-P-M through Tyzzerella_4 (ASV23) and Clostrid- ASVs mainly consisted of Clostridium_sensu_stricto_1 ium_sensu_stricto_1 (ASV48). The D-P-M was occupied (6 ASVs), unclassified Fusobacteriaceae (7 ASVs), and by Fusobacterium, Prevotella_2 and Clostridium_sensu_ unclassified Enterobacteriaceae (4 ASVs). The co-occur - stricto_1 with a relative abundance of 30.94%, 5.34% and rence pattern constructed of the fecal microbiotas at 5.19%, respectively (Fig. 7F). The post-D-M1 was mainly post-diarrhea and diarrhea phases had 57 nodes, 116 occupied by Alloprevotella, Bacteroides and Prevotella_9, edges, and 5 modules (Fig. 7D). The D-P-M was aggre - with a combined relative abundance of 4.80%, 2.32%, and gated with 11 diarrhea phase-associated ASVs and two 0.88%, respectively. The post-D-M2 was only occupied by post-diarrhea phase-associated ASVs (ASV4 and ASV26, Prevotella_9, with a relative abundance of 0.21%. Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 12 of 20 Fig. 6 Fecal microbiota changes from pre-diarrhea, diarrhea, and post-diarrhea phases in trial 2. Two alpha diversity metrics of the fecal microbiota (A), Relative abundance of predominant bacterial phyla (B) and genera (C). Only the phyla and genera each with a relative abundance > 1% in at least 60% of samples in a single phase were shown. Significant differences between two phases were indicated with a * (P < 0.05) Discussion supplementation [40], feeding waste milk containing A better understanding of the fecal microbiota in diar- antibiotic residues [41] or supplemented with sodium rheic and non-diarrheic calves can inform improved humate and glutamine combination [42], or single spe- treatment and prevention strategies. Fecal microbio- cies [43] or multispecies probiotics [44]. These interven - tas between diarrheic and non-diarrheic calves have tions affected the developing process of gut microbiota been compared after interventions, such as trehalose and increased the abundance of Bifidobacterium and Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 13 of 20 Fig. 7 Co-occurrent pattern of the fecal microbiota among three phases in trial 2. Pattern among pre-diarrhea and diarrhea phases (A). Mean relative abundance of phase-associated modules in pre-diarrhea and diarrhea (B). ASVs relative abundance at genus level in pre-diarrhea and diarrhea modules (C). Pattern among diarrhea and post-diarrhea phases (D). Mean relative abundance of phase-associated modules in diarrhea and post-diarrhea (E). ASVs relative abundance at genus level in diarrhea and post-diarrhea modules (F). ASV4 and ASV26 in diarrhea-M were divided into post-D-M1 in plot (E) and (F) Lactobacillus, which might conceal the natural devel- we first examined the dynamic development of the gut opment of resistance to pathogenic colonization in pre- microbiota (represented by fecal microbiota) and diar- weaned calves. Without these types of interventions, rhea occurrence in dairy calves from birth to 15 d post- Kim et al. [20] described the ability of a fecal microbiota weaning with frequently fecal sampling in trial 1, which transplantation (inclusion was collected from the health allowed us to identify two diarrhea peaks. Then we ana - calves of the similar age to diarrheic calves) could ame- lyzed the fecal microbiota and diarrhea occurrence of liorate diarrhea and restore gut microbial composition another group of dairy calves over 11 d corresponding to in pre-weaning calves. Most of these comparative stud- the first diarrhea peak with fecal samples collected daily. ies focused on pre-weaning calves. In the present study, Trial 1 helped test our hypothesis that some gut microbes Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 14 of 20 might have resistance to dysbiotic process with calf diar- diarrhea peak, but it should be given special attention in rhea by dictating the microbial co-occurrence patterns calf industry because of its pathogenic significance. during weaning, while trial 2 allowed us to examine in Bacteroides was the most abundant genus through details as the calves transitioned from pre-diarrhea to the whole period, but its relative abundance increased diarrhea and then to post-diarrhea phases shortly after sharply to 41.96%, 49.66%, and 40.38% at d 5, 7, and 9, birth. respectively, so it could be an age-associated bacterial The development of the gut microbiota appeared to genus for the first two weeks after birth. Its high abun - have three age periods with birth and weaning as the dance was attributed to its stronger saccharolytic ability separatrices [45, 46]. As antibiotics had been used from than Prevotella in the gut of young calves [58, 59]. The d 1 to 5, the drastically decreasing of bacterial richness acetate produced by Bacteroides could be consumed by after birth (Fig. 1A) might be the effects of tylosin tartrate other bacteria, such as Butyricicoccus and Megamonas, to and sulfadimidine. However, in the recent study of neo- produce butyrate and propionate [60], both of which are natal dairy calves, without antibiotics, a similar decrease the main source of energy for intestinal epithelial cells, of bacterial richness was reported by Kim et al. [47] and and butyrate can also inhibit the signaling pathways of Klein-Jöbstl et al. [48]. In the first two weeks after birth, pro-inflammatory cytokines [61], enhance intestinal bar - the underdeveloped gut was colonized primarily by fac- rier function by increasing mucin secretion and enhanc- ultatively anaerobic microbes, especially Escherichia/ ing the tight-junctions [62]. Thus, the colonization by Shigella, to render the intestinal environment suitable for these microbes in the gut of young calves might facili- anaerobic intestinal microbes to colonize, so the decrease tate the establishment of a functional gut. From the third of bacterial richness might be a result of bacterial adapta- week onward, weaning separated the microbiota char- tion. Escherichia/Shigella had a low relative abundance in acteristic of the two periods. Prior to weaning for more the fecal samples at any day after birth to 15 d after wean- than 40 d, the microbiotas remained similar, suggesting ing, but its relative abundance increased up to 45.36% that the gut might have established a stable functional and 31.51% on d 3 and 5, respectively, and then decreased microbiota over that period, with limited recruitment. to around 2% since d 15. The increased colonization by However, with the transition from liquid to total solid Escherichia/Shigella in young calves has