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Background Ruminants can utilize urea as a dietary nitrogen source owing to their ability to recycle urea-N back to the rumen where numerous ureolytic bacteria hydrolyze urea into ammonia, which is used by numerous bacteria as their nitrogen source. Rumen ureolytic bacteria are the key microbes making ruminants the only type of animals independent of pre-formed amino acids for survival, thus having attracted much research interest. Sequencing-based studies have helped gain new insights into ruminal ureolytic bacterial diversity, but only a limited number of ureolytic bacteria have been isolated into pure cultures or studied, hindering the understanding of ureolytic bacteria with respect to their metabolism, physiology, and ecology, all of which are required to effectively improve urea-N utilization efficiency. Results We established and used an integrated approach, which include urease gene (ureC) guided enrichment plus in situ agarose microsphere embedding and cultivation under rumen-simulating conditions, to isolate ureolytic bacteria from the rumen microbiome. We optimized the dilutions of the rumen microbiome during the enrichment, single-cell embedding, and then in situ cultivation of microsphere-embedded bacteria using dialysis bags placed in rumen fluid. Metabonomic analysis revealed that the dialysis bags had a fermentation profile very similar to the simulated rumen fermentation. In total, we isolated 404 unique strains of bacteria, of which 52 strains were selected for genomic sequencing. Genomic analyses revealed that 28 strains, which were classified into 12 species, contained urease genes. All these ureolytic bacteria represent new species ever identified in the rumen and represented the most abundant ureolytic species. Compared to all the previously isolated ruminal ureolytic species combined, the newly isolated ureolytic bacteria increased the number of genotypically and phenotypically characterized ureolytic species by 34.38% and 45.83%, respectively. These isolated strains have unique genes compared to the known ureolytic strains of the same species indicating their new metabolic functions, especially in energy and nitrogen metabolism. All the ureolytic species were ubiquitous in the rumen of six different species of ruminants and were correlated to dietary urea metabolism in the rumen and milk protein production. We discovered five different organizations of urease gene clusters among the new isolates, and they had varied approaches to hydrolyze urea. The key amino acid residues of the UreC protein that potentially plays critical regulatory roles in urease activation were also identified. Conclusions We established an integrated methodology for the efficient isolation of ureolytic bacteria, which expanded the biological resource of crucial ureolytic bacteria from the rumen. These isolates play a vital role in the incorporation of dietary nitrogen into bacterial biomass and hence contribute to ruminant growth and productivity. Moreover, this methodology can enable efficient isolation and cultivation of other bacteria of interest in the environment and help bridge the knowledge gap between genotypes and phenotypes of uncultured bacteria.

IntroductionUrea is an important non-protein nitrogen source for ruminants. In the rumen, ureolytic bacteria play critical roles in urea-nitrogen metabolism, however, a few ureolytic strains have been isolated and genomically sequenced. The purpose of this study was to isolate a novel ureolytic bacterial strain from cattle rumen and characterize its genome and function.MethodsThe ureolytic bacterium was isolated using an anaerobic medium with urea and phenol red as a screening indicator from the rumen fluid of dairy cattle. The genome of isolates was sequenced, assembled, annotated, and comparatively analyzed. The pan-genome analysis was performed using IPGA and the biochemical activity was also analyzed by test kits.ResultsA gram-positive ureolytic strain was isolated. Its genome had a length of 4.52 Mbp and predicted genes of 4223. The 16S rRNA gene and genome GTDB-Tk taxonomic annotation showed that it was a novel strain of Enterobacter hormaechei, and it was named E. hormaechei Z129. The pan-genome analysis showed that Z129 had the highest identity to E. hormaechei ATCC 49162 with a genome average nucleotide identity of 98.69% and possessed 238 unique genes. Strain Z129 was the first E. hormaechei strain isolated from the rumen as we know. The functional annotation of the Z129 genome showed genes related to urea metabolism, including urea transport (urtA-urtE), nickel ion transport (ureJ, tonB, nixA, exbB, exbD, and rcnA), urease activation (ureA-ureG) and ammonia assimilation (gdhA, glnA, glnB, glnE, glnL, glsA, gltB, and gltD) were present. Genes involved in carbohydrate metabolism were also present, including starch hydrolysis (amyE), cellulose hydrolysis (celB and bglX), xylose transport (xylF-xylH) and glycolysis (pgi, pgk, fbaA, eno, pfkA, gap, pyk, gpmL). Biochemical activity analysis showed that Z129 was positive for alkaline phosphatase, leucine arylamidase, acid phosphatase, naphthol-AS-BI-phosphohydrolase, α-glucosidase, β-glucosidase, and pyrrolidone arylaminase, and had the ability to use D-ribose, L-arabinose, and D-lactose. Urea-nitrogen hydrolysis rate of Z129 reached 55.37% at 48 h of incubation.DiscussionTherefore, the isolated novel ureolytic strain E. hormaechei Z129 had diverse nitrogen and carbon metabolisms, and is a preferred model to study the urea hydrolysis mechanism in the rumen.