been described feed, some of the gut bacteria displayed considerable previously [49], and it might explain the susceptibil- changes, as exemplified by the replacement of Alloprevo - ity of calves to diarrhea caused by E. coli. Klebsiella, tella, Faecalibacterium, and Parabacteroides by Prevo- which belongs to the same family, Enterobacteriaceae, as tellaceae_UCG-003, Rikenellaceae_RC9_gut_group, Escherichia/Shigella, also appeared to be an age-associ- Ruminococcaceae_UCG-005, Ruminococcaceae_UGC- ated bacterial genus for this period. Klebsiella pneumo- 010, and Lachnospiraceae_FCS020_group. These five niae [50] and Klebsiella oxytoca [51] are opportunistic genera are within families that contain species capable pathogenic in humans and are associated with increased of utilizing structural polysaccharides, and this replace- infection mortality rate, particularly in immunocompro- ment might facilitate the degradation of structural poly- mised individuals, neonates, and the elderly. However, saccharides and the stability of the gut microbiota. Future infections in calves caused by Klebsiella have not been research can help identify the species of these five gen - reported frequently. Glantz and Jacks reported that Kleb- era and characterize their functions. Two diarrhea peaks siella spp. occurred naturally in calves, and they might were observed during the three developing periods of the be responsible for some mortality [52]. Komatsu et al. gut microbiota, and less than half of the tested diarrheic reported fatal suppurative meningoencephalitis caused fecal samples were pathogen positive, which suggests that by K. pneumoniae in two calves [53]. Aslan et al. reported microbiota homeostasis may be more important in pre- that K. pneumoniae could be isolated in calves suffering venting diarrhea than directly killing pathogens. Com- from respiratory tract infection, which was not cured paring fecal microbiota transplantation and antibiotic by florfenicol [54]. In the present study, we observed treatment for ameliorating calf diarrhea, Kim et al. [20] changes of relative abundance of Klebsiella, decreas- confirmed that gut microbial manipulation could offer ing from 1.07% and 2.09% on d 1 and 3, respectively, to another therapeutic paradigm, beyond antibiotic based less than 0.1% at d 5. Tylosin tartrate and sulfadimine are therapies. Our data also suggest that prophylaxis/pre- both broad spectrum antibiotics exerting their antimi- ventions with probiotics should be better administered crobial action by inhibiting the bacterial protein synthe- in d 1 to 7 (stage 1, before the first diarrhea peak) and d sis [55], and competing with para-aminobenzoic acid for 18 to 38 (stage 3, the stage after the first peak but before dihydropteroate synthase [56], respectively. The decrease the second diarrhea peak). Supplementation of newborn in Klebsiella might be a result of tylosin tartrate [57]. The calves with Lactobacillus and Bifidobacterium [63] or peak of Klebsiella abundance did not correspond to a Faecalibacterium prausnitzii [64] within the first 7 d of Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 15 of 20 life decreased diarrhea, but no study was found in the associated with diarrhea, both ASV151 and ASV254 literature that had tested probiotic supplementation in (both assigned to Ruminococcaceae) were associated stage 3. To promote the natural development of the gut with non-diarrhea. So Muribaculaceae (ASV28 and microbiotas during the transition, the potential probiot- ASV44), UBA1819 (ASV151), Barnesiella (ASV497), and ics in the gut of calves should be identified. Ruminococcaceae_UCG-005 (ASV254) might be used as Comparison of the fecal microbiota using LEfSe and potential probiotics for this specific commercial farm, the subsequent identification of the correlations between with this specific microbial colonization patterns. The the differential bacterial genera and the core bacterial sequences of these ASVs were included in Table 2, and genera in the gut is a well-trodden path to reveal the these ASVs can be verified in future studies. Fusobac - effects of diarrhea on the microbiota and find potential teriaceae dominated D-M2 (Fig. 4C). Fusobacteriaceae probiotics in neonatal dairy calves for preventing diar- was reported to have high relative abundances in dairy rhea [65]. But many standard correlation analyses may calves suffering from diarrhea, either infected [72] or lead to misleading results because 16S rRNA gene pro- uninfected [65], which indicates that module analysis can filing data are sparse and compositional [37]. SparCC, help identify bacteria associated with diarrhea or oth- which is tailored to the compositional and sparse features erwise. With this module analysis, Muribaculaceae and of genomic survey data and allows for inference of corre- Prevotella were identified as the core microbiota resist - lations between genes or species, has been used to eluci- ing diarrhea in weaning calves. Kim et al. reported that date the networks of interaction among microbial species substituting fermented soybean meal (FSBM) for soybean living in or on the human body [37, 66]. Therefore, to meal (SBM) at 5% level in calf starter reduced the inci- reduce the incidence of false positive results, three levels dence of diarrhea and improved immunocompetence in of evaluation criteria including co-occurrence patterns neonatal calves after microbial infection [73]. The rea - examined using the SparCC algorithm (correlations with son for the positive effects of FSBM on immunocompe - a P < 0.05 and a co-efficient R ≥ 0.5 or ≤ – 0.5 being con- tence was not reported, but in a recent study, Feizi et al. sidered positive and negative correlations, respectively), found that FSBM increased the abundance of Prevotella |log fold change|> 1, LDA score > 2 in LEfSe or adjusted ruminicola in the rumen of dairy calves [74]. Essential P-value < 0.05 in DESeq2 were used in the present study. oils showed similar effects on dairy calves, increasing the During the weaning, some ASVs and modules were asso- Prevotellaceae abundance in the rumen [75] and decreas- ciated with diarrhea, while some were associated with ing the morbidity of neonatal diarrhea among pre-wean- non-diarrhea (Fig. 4 and Fig. S2). ing calves [76]. Furthermore, a recent study reported With the three levels of evaluation criteria, the identi- that sodium humate and glutamine in combination also fied diarrhea-associated ASVs were aggregated in Bac - elevated the abundance of P. ruminicola in the rectum teroides (ASV39, ASV41, ASV145, ASV170, ASV206, while reducing diarrhea incidence among dairy calves and ASV259). These members might be biomarkers of during the weaning period [42]. Tap et al. reported that diarrhea risk. Muribaculaceae (ASV28 and ASV44), Prevotellaceae enterotype was less susceptible to irrita- UBA1819 (ASV151), Barnesiella (ASV497), and Rumino- ble bowel syndrome (IBS) compared with Bacteroidaceae coccaceae_UCG-005 (ASV254) were non-diarrhea-asso- enterotype [77]. Therefore, future research is warranted ciated ASVs in the co-occurrence pattern, and ASV28 to investigate the relationship between calf diarrhea and and ASV497 had a direct inhibitory relationship with Prevotella as a genus and its species. Prevotella may also the members of D-M1 (Fig. 4A). Muribaculaceae, which be explored for its preventative ability to reduce calf was previously assigned as family S24-7 or Homeother- diarrhea. maceae, is a common and abundant family of symbiotic Consistent changes in relative abundance of Bacte- bacteria in the gut and specialized in fermenting com- roides, Butyricicoccus, Faecalibacterium, Alloprevotella, plex carbohydrates [67]. It responded most positively to and Fusobacterium were observed in both trial 1 and acarbose treatment for diabetes [68] and was linked to trial 2 (Fig. 1, 2 and 6). When the fecal microbiota was longevity [69]. Barnesiella belongs to the family Porphy- examined in detail as the calves transitioned from pre- romonadaceae within the phylum Bacteroidota. It was diarrhea to diarrhea and then to post-diarrhea phases found to suppress the growth of intestinal vancomycin- in trial 2, the peak of both Clostridium_sensu_stricto_1 resistant Enterococcus [70]. Members of Ruminococ- and Fusobacterium coincided with the peak of diarrhea. caceae are mostly butyrate-producing bacteria. Weese It has been reported that Clostridium_sensu_stricto_1 et al. suggested that Firmicutes (particularly Lachno- might cause epithelial inflammation in piglets [78] and spiraceae and Ruminococcaceae)/Proteobacteria ratio stunting in infants (defined as height-for-age Z score might be used to potentially predict and prevent colic equal to or lower than – 2, [79]). Therefore, research [71]. Although some members of Ruminococcacea were is needed to further investigate these two genera with Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 16 of 20 Table 2 The sequences of ASVs with potentials being probiotics and biomarker of diarrhea risk ASV ID Taxonomy Sequence ASV4 Prevotella_2TGA GGA ATA TTG GTC AAT GGA CGG GAG TCT GAA CCA GCC AAG TAG CGT GCA GGA TGA CGG CCC TAT GGG TTG TAA ACT GCT TTT ATA GGG GGA TAA AGT GTG CCA CGT GTG GCA TAT TGC AGG TAC CCT ATG AAT AAG GAC CGG CTA ATT CCG TGC CAG CAG CCG CGG TAA TAC GGA AGG TCC GGG CGT TAT CCG GAT TTA TTG GGT TTA AAG GGA GCG TAG GCC GTC TTA TAA GCG TGT TGT GAA ATG TCG GGG CTC AAC CTG GGC ATT GCA GCG CGA ACT GTG AGA CTT GAG TGC GCA GGA AGT AGG CGG AAT TCG TCG TGT AGC GGT GAA ATG CTT AGA TAT GAC GAA GAA CTC CGA TTG CGA AGG CAG CCT GCT GTA GCG CAA CTG ACG CTG AAG CTC GAA AGC GTG GGT ATC GAA CAGG ASV14 AlloprevotellaTGA GGA ATA TTG GTC AAT GGA CGC AAG TCT GAA CCA GCC AAG TAG CGT GCA GGA CGA CGG CCC TCC GGG TTG TAA ACT GCT TTT AGT TGG GAA TAA AGT GCA GCT CGT GAG CTG TTT TGT ATG TAC CAT CAG AAA AAG GAC CGG CTA ATT CCG TGC CAG CAG CCG CGG TAA TAC GGA AGG TCC GGG CGT TAT CCG GAT TTA TTG GGT TTA AAG GGA GCG CAG GCG GAC TCT TAA GTC AGT TGT GAA ATA CGG CGG CTC AAC CGT CGG ACT GCA GTT GAT ACT GGG AGT CTT GAG TGC ACA CAG GGA TGC TGG AAT TCA TGG TGT AGC GGT GAA ATG CTC AGA TAT CAT GAA GAA CTC CGA TCG CGA AGG CAG GTA TCC GGG GTG CAA CTG ACG CTG AGG CTC GAA AGT GCG GGT ATC AAA CAGG ASV26 Prevotella_2TGA GGA ATA TTG GTC AAT GGA CGA GAG TCT GAA CCA GCC AAG TAG CGT GCA GGA CGA CGG CCC TAT GGG TTG TAA ACT GCT TTT ATA GGG GGA TAA AGT GTG CCA CGT GTG GCA TAT TGC AGG TAC CCT ATG AAT AAG GAC CGG CTA ATT CCG TGC CAG CAG CCG CGG TAA TAC GGA AGG TCC GGG CGT TAT CCG GAT TTA TTG GGT TTA AAG GGA GCG TAG GCC GTC TTA TAA GCG TGT TGT GAA ATG TCG GGG CTC AAC CTG GGC ATT GCA GCG CGA ACT GTG AGA CTT GAG TGC GCA GGA AGT AGG CGG AAT TCG TCG TGT AGC GGT GAA ATG CTT AGA TAT GAC GAA GAA CTC CGA TTG CGA AGG CAG CCT GCT GTA GCG CAA CTG ACG CTG AAG CTC GAA AGC GTG GGT ATC GAA CAGG ASV43 Prevotella_9TGA GGA ATA TTG GTC AAT GGA CGA GAG TCT GAA CCA GCC AAG TAG CGT GCA GGA AGA CGG CCC TAT GGG TTG TAA ACT GCT TTT ATA AGG GAA TAA AGT GAG TCT CGT GAG ACT TTT TGC ATG TAC CTT ATG AAT AAG GAC CGG CTA ATT CCG TGC CAG CAG CCG CGG TAA TAC GGA AGG TCC GGG CGT TAT CCG GAT TTA TTG GGT TTA AAG GGA GCG TAG GCC GGA GAT TAA GCG TGT TGT GAA ATG TAG ACG CTC AAC GTC TGC ACT GCA GCG CGA ACT GGT TTC CTT GAG TAC GCA CAA AGT GGG CGG AAT TCG TGG TGT AGC GGT GAA ATG CTT AGA TAT CAC GAA GAA CTC CGA TTG CGA AGG CAG CTC ACT GGA GCG CAA CTG ACG CTG AAG CTC GAA AGT GCG GGT ATC GAA CAGG ASV28 MuribaculaceaeTGA GGA ATA TTG GTC AAT GGG CGC AGG CCT GAA CCA GCC AAG TCG CGT GAG GGA GGA CGG TCC TAC GGA TTG TAA ACC TCT TTT GTC GGG GAG TAA CGT GCG GGA CGC GTC CCG TAT TGA GAG TAC CCG AAG AAA AAG CAT CGG CTA ACT CCG TGC CAG CAG CCG CGG TAA TAC GGA GGA TGC GAG CGT TAT CCG GAT TTA TTG GGT TTA AAG GGT GCG CAG GCG GCG CGC CAA GTC AGC GGT CAA AGT TCC GGG CTC AAC CCG GTG TCG CCG TTG AAA CTG GCG TGC TCG AGT GCG TGC GAG GAA GGC GGA ATG CGT TGT GTA GCG GTG AAA TGC ATA GAT ATG ACG CAG AAC TCC GAT TGC GAA GGC AGC TTT CCA GCG CGC TAC TGA CGC TGA GGC ACG AAA GCG TGG GGA TCG AAC AGG ASV44 MuribaculaceaeTGA GGA ATA TTG GTC AAT GGG CGC AGG CCT GAA CCA GCC AAG TCG CGT GAG GGA AGA CGG TCC TAC GGA TTG TAA ACC TCT TTT GTC GGG GAG TAA CGT GCG GGA CGC GTC CCG TAT TGA GAG TAC CCG AAG AAA AAG CAT CGG CTA ACT CCG TGC CAG CAG CCG CGG TAA TAC GGA GGA TGC GAG CGT TAT CCG GAT TTA TTG GGT TTA AAG GGT GCG CAG GCG GCG CGC CAA GTC AGC GGT CAA AGT TCC GGG CTC AAC CCG GTG TCG CCG TTG AAA CTG GCG TGC TCG AGT GCG TGC GAG GAA GGC GGA ATG CGT TGT GTA GCG GTG AAA TGC ATA GAT ATG ACG CAG AAC TCC GAT TGC GAA GGC AGC TTT CCA GCG CGC TAC TGA CGC TGA GGC ACG AAA GCG TGG GGA TCG AAC AGG ASV151 UBA1819TGG GGA ATA TTG CAC AAT GGG GGA AAC CCT GAT GCA GCG ACG CCG CGT GGA GGA AGA AGG TTT TCG GAT TGT AAA CTC CTG TCT TCG GGG ACG ATA ATG ACG GTA CCC GAG GAG GAA GCC ACG GCT AAC TAC GTG CCA GCA GCC GCG GTA AAA CGT AGG TGG CAA GCG TTG TCC GGA ATT ACT GGG TGT AAA GGG AGC GCA GGC GGG TCG GCA AGT TGG AGG TGA AAG CTG TGG GCT CAA CCC ACA AAC TGC CTT CAA AAC TGC CGA TCT TGA GTG GTG TAG AGG TAG GCG GAA TTC CCG GTG TAG CGG TGG AAT GCG TAG ATA TCG GGA GGA ACA CCA GTG GCG AAG GCG GCC TAC TGG GCA CTA ACT GAC GCT GAG GCT CGA AAG CAT GGG