Biochanin A (BCA), an isoflavone phytoestrogen, is a secondary metabolite produced mainly in leguminous plants. The objective of this study was to evaluate the effect of BCA on lactation performance, nitrogen metabolism, and the health of dairy goat. Thirty mid-lactation Saanen dairy goats were divided into three groups randomly: control, 2 g/d BCA group, and 6 g/d BCA group. After 36 days of feeding, 30 dairy goats were transferred to individual metabolic cages. Subsequently, milk yield, feed intake, total feces, and urine excretion were recorded and samples were collected continuously for 3 days. Blood and ruminal fluid samples were collected over the subsequent 4 days. Milk yield, milk protein, fat content, and the feed conversion ratio of dairy goat were significantly increased by the BCA treatment. The levels of serum 17β-estradiol, growth hormone, insulin-like growth factor 1, glutathione peroxidase activity, and total antioxidant capacity were also increased significantly by BCA, indicating that BCA enhanced the antioxidant capacity of dairy goat. Amino acid degradation was significantly inhibited, while the ammonia nitrogen content was reduced significantly by BCA. Total volatile fatty acids was significantly increased by BCA supplementation. In addition, the relative abundance of Verrucomicrobiota was decreased significantly. However, the growth of nitrogen metabolism and cellulolytic bacteria was significantly increased under BCA treatment, including Prevotella sp., Treponema sp., Ruminococcus flavefaciens, and Ruminobacter amylophilus. In conclusion, supplementation with BCA improved the milk production performance, nitrogen metabolism, rumen fermentation and antioxidant capacity, and regulated the rumen microbiome of dairy goat.

Additional file 8: Supplementary Fig. 3. Circular diagrams of the core and unique gene clusters of the ureolytic isolates of individual species.

Additional file 9: Supplementary Fig. 4. Pan-genome profiles of ureolytic isolates. Pan-genome (blue) and core-genome (red) sizes were predicted based on all the strains of individual species.

Additional file 6: Supplementary Fig. 1. Optimization of isolation methodology of functional gene-guided enrichment plus in situ microsphere cultivation for ureolytic bacteria. A. A plot of ureC-positive wells vs. dilution; B. Distribution of agarose microsphere sizes (diameter); C. Percentage of agarose microspheres with embedded bacteria at different dilutions; D. Percentage of agarose microspheres with embedded single bacteria at different dilutions; E. Percentage of agarose microspheres with PCR-detected embedded bacteria with increasing incubation time; F. Percentage of agarose microsphere with PCR-detected single bacteria with increasing incubation time.

Additional file 10: Supplementary Fig. 5. Urease gene clusters identified among the ureolytic isolates.