TAG CAA ACA GG ASV497 BarnesiellaTGA GGA ATA TTG GTC AAT GGT CGG CAG ACT GAA CCA GCC AAG TCG CGT GAG GGA AGA CGG CCC TAC GGG TTG TAA ACC TCT TTT GTC GGA GAG TAA AGT ACG CTA CGT GTA GTG TAT TGC AAG TAT CCG AAG AAA AAG CAT CGG CTA ACT CCG TGC CAG CAG CCG CGG TAA TAC GGA GGA TGC AAG CGT TAT CCG GAT TTA TTG GGT TTA AAG GGT GCG TAG GCG GCA CGC CAA GTC AGC GGT GAA ATT TCC GGG CTC AAC CCG GAC TGT GCC GTT GAA ACT GGC GAG CTA GAG TAC ACA AGA GGC AGG CGG AAT GCG TGG TGT AGC GGT GAA ATG CAT AGA TAT CAC GCA GAA CCC CGA TTG CGA AGG CAG CCT GCT AGG GTG AAA CAG ACG CTG AGG CAC GAA AGC GTG GGT ATC GAA CAGG ASV254 Ruminococcaceae UCG-005TGG GGA ATA TTG GGC AAT GGG GGA AAC CCT GAC CCA GCA ACG CCG CGT GAA GGA AGA AGG CCC TCG GGT TGT AAA CTT CTT TTA CCA GGG ACG AAG GAC GTG ACG GTA CCT GGA GAA AAA GCA ACG GCT AAC TAC GTG CCA GCA GCC GCG GTA ATA CGT AGG TTG CAA GCG TTG TCC GGA TTT ACT GGG TGT AAA GGG CGT GTA GGC GGA GCT GCA AGT CAG ATG TGA AAT CCC GGG GCT CAA CCC CGG AAC TGC ATT TGA AAC TGT AGC CCT TGA GTA TCG GAG AGG CAA GCG GAA TTC CTA GTG TAG CGG TGA AAT GCG TAG ATA TTA GGA GGA ACA CCA GTG GCG AAG GCG GCT TGC TGG ACG ACA ACT GAC GCT GAG GCG CGA AAG CGT GGG GAG CAA ACAGG ASV48 Clostridium sensu_stricto_1TGG GGA ATA TTG CAC AAT GGG GGA AAC CCT GAT GCA GCA ACG CCG CGT GAG TGA TGA CGG CCT TCG GGT TGT AAA GCT CTG TCT TTG GGG ACG ATA ATG ACG GTA CCC AAG GAG GAA GCC ACG GCT AAC TAC GTG CCA GCA GCC GCG GTA ATA CGT AGG TGG CAA GCG TTG TCC GGA TTT ACT GGG CGT AAA GGG AGC GTA GGC GGA TTT TTA AGT GGG ATG TGA AAT ACC CGG GCT CAA CCT GGG TGC TGC ATT CCA AAC TGG AAA TCT AGA GTG CAG GAG GGG AAA GTG GAA TTC CTA GTG TAG CGG TGA AAT GCG TAG AGA TTA GGA AGA ACA CCA GTG GCG AAG GCG ACT TTC TGG ACT GTA ACT GAC GCT GAG GCT CGA AAG CGT GGG GAG CAA ACA GG Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 17 of 20 Table 2 (continued) ASV ID Taxonomy Sequence ASV39 BacteroidesTGA GGA ATA TTG GTC AAT GGA CGA GAG TCT GAA CCA GCC AAG TAG CGT GAA GGA TGA AGG TCC TAC GGA TTG TAA ACT TCT TTT ATA AGG GAA TAA ACC CTC CCA CGT GTG GGA GCT TGT ATG TAC CTT ATG AAT AAG CAT CGG CTA ACT CCG TGC CAG CAG CCG CGG TAA TAC GGA GGA TGC GAG CGT TAT CCG GAT TTA TTG GGT TTA AAG GGA GCG CAG ACG GGT CGT TAA GTC AGC TGT GAA AGT TTG GGG CTC AAC CTT AAA ATT GCA GTT GAT ACT GGC GTC CTT GAG TGC GGT TGA GGT GTG CGG AAT TCG TGG TGT AGC GGT GAA ATG CTT AGA TAT CAC GAA GAA CTC CGA TTG CGA AGG CAG CAC ACT AAG CCG TAA CTG ACG TTC ATG CTC GAA AGT GTG GGT ATC AAA CAGG ASV41 BacteroidesTGA GGA ATA TTG GTC AAT GGG CGA GAG CCT GAA CCA GCC AAG TAG CGT GAA GGA TGA AGG TCC TAC GGA TTG TAA ACT TCT TTT ATA AGG GAA TAA AAC GCT CCA CGT GTG GAG CCT TGT ATG TAC CTT ATG AAT AAG CAT CGG CTA ACT CCG TGC CAG CAG CCG CGG TAA TAC GGA GGA TGC GAG CGT TAT CCG GAT TTA TTG GGT TTA AAG GGA GCG CAG ACG GGA TGT TAA GTC AGC TGT GAA AGT TTG CGG CTC AAC CGT AAA ATT GCA GTT GAT ACT GGC GTT CTT GAG TGC AGT TGA GGT GTG CGG AAT TCG TGG TGT AGC GGT GAA ATG CTT AGA TAT CAC GAA GAA CTC CGA TTG CGA AGG CAG CTC ACT AAA CTG TAA CTG ACG TTC ATG CTC GAA AGT GTG GGT ATC AAA CAGG ASV145 BacteroidesTGA GGA ATA TTG GTC AAT GGG CGA GAG CCT GAA CCA GCC AAG TAG CGT GAA GGA TGA AGG TCC TAT GGA TTG TAA ACT TCT TTT ATA AGG GAA TAA AAC GCT CCA CGT GTG GAG CCT TGT ATG TAC CTT ATG AAT AAG CAT CGG CTA ACT CCG TGC CAG CAG CCG CGG TAA TAC GGA GGA TGC GAG CGT TAT CCG GAT TTA TTG GGT TTA AAG GGA GCG CAG ACG GGA TGT TAA GTC AGC TGT GAA AGT TTG CGG CTC AAC CGT AAA ATT GCA GTT GAT ACT GGC GTT CTT GAG TGC AGT TGA GGT GTG CGG AAT TCG TGG TGT AGC GGT GAA ATG CTT AGA TAT CAC GAA GAA CTC CGA TTG CGA AGG CAG CTC ACT AAA CTG TAA CTG ACG TTC ATG CTC GAA AGT GTG GGT ATC AAA CAGG ASV170 BacteroidesTGA GGA ATA TTG GTC AAT GGT CGG AAG ACT GAA CCA GCC AAG TAG CGT GAA GGA TGA AGG TTC TAT GGA TTG TAA ACT TCT TTT ATA CGG GAA TAA AAC CAC CTA CGT GTA GGT GCT TGT ATG TAC CGT ATG AAT AAG CAT CGG CTA ACT CCG TGC CAG CAG CCG CGG TAA TAC GGA GGA TGC GAG CGT TAT CCG GAT TTA TTG GGT TTA AAG GGA GCG TAG ACG GGG GAT TAA GTC AGT TGT GAA AGG CTG CGG CTC AAC CGC AGC ACT GCA GTT GAT ACT GGT TTC CTT GAG TGC GGT TGA GGT GTA TGG AAT TCG TGG TGT AGC GGT GAA ATG CTT AGA TAT CAC GAA GAA CTC CGA TTG CGA AGG CAG TAC ACT AAG CCG TAA CTG ACG TTG AGG CTC GAA AGT GTG GGT ATC AAA CAGG ASV206 BacteroidesTGA GGA ATA TTG GTC AAT GGC CGG AAG GCT GAA CCA GCC AAG TAG CGT GAA GGA TGA AGG TTC TAT GGA TTG TAA ACT TCT TTT ATA CGG GAA TAA AAC CAC CTA CGT GTA GGT GCT TGT ATG TAC CGT ATG AAT AAG CAT CGG CTA ACT CCG TGC CAG CAG CCG CGG TAA TAC GGA GGA TGC GAG CGT TAT CCG GAT TTA TTG GGT TTA AAG GGA GCG TAG ACG GGA TGT TAA GTC AGT TGT GAA AGG CTG CGG CTC AAC CGC AGC ACT GCA GTT GAT ACT GGC GTC CTT GAG TGC GGT TGA GGT ATG TGG AAT TCG TGG TGT AGC GGT GAA ATG CTT AGA TAT CAC GAA GAA CTC CGA TTG CGA AGG CAG CAT ACT AAG CCG CTA CTG ACG TTG AGG CTC GAA AGT GTG GGT ATC AAA CAGG ASV259 BacteroidesTGA GGA ATA TTG GTC AAT GGA CGA GAG TCT GAA CCA GCC AAG TAG CGT GAA GGA TGA AGG TCC TAC GGA TTG TAA ACT TCT TTT ATA AGG GAA TAA AAC CTC CCA CGT GTG GGA GCT TGT ATG TAC CTT ATG AAT AAG CAT CGG CTA ACT CCG TGC CAG CAG CCG CGG TAA TAC GGA GGA TGC GAG CGT TAT CCG GAT TTA TTG GGT TTA AAG GGA GCG CAG ACG GGA TGT TAA GTC AGC TGT GAA AGT TTG CGG CTC AAC CGT AAA ATT GCA GTT GAT ACT GGC GTT CTT GAG TGC AGT TGA GGT GTG CGG AAT TCG TGG TGT AGC GGT GAA ATG CTT AGA TAT CAC GAA GAA CTC CGA TTG CGA AGG CAG CTC ACT AAA CTG TAA CTG ACG TTC ATG CTC GAA AGT GTG GGT ATC AAA CAGG respect to their role in calf diarrhea. Our co-occurrence These potential probiotics may be supplemented in the analysis (Fig. 7) showed that the post-D-M1 might first week after birth to prevent diarrhea, and fiber diets be a driver of diarrhea recovery because of the close [80] or FSBM [71] may improve their efficacy. Clostrid - interaction between its constituent members and the ium_sensu_stricto_1 (ASV48, Fig. 7, Table 2), was nega- inhibitory relationship with D-P-M. Prevotella_2 and tively related with Prevotella_2 (ASV4 and ASV26), Alloprevotella increased their relative abundance in the Alloprevotella (ASV14) and Prevotella_9 (ASV43). All post-diarrhea phase (Fig. 6C), and they dominated the of the four ASVs established the negative relationship post-D-M1 (Fig. 7F), which supports the importance between the post-D-M1 and D-P-M. Thus, Clostrid - of Prevotellaceae in resisting calf diarrhea. In particu- ium_sensu_stricto_1 (ASV48) might be a biomarker of lar, Prevotella_2 (ASV4 and ASV26), Alloprevotella diarrhea risk in the early stage. (ASV14) and Prevotella_9 (ASV43), their sequences were included in Table 2, might be potential probiot- Conclusions ics for preventing diarrhea in early stage. It should be In conclusion, microbial successions of the gut microbiome noted that although most of the constituent members in dairy calves were rapid, and daily sampling is needed to of post-D-M1 were detected before diarrhea (module 3 capture the rapid dynamic gut microbial successions. Pro- and 4 in Fig. 7A), their betweenness and close centrality moting indigenous Prevotella and Muribaculaceae might increased in post-D-M1. This suggests that the interac - be a new strategy to reduce the incidence of diarrhea in tions among different bacteria might play an important neonatal calves and help calves to go through the wean- role in maintaining intestinal homeostasis in the gut. ing transition smoothly. Prevotella_2 (ASV4 and ASV26), Prevotella_9 (ASV43), Alloprevotella (AVS14), unclassified Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 18 of 20 Declarations Muribaculaceae (ASV28 and ASV44), UBA1819 (ASV151), Ruminococcaceae_UCG-005 (ASV254), and Barnesiella Ethics approval and consent to participate (ASV497) might be used as probiotics to reduce or prevent All experimental protocols used in the current study were approved by the Animal Care and Use Committee of Zhejiang University (Protocol number: calf diarrhea; Clostridium_sensu_stricto_1 (ASV48) might ZJU-20262), and all experimental procedures were performed following the be a useful biomarker of diarrhea risk in this large-scale approved protocols. dairy farm locating in subtropical monsoon climate zone Consent for publication with automated milk feeder system. Not applicable. Competing interests Abbreviations The authors declare that they have no competing interests. + + E. coli K99 : Escherichia coli K99 ; BRV: Bovine rotavirus; BCoV: Bovine coro- navirus; ASV: Amplicon sequence variant; Faith’s PD: Faith’s phylogenetic Author details diversity; ANOSIM: Aanalysis of similarity; PCoA: Principal coordinates analysis; Institute of Dairy Science, College of Animal Sciences, Zhejiang University, PERMANOVA: Permutational multivariate analysis of variance; LEfSe: Linear Hangzhou, China. MoE Key Laboratory of Molecular Animal Nutrition, Zheji- discriminant analysis Eec ff t Size; D-M1: Diarrhea module 1; D-M2: Diarrhea ang University, Hangzhou, China. Department of Animal Sciences, The Ohio module 2; ND-M: Non-diarrhea module; pre-D-M: Pre-diarrhea phase- State University, Columbus, OH, USA. associated module; D-P-M: Diarrhea phase-associated module; post-D-M1/2: Post-diarrhea phase-associated modules. Received: 15 March 2022 Accepted: 13 July 2022 Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1186/ s40104- 022- 00758-4. References 1. Cho YI, Yoon KJ. An overview of calf diarrhea - infectious etiology, diagno- Additional file 1: Fig. S1. A schematic showing the experimental design, sis, and intervention. J Vet Sci. 2014;15(1):1–17. https:// doi. org/ 10. 4142/ milk feeding, and fecal sample collection of the two trials. jvs. 2014. 15.1.1. 2. Meganck V, Hoflack G, Opsomer G. Advances in prevention and therapy Additional file 2: Fig. S2. Heatmap of the ASVs associated with diarrheic of neonatal dairy calf diarrhoea: a systematical review with emphasis on status in stage 4 of trial 1. The ASVs were identified based on fold change colostrum management and fluid therapy. Acta Vet Scand. 2014;56(1):75. and analysis using LEfSe or DESeq2. https:// doi. org/ 10. 1186/ s13028- 014- 0075-x. Additional file 3: Table S1. The ASVs in co-occurrence pattern of trial 1 3. Castro JJ, Gomez A, White BA, Mangian HJ, Loften JR, Drackley JK. stage 4. Changes in the intestinal bacterial community, short-chain fatty acid Additional file 4: Fig. S3. Principal coordinates analysis (PCoA) plot of profile, and intestinal development of preweaned Holstein calves. 1. fecal microbiotas among ages in trial 2. The fecal microbiotas differences Eec ff ts of prebiotic supplementation depend on site and age. J Dairy Sci. (P values) between diarrheic and non-diarrheic calves within the same 2016;99(12):9682–702. https:// doi. org/ 10. 3168/ jds. 2016- 11006. age were compared. 4. Windeyer MC, Leslie KE, Godden SM, Hodgins DC, Lissemore KD, LeBlanc SJ. Factors associated with morbidity, mortality, and growth of dairy Additional file 5: Fig. S4. Heatmap of the ASVs that identified to be diar - heifer calves up to 3 months of age. Prev Vet Med. 2014;113(2):231–40. rheic status transition-associated in trial 2. The ASVs were identified based https:// doi. org/ 10. 1016/j. preve tmed. 2013. 10. 019. on fold change and analysis using LEfSe or DESeq2. 5. Aghakeshmiri F, Azizzadeh M, Farzaneh N, Gorjidooz M. Eec ff ts of neona- tal diarrhea and other conditions on subsequent productive and repro- ductive performance of heifer calves. Vet Res Commun. 2017;41(2):107– Acknowledgements 12. https:// doi. org/ 10. 1007/ s11259- 017- 9678-9. We thank the College Experimental Teaching Center, College of Animal 6. Heinrichs AJ, Heinrichs BS, Harel O, Rogers GW, Place NT. A prospective Sciences, Zhejiang University for experimental equipment surport study of calf factors affecting age, body size, and body condition score and assistance. We also thank our colleagues Guangyu Zhang, Lei Cao, at first calving of holstein dairy heifers. J Dairy Sci. 2005;88(8):2828–35. Weibing Shi, Shaobo Yu, Zhibo Liu and Peng Wu at the Institute of https:// doi. org/ 10. 3168/ jds. S0022- 0302(05) 72963-5. Dair y Science, College of Animal Sciences, Zhejiang University for their 7. Kargar S, Roshan M, Ghoreishi SM, Akhlaghi A, Kanani M, Abedi Shams- assistance with sampling. Abadi AR, et al. Extended colostrum feeding for 2 weeks improves growth performance and reduces the susceptibility to diarrhea and pneumonia Authors’ contributions in neonatal Holstein dairy calves. J Dairy Sci. 2020;103(9):8130–42. https:// JW and HC designed the study and drafted the manuscript. HC, YL and KH doi. org/ 10. 3168/ jds. 2020- 18355. conducted the animal trials and collected the fecal samples. HC performed 8. Khan MA, Lee HJ, Lee WS, Kim HS, Kim SB, Ki KS, et al. Pre- and postwean- the DNA extraction and pathogen detection. HC and BY performed the data ing performance of holstein female calves fed milk through step-down analyses and visualization. YZ provided experiment assistance. ZY provided and conventional methods. J Dairy Sci. 2007;90(2):876–85. https:// doi. critical guidance in the interpretation of results and revised the manuscript. org/ 10. 