Additional file 7: Supplementary Fig. 2. Microbial metabolites inside and outside of the dialysis bags. A. The identified metabolites inside the dialysis bags; B. A heatmap of the metabolite profiles at each incubation time both inside and outside of the dialysis bags; C. Correlation of metabolite profiles between insides and outside the dialysis bags.

Additional file 11: Supplementary Fig. 6. Urea hydrolysis rates (A) and urea-utilization rates of each ureolytic isolate per unit of culture optical density (OD, n = 3).

Additional file 12: Supplementary Fig. 7. Sequence alignment of UreC of type I urease gene cluster.

Understanding the signaling pathway regulatory mechanisms in type II alveolar epithelial (ATII) cells, the progenitor cells responsible for proliferating and regenerating type I alveolar epithelial (ATI) and ATII cells, in Tibetan pigs is beneficial for exploring methods of preventing and repairing cellular damage during hypoxia. We simulated a hypoxic environment (2% O2) for culture ATII cells of Tibetan pigs and Landrace pigs, with cells cultured under normoxic conditions (21% O2) as a control group, and performed integrated analysis of circular RNA (circRNA)–microRNA (miRNA)–messenger RNA (mRNA) regulatory axes by whole-transcriptome sequencing. Functional enrichment analysis indicated that the source genes of the differential expressed circRNAs (DEcircRNAs) were primarily involved in cell proliferation, cellular processes, and cell killing. A series of DEcircRNAs were derived from inhibitors of apoptosis proteins and led to a key autonomous effect as modulators of cell repair in Tibetan pigs under hypoxia. The significant higher expression of COL5A1 in TL groups may inhibited apoptosis of ATII cells in Tibetan pigs under lower oxygen concentration, and may lead their better survive in the hypoxia environment. In addition, a competing endogenous RNA (ceRNA) network of functional interactions was constructed that included novel_circ_000898-ssc-miR-199a-5p-CAV1 and novel_circ_000898-ssc-miR-378-BMP2, based on the node genes ssc-miR-199a-5p and ssc-miR-378, which may regulate multiple miRNAs and mRNAs that mediate endoplasmic reticulum (ER) stress-induced apoptosis and inflammation and attenuate hypoxia-induced injury in ATII cells under hypoxic conditions. These results broaden our knowledge of circRNAs, miRNAs, and mRNAs associated with hypoxia and provide new insights into the hypoxic response of ATII cells in Tibetan pigs.

Additional file 7: Figure S6. Weighted (A) and unweighted (B) UniFrac distance-based beta diversity of the cow rumen microbial communities revealed by DNBSEQ and NovaSeq sequencing platforms.

Additional file 5: Figure S4. Comparison of the amplicon sequencing results of three typical ecological systems between DNBSEQ and NovaSeq sequencing platforms. (A) Evaluation of the reproducibility of the amplicon sequencing results of DNBSEQ and NovaSeq sequencing platform in revealing the accumulated relative abundance of microbes of abundant, moderate and rate taxa subcommunity from ecosystems with various complexity (DNB All, DNB 2, DNB 3 denotes number of all unique OTUs detected by DNBSEQ platform, consistently detected by at least 2 technical replicates, and consistently detected by all three technical replicates, respectively. Similar naming scheme was used for the NovaSeq platform). (B) Cross-verification of the OTUs identified by amplicon sequencing on DNBSEQ and NovaSeq platforms and shotgun sequencing results of the same set of mangrove sediment samples. (C) Cross-verification of the OTUs identified by amplicon sequencing on DNBSEQ and NovaSeq platforms and shotgun sequencing results of the same set of mice gut samples.

Additional file 2: Figure S1. Schematic presentation of the design and flow of this study. This study was designed to evaluate the potential harm of false positives introduced by index misassignment during the sequencing to the interpretation of metabarcoding or here amplicon sequencing studies. In part 1, mock communities with known microbial composition were used to confirm the presence of index misassignment introduced false positives and the rate of this misassignment on different sequencing platform; In part 2, samples from several typical ecosystems with various complexity were sequenced to test how index misassignment could affect the estimation of community diversity for different samples; In part 3, a case study using a set of cow rumen samples was conducted to evaluate how index misassignment caused false positives could affect the interpretation of microbial ecological roles and community assembly mechanisms.