3168/ jds. S0022- 0302(07) 71571-0. JW supervised the study and revised the manuscript. The authors read and 9. Klein-Jöbstl D, Iwersen M, Drillich M. Farm characteristics and calf approved the final manuscript. management practices on dairy farms with and without diarrhea: a case-control study to investigate risk factors for calf diarrhea. J Dairy Sci. Funding 2014;97(8):5110–9. https:// doi. org/ 10. 3168/ jds. 2013- 7695. This work was supported by the National Key Research and Development 10. Caballero S, Kim S, Carter RA, Leiner IM, Sušac B, Miller L, et al. Cooperat- Program of China (2017YFD0500502). ing commensals restore colonization resistance to vancomycin-resistant Enterococcus faecium. Cell Host Microbe. 2017;21(5):592–602. https:// doi. Availability of data and material org/ 10. 1016/j. chom. 2017. 04. 002. The raw sequencing data generated in this study are publicly available in NCBI 11. Jacobson A, Lam L, Rajendram M, Tamburini F, Honeycutt J, Pham T, et al. Sequence Read Archive (http:// www. ncbi. nim. nih. gov/ sra) under the acces- A gut commensal-produced metabolite mediates colonization resistance sion number PRJNA716761. Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 19 of 20 to Salmonella infection. Cell Host Microbe. 2018;24(2):296–307. https:// 30. Pinheiro J, Bates D, DebRoy S, Sarkar D, Heisterkamp S, Van Willigen B, doi. org/ 10. 1016/j. chom. 2018. 07. 002. et al. nlme: Linear and nonlinear mixed effects models. R package version. 12. Kim YG, Sakamoto K, Seo SU, Pickard JM, Gillilland MG, Pudlo NA, et al. 2022;3.1–155. Available from: https:// cran.r- proje ct. org/ packa ge= nlme. Neonatal acquisition of Clostridia species protects against colonization 31. Hothorn T, Bretz F, Westfall P. Simultaneous inference in general paramet- by bacterial pathogens. Science. 2017;356(6335):315–9. https:// doi. org/ ric models. Biom J. 2008;50(3):346–63. https:// doi. org/ 10. 1002/ bimj. 20081 10. 1126/ scien ce. aag20 29. 0425. 13. Wu H, Ma Y, Peng X, Qiu W, Kong L, Ren B, et al. Antibiotic-induced 32. Oksanen J, Blanchet FG, Friendly M, Kindt R. vegan: Community ecology dysbiosis of the rat oral and gut microbiota and resistance to Salmonella. package 2019. Available from: https:// cran.r- proje ct. org/ packa ge= vegan. Arch Oral Biol. 2020;114:104730. https:// doi. org/ 10. 1016/j. archo ralbio. 33. Breiman L. Random Forests. Mach Learn. 2001;45(1):5–32. https:// doi. org/ 2020. 104730.10. 1023/A: 10109 33404 324. 14. Jang JY, Kim S, Kwon MS, Lee J, Yu DH, Song RH, et al. Rotavirus-mediated 34. Liaw A, Wiener M. Classification and regression by randomForest. R News. alteration of gut microbiota and its correlation with physiological char- 2002;2:18–22. Available from: https:// cran.r- proje ct. org/ doc/ Rnews/ acteristics in neonatal calves. J Microbiol. 2019;57(2):113–21. https:// doi. Rnews_ 2002-3. pdf org/ 10. 1007/ s12275- 019- 8549-1. 35. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. 15. Haag LM, Fischer A, Otto B, Plickert R, Kühl AA, Göbel UB, et al. Intestinal Metagenomic biomarker discovery and explanation. Genome Biol. microbiota shifts towards elevated commensal Escherichia coli loads abro- 2011;12(6):R60. https:// doi. org/ 10. 1186/ gb- 2011- 12-6- r60. gate colonization resistance against Campylobacter jejuni in mice. PLoS 36. Love MI, Huber W, Anders S. Moderated estimation of fold change and ONE. 2012;7(5): e35988. https:// doi. org/ 10. 1371/ journ al. pone. 00359 88. dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. 16. Kim YH, Nagata R, Ohtani N, Ichijo T, Ikuta K, Sato S. Eec ff ts of dietary https:// doi. org/ 10. 1186/ s13059- 014- 0550-8. forage and calf starter diet on ruminal pH and bacteria in Holstein calves 37. Friedman J, Alm EJ. Inferring correlation networks from genomic survey during weaning transition. Front Microbiol. 2016;7:1575. https:// doi. org/ data. PLoS Comput Biol. 2012;8(9):e1002687. https:// doi. org/ 10. 1371/ 10. 3389/ fmicb. 2016. 01575.journ al. pcbi. 10026 87. 17. Meale SJ, Chaucheyras-Durand F, Berends H, Guan LL, Steele MA. From 38. Csárdi G, Nepusz T. The igraph software package for complex network pre- to postweaning: Transformation of the young calf ’s gastrointesti- research. Int J Complex Syst. 2006;1695(5):1–9. Available from: https:// nal tract. J Dairy Sci. 2017;100(7):5984–95. https:// doi. org/ 10. 3168/ jds. igraph. org. 2016- 12474. 39. Pons P, Latapy M. Computing communities in large networks using random 18. Karasova D, Crhanova M, Babak V, Jerabek M, Brzobohaty L, Matesova Z, walks. In: Yolum P, Güngör T, Gürgen F, Özturan C, editors. Computer and et al. Development of piglet gut microbiota at the time of weaning influ- Information Sciences - ISCIS 2005. Lecture Notes in Computer Science, vol ences development of postweaning diarrhea - A field study. Res Vet Sci. 3733. Berlin Heidelberg: Springer; 2005. p. 284–93. https:// doi. org/ 10. 1007/ 2021;135:59–65. https:// doi. org/ 10. 1016/j. rvsc. 2020. 12. 022.11569 596_ 31. 19. Ma T, Villot C, Renaud D, Skidmore A, Chevaux E, Steele M, et al. Linking 40. Miura H, Mukai K, Sudo K, Haga S, Suzuki Y, Kobayashi Y, et al. Eec ff t of perturbations to temporal changes in diversity, stability, and composi- trehalose supplementation in milk replacer on the incidence of diarrhea tions of neonatal calf gut microbiota: prediction of diarrhea. ISME J. and fecal microbiota in preweaned calves. J Anim Sci. 2021;99(1):skab012. 2020;14(9):2223–35. https:// doi. org/ 10. 1038/ s41396- 020- 0678-3.https:// doi. org/ 10. 1093/ jas/ skab0 12. 20. Kim HS, Whon TW, Sung H, Jeong YS, Jung ES, Shin NR, et al. Longitudinal 41. Penati M, Sala G, Biscarini F, Boccardo A, Bronzo V, Castiglioni B, et al. evaluation of fecal microbiota transplantation for ameliorating calf diar- Feeding pre-weaned calves with waste milk containing antibiotic rhea and improving growth performance. Nat Commun. 2021;12(1):161. residues is related to a higher incidence of diarrhea and alterations in the https:// doi. org/ 10. 1038/ s41467- 020- 20389-5. fecal microbiota. Front Vet Sci. 2021;8:650150. https:// doi. org/ 10. 3389/ 21. Lesmeister KE, Heinrichs AJ. Eec ff ts of corn processing on growth fvets. 2021. 650150. characteristics, rumen development, and rumen parameters in neonatal 42. Wang D, Du Y, Wang S, You Z, Liu Y. Eec ff ts of sodium humate and dairy calves. J Dairy Sci. 2004;87(10):3439–50. https:// doi. org/ 10. 3168/ jds. glutamine combined supplementation on growth performance, diarrhea S0022- 0302(04) 73479-7. incidence, blood parameters, and intestinal microflora of weaned calves. 22. Zoetendal EG, Heilig HG, Klaassens ES, Booijink CC, Kleerebezem M, Smidt Anim Sci J. 2021;92(1): e13584. https:// doi. org/ 10. 1111/ asj. 13584. H, et al. Isolation of DNA from bacterial samples of the human gastroin- 43. Fernández-Ciganda S, Fraga M, Zunino P. Probiotic Lactobacilli admin- testinal tract. Nat Protoc. 2006;1(2):870–3. https:// doi. org/ 10. 1038/ nprot. istration induces changes in the fecal microbiota of preweaned dairy 2006. 142. calves. Probiotics Antimicrob Proteins. 2021. https:// doi. org/ 10. 1007/ 23. Yu Y, Lee C, Kim J, Hwang S. Group-specific primer and probe sets to s12602- 021- 09834-z. detect methanogenic communities using quantitative real-time poly- 44. Wu Y, Wang L, Luo R, Chen H, Nie C, Niu J, et al. Eec ff t of a multispecies merase chain reaction. Biotechnol Bioeng. 2005;89(6):670–9. https:// doi. probiotic mixture on the growth and incidence of diarrhea, immune org/ 10. 1002/ bit. 20347. function, and fecal microbiota of pre-weaning dairy calves. Front Micro- 24. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. biol. 2021;12:681014. https:// doi. org/ 10. 3389/ fmicb. 2021. 681014. DADA2: High-resolution sample inference from Illumina amplicon data. 45. Meale SJ, Li SC, Azevedo P, Derakhshani H, DeVries TJ, Plaizier JC, et al. Nat Methods. 2016;13(7):581–3. https:// doi. org/ 10. 1038/ nmeth. 3869. Weaning age influences the severity of gastrointestinal microbiome 25 Strube ML. RibDif: can individual species be differentiated by 16S shifts in dairy calves. Sci Rep. 2017;7(1):198. https:// doi. org/ 10. 1038/ sequencing? Bioinform adv. 2021;1(1):vbab020. https:// doi. org/ 10. 1093/ s41598- 017- 00223-7. bioadv/ vbab0 20. 46. Amin N, Schwarzkopf S, Kinoshita A, Tröscher-Mußotter J, Dänicke S, Cam- 26. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA arinha-Silva A, et al. Evolution of rumen and oral microbiota in calves is ribosomal RNA gene database project: improved data processing and influenced by age and time of weaning. Anim Microbiome. 2021;3(1):31. web-based tools. Nucleic Acids Res. 2013;41:D590–6. https:// doi. org/ 10. https:// doi. org/ 10. 1186/ s42523- 021- 00095-3. 1093/ nar/ gks12 19. 47. Kim ET, Lee SJ, Kim TY, Lee HG, Atikur RM, Gu BH, et al. Dynamic changes 27. Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid in fecal microbial communities of neonatal dairy calves by aging and assignment of rRNA sequences into the new bacterial taxonomy. Appl diarrhea. Animals (Basel). 2021;11(4):1113. https:// doi. org/ 10. 3390/ ani11 Environ Microbiol. 2007;73(16):5261–7. https:// doi. org/ 10. 1128/ AEM. 041113. 00062- 07. 48. Klein-Jöbstl D, Quijada NM, Dzieciol M, Feldbacher B, Wagner M, Drillich 28. McMurdie PJ, Holmes S. phyloseq: an R package for reproducible M, et al. Microbiota of newborn calves and their mothers reveals possible interactive analysis and graphics of microbiome census data. PLoS ONE. transfer routes for newborn calves’ gastrointestinal microbiota. PLoS ONE. 2013;8(4):e61217. https:// doi. org/ 10. 1371/ journ al. pone. 00612 17. 2019;14(8):e0220554. https:// doi. org/ 10. 1371/ journ al. pone. 02205 54. 29 Nam HM, Srinivasan V, Gillespie BE, Murinda SE, Oliver SP. Application 49. Mayer M, Abenthum A, Matthes JM, Kleeberger D, Ege MJ, Holzel C, of SYBR green real-time PCR assay for specific detection of Salmo - et al. Development and genetic influence of the rectal bacterial flora of nella spp. in dairy farm environmental samples. Int J Food Microbiol. newborn calves. Vet Microbiol. 2012;161(1–2):179–85. https:// doi. org/ 10. 2005;102(2):161–71. https:// doi. org/ 10. 1016/j. ijfoo dmicro. 2004. 12. 020.1016/j. vetmic. 2012. 07. 023. Chen et al. Journal of Animal Science and Biotechnology (2022) 13:132 Page 20 of 20 50 Podschun R, Ullmann U. Klebsiella spp. as nosocomial pathogens: epi- 69. Sibai M, Altuntaş E, Yıldırım B, Öztürk G, Yıldırım S, Demircan T. Micro- demiology, taxonomy, typing methods, and pathogenicity factors. Clin biome and longevity: High abundance of longevity-linked muribacu- Microbiol Rev. 1998;11(4):589–603. https:// doi. org/ 10. 1128/ CMR. 11.4. 589. laceae in the gut of the long-living rodent Spalax leucodon. OMICS. 51. Broberg CA, Palacios M, Miller VL. Klebsiella: a long way to go towards 2020;24(10):592–601. https:// doi. org/ 10. 1089/ omi. 2020. 0116. understanding this enigmatic jet-setter. F1000Prime Rep. 2014;6:64. 70. Ubeda C, Bucci V, Caballero S, Djukovic A, Toussaint NC, Equinda https:// doi. org/ 10. 12703/ P6- 64. M, et al. Intestinal microbiota containing Barnesiella species cures 52. Glantz PJ, Jacks TM. A bacteriological and serological study of vancomycin-resistant Enterococcus faecium colonization. Infect Immun. experimental escherichia coli infection of calves. Can J Comp Med. 2013;81(3):965–73. https:// doi. org/ 10. 1128/ IAI. 01197- 12. 1969;33(2):128–33. 71. Weese JS, Holcombe SJ, Embertson RM, Kurtz KA, Roessner HA, Jalali M, 53. Komatsu T, Yoshida E, Shigenaga A, Yasuie N, Uchiyama S, Takamura Y, et al. Changes in the faecal microbiota of mares precede the develop- et al. Fatal suppurative meningoencephalitis caused by Klebsiella pneu- ment of post partum colic. Equine Vet J. 2015;47(6):641–9. https:// doi. moniae in two calves. J Vet Med Sci. 2021;83(7):1113–9. https:// doi. org/ 10. org/ 10. 1111/ evj. 12361. 1292/ jvms. 21- 0166. 72. Ichikawa-Seki M, Motooka D, Kinami A, Murakoshi F, Takahashi Y, Aita 54. Aslan V, Maden M, Erganis O, Birdane FM, Corlu M. Clinical efficacy of J, et al. Specific increase of Fusobacterium in the faecal microbiota florfenicol in the treatment of calf respiratory tract infections. Vet Q. of neonatal calves infected with Cryptosporidium parvum. Sci Rep. 2002;24(1):35–9. https:// doi. org/ 10. 1080/ 01652 176. 2002. 96951 22. 2019;9(1):12517. https:// doi. org/ 10. 1038/ s41598- 019- 48969-6. 55. Brisson-Noël A, Trieu-Cuot P, Courvalin P. Mechanism of action of spiramy- 73. Kim MH, Yun CH, Kim HS, Kim JH, Kang SJ, Lee CH, et al. Eec ff ts of cin and other macrolides. J Antimicrob Chemother. 1988;22(Suppl. fermented soybean meal on growth performance, diarrheal incidence B):13–23. https:// doi. org/ 10. 1093/ jac/ 22. suppl ement_b. 13. and immune-response of neonatal calves. Anim Sci J. 2010;81(4):475–81. 56. Roland S, Ferone R, Harvey RJ, Styles VL, Morrison RW. The characteristics https:// doi. org/ 10. 1111/j. 1740- 0929. 2010. 00760.x. and significance of sulfonamides as substrates for Escherichia coli dihy- 74. Feizi LK, Zad SS, Jalali SAH, Rafiee H, Jazi MB, Sadeghi K, et al. Fermented dropteroate synthase. J Biol Chem. 1979;254(20):10337–45. https:// doi. soybean meal affects the ruminal fermentation and the abundance of org/ 10. 