Additional file 4: Figure S3. Venn diagrams showing the shared OTUs among distinct samples sequenced within the same batch of sequencing run on the Novaseq platform. The large number of shared OTUs among distinct samples (including two customized communities) were likely sample wise cross contaminations caused by index misassignment.

Additional file 6: Figure S5. Weighted (A) and unweighted (B) UniFrac distance-based clustering of the amplicon sequencing results revealed by DNBSEQ and NovaSeq sequencing platforms for samples from the three typical ecosystems.

Additional file 3: Figure S2. Comparison of shared fraction of OTUs by different sequencing platforms. UpSet graph showing the number of shared OTUs among the technical triplicates of DNBSEQ and NovaSeq sequencing platforms for customized mock community with four (A) or seven (B) microbes used in the current study. And heatmap showing the phylum rank relative abundances revealed by different sequencing platforms for the commercial mock (C) and customized mock communities with four (D) or seven (E) bacteria.

Additional file 8: Figure S7. Comparison of the results using unoise3 and DADA2 denoising algorithms. The accumulated OTU relative abundances in each phylum based on unoise3 and DADA2 were graphed as bar-chart side-by-side for all the mock communities and samples from three typical ecosystems.

Objective: Evidence from previous reports indicates that pig domestication in East Asia mainly occurred in the Mekong region and the middle and downstream regions of the Yangtze River. Further research identified two new origin centers for domestic pigs in the Tibetan Plateau and the islands of Southeast Asia. However, due to the small sample size of Tibetan pigs, details of the origin and spread of Tibetan pigs has not yet been established.Methods: We analyzed mitochondrial DNA control region (D-loop) variation in 1,201 individuals from nine Tibetan pig populations across five provinces. Comprehensive Tibetan pig samples were taken to perform the most detailed analysis of Tibetan pigs to date.Results: The result indicate that Rkaze pigs had the lowest level of diversity, while Changdu pigs had the highest diversity. Interestingly, these two populations were both in the Tibetan Plateau area. If we calculate diversity in terms of each province, the Tibetan Plateau area had the lowest diversity, while the Chinese province of Gansu had the highest diversity. Diversity gradient analysis of major haplotypes suggested three domestication centers of Tibetan pigs in the Tibetan Plateau and the Chinese provinces of Gansu and Yunnan.Conclusion: We found two new domestication centers for Tibetan pigs. One is in the Chinese province of Gansu, which lies in the upstream region of the Yellow River, and the other is in the Chinese province of Yunnan.

Additional file 1. Fig. S1. Distribution of sampling location. T1 represented the yaks from Maqu County in the Gannan Tibetan Autonomous Prefecture of Gansu Province at an altitude of 3400 m, T2 represented the yaks from Bange County, Linzhou County and Dangxiong County in the Tibetan Autonomous Region at an altitude of 4200 m, T3 represented the yaks from Anduo County in the Tibetan Autonomous Region at an altitude of 5000 m, and CON represented the Zaosheng cattle from Ningxian County in Gansu Province at an altitude of 1500 m. Fig. S2. (A) Expression levels of circRNAs. The lines of the whiskers in the box represent the medians. (B) Density distribution of circRNAs. (C) Percentage of expression levels of circRNAs in each group. Table S1. Characteristics of samples. Table S2. Summary of quality and statistics of the mRNA data. Table S3. Summary of quality and statistics of the miRNA data. Table S4. Distribution of known and novel miRNAs in each sample. Table S5. Classification of mRNAs, miRNAs and circRNAs. Table S6. Primers designed for qRT-PCR validation of candidate circRNAs and mRNAs.