1016/ s0021- 9258(19) 86714-5. selected bacterial species in Holstein calves: a multilevel analysis. Sci Rep. 57. Akhter S, Ansari MS, Andrabi S, Ullah N, Qayyum M. Eec ff t of antibiot - 2020;10(1):12062. https:// doi. org/ 10. 1038/ s41598- 020- 68778-6. ics in extender on bacterial and spermatozoal quality of cooled buffalo 75. Poudel P, Froehlich K, Casper DP, St-Pierre B. Feeding essential oils to (Bubalus bubalis) bull semen. Reprod Domest Anim. 2008;43(3):272–8. neonatal Holstein dairy calves results in increased ruminal Prevotel- https:// doi. org/ 10. 1111/j. 1439- 0531. 2007. 00890.x. laceae abundance and propionate concentrations. Microorganisms. 58. Shah HN, Collins MD. Proposal to restrict the genus Bacteroides (Castellani 2019;7(5):120. https:// doi. org/ 10. 3390/ micro organ isms7 050120. and Chalmers) to Bacteroides fragilis and closely related species. Int J Syst 76. Campolina JP, Coelho SG, Belli AL, Machado FS, Pereira LGR, Tomich TR, Bacteriol. 1989;39(1):85–7. https:// doi. org/ 10. 1099/ 00207 713- 39-1- 85. et al. Eec ff ts of a blend of essential oils in milk replacer on performance, 59. Willems A, Collins MD. 16S rRNA gene similarities indicate that Hallella rumen fermentation, blood parameters, and health scores of dairy heifers. seregens (Moore and Moore) and Mitsuokella dentalis (Haapsalo et al.) are PLoS ONE. 2021;16(3):e0231068. https:// doi. org/ 10. 1371/ journ al. pone. genealogically highly related and are members of the genus Prevo-02310 68. tella: emended description of the genus Prevotella (Shah and Collins) 77. Tap J, Derrien M, Tornblom H, Brazeilles R, Cools-Portier S, Dore J, et al. and description of Prevotella dentalis comb. nov. Int J Syst Bacteriol. Identification of an intestinal microbiota signature associated with sever - 1995;45(4):832–6. https:// doi. org/ 10. 1099/ 00207 713- 45-4- 832. ity of Irritable Bowel Syndrome. Gastroenterology. 2017;152(1):111-23.e8. 60. Trachsel J, Humphrey S, Allen HK. Butyricicoccus porcorum sp. nov., a https:// doi. org/ 10. 1053/j. gastro. 2016. 09. 049. butyrate-producing bacterium from swine intestinal tract. Int J Syst Evol 78. Wang J, Ji H, Wang S, Liu H, Zhang W, Zhang D, et al. Probiotic Lactobacil- Microbiol. 2018;68(5):1737–42. https:// doi. org/ 10. 1099/ ijsem.0. 002738. lus plantarum promotes intestinal barrier function by strengthening the 61. Morgan XC, Tickle TL, Sokol H, Gevers D, Devaney KL, Ward DV, et al. epithelium and modulating gut microbiota. Front Microbiol. 2018;9:1953. Dysfunction of the intestinal microbiome in inflammatory bowel disease https:// doi. org/ 10. 3389/ fmicb. 2018. 01953. and treatment. Genome Biol. 2012;13(9):R79. https:// doi. org/ 10. 1186/ 79. Zambruni M, Ochoa TJ, Somasunderam A, Cabada MM, Morales ML, gb- 2012- 13-9- r79. Mitreva M, et al. Stunting is preceded by intestinal mucosal damage and 62. Jonkers D, Penders J, Masclee A, Pierik M. Probiotics in the management microbiome changes and is associated with systemic inflammation in of inflammatory bowel disease: a systematic review of intervention stud- a cohort of Peruvian infants. Am J Trop Med Hyg. 2019;101(5):1009–17. ies in adult patients. Drugs. 2012;72(6):803–23. https:// doi. org/ 10. 2165/ https:// doi. org/ 10. 4269/ ajtmh. 18- 0975. 11632 710- 00000 0000- 00000. 80. Wu GD, Chen J, Hoffmann C, Bittinger K, Chen YY, Keilbaugh SA, et al. 63. Abe F, Ishibashi N, Shimamura S. Eec ff t of administration of bifidobac- Linking long-term dietary patterns with gut microbial enterotypes. Sci- teria and lactic acid bacteria to newborn calves and piglets. J Dairy Sci. ence. 2011;334(6052):105–8. https:// doi. org/ 10. 1126/ scien ce. 12083 44. 1995;78(12):2838–46. https:// doi. org/ 10. 3168/ jds. S0022- 0302(95) 76914-4. 64. Foditsch C, Pereira RV, Ganda EK, Gomez MS, Marques EC, Santin T, et al. Oral administration of Faecalibacterium prausnitzii decreased the incidence of severe diarrhea and related mortality rate and increased weight gain in preweaned dairy heifers. PLoS ONE. 2015;10(12):e0145485. https:// doi. org/ 10. 1371/ journ al. pone. 01454 85. 65. Fan P, Kim M, Liu G, Zhai Y, Liu T, Driver JD, et al. The gut microbiota of newborn calves and influence of potential probiotics on reducing diar - Re Read ady y to to submit y submit your our re researc search h ? Choose BMC and benefit fr ? Choose BMC and benefit from om: : rheic disease by inhibition of pathogen colonization. Front Microbiol. 2021;12:772863. https:// doi. org/ 10. 3389/ fmicb. 2021. 772863. fast, convenient online submission 66. Carr A, Diener C, Baliga NS, Gibbons SM. Use and abuse of correlation thorough peer review by experienced researchers in your ﬁeld analyses in microbial ecology. ISME J. 2019;13(11):2647–55. https:// doi. org/ 10. 1038/ s41396- 019- 0459-z. rapid publication on acceptance 67. Lagkouvardos I, Lesker TR, Hitch TCA, Galvez EJC, Smit N, Neuhaus K, et al. support for research data, including large and complex data types Sequence and cultivation study of Muribaculaceae reveals novel species, • gold Open Access which fosters wider collaboration and increased citations host preference, and functional potential of this yet undescribed family. Microbiome. 2019;7(1):28. https:// doi. org/ 10. 1186/ s40168- 019- 0637-2. maximum visibility for your research: over 100M website views per year 68. Smith BJ, Miller RA, Schmidt TM. Muribaculaceae genomes assembled from metagenomes suggest genetic drivers of differential response to At BMC, research is always in progress. acarbose treatment in mice. mSphere. 2021;6(6):e0085121. https:// doi. Learn more biomedcentral.com/submissions org/ 10. 1128/ msphe re. 00851- 21.

Journal

Journal of Animal Science and Biotechnology – Springer Journals

Published: Oct 28, 2022

Keywords: Calf diarrhea; Co-occurrence pattern; Dynamic development; Fecal microbiota

Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Fecal microbiota dynamics and its relationship to diarrhea and health in dairy calves

Fecal microbiota dynamics and its relationship to diarrhea and health in dairy calves

Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Fecal microbiota dynamics and its relationship to diarrhea and health in dairy calves

Fecal microbiota dynamics and its relationship to diarrhea and health in dairy calves

References (96)

Abstract

Journal

Recommended Articles

There are no references for this article.

Our policy towards the use of cookies