Enhancing milk quality and modulating rectal microbiota of dairy goats in starch-rich diet: the role of bile acid supplementation.

BACKGROUND: Diets rich in starch have been shown to increase a risk of reducing milk fat content in dairy goats. While bile acids (BAs) have been used as a lipid emulsifier in monogastric and aquatic animals, their effect on ruminants is not well understood. This study aimed to investigate the impact of BAs supplementation on various aspects of dairy goat physiology, including milk composition, rumen fermentation, gut microbiota, and BA metabolism. RESULTS: We randomly divided eighteen healthy primiparity lactating dairy goats (days in milk = 100 ± 6 d) into two groups and supplemented them with 0 or 4 g/d of BAs undergoing 5 weeks of feeding on a starch-rich diet. The results showed that BAs supplementation positively influenced milk yield and improved the quality of fatty acids in goat milk. BAs supplementation led to a reduction in saturated fatty acids (C16:0) and an increase in monounsaturated fatty acids (cis-9 C18:1), resulting in a healthier milk fatty acid profile. We observed a significant increase in plasma total bile acid concentration while the proportion of rumen short-chain fatty acids was not affected. Furthermore, BAs supplementation induced significant changes in the composition of the gut microbiota, favoring the enrichment of specific bacterial groups and altering the balance of microbial populations. Correlation analysis revealed associations between specific bacterial groups (Bacillus and Christensenellaceae R-7 group) and BA types, suggesting a role for the gut microbiota in BA metabolism. Functional prediction analysis revealed notable changes in pathways associated with lipid metabolism, suggesting that BAs supplementation has the potential to modulate lipid-related processes. CONCLUSION: These findings highlight the potential benefits of BAs supplementation in enhancing milk production, improving milk quality, and influencing metabolic pathways in dairy goats. Further research is warranted to elucidate the underlying mechanisms and explore the broader implications of these findings.

Multi-omics reveals that the host-microbiome metabolism crosstalk of differential rumen bacterial enterotypes can regulate the milk protein synthesis of dairy cows.

BACKGROUND: Dairy cows' lactation performance is the outcome of the crosstalk between ruminal microbial metabolism and host metabolism. However, it is still unclear to what extent the rumen microbiome and its metabolites, as well as the host metabolism, contribute to regulating the milk protein yield (MPY). METHODS: The rumen fluid, serum and milk of 12 Holstein cows with the same diet (45% coarseness ratio), parity (2-3 fetuses) and lactation days (120-150 d) were used for the microbiome and metabolome analysis. Rumen metabolism (rumen metabolome) and host metabolism (blood and milk metabolome) were connected using a weighted gene co-expression network (WGCNA) and the structural equation model (SEM) analyses. RESULTS: Two different ruminal enterotypes, with abundant Prevotella and Ruminococcus, were identified as type1 and type2. Of these, a higher MPY was found in cows with ruminal type2. Interestingly, [Ruminococcus] gauvreauii group and norank_f_Ruminococcaceae (the differential bacteria) were the hub genera of the network. In addition, differential ruminal, serum and milk metabolome between enterotypes were identified, where the cows with type2 had higher L-tyrosine of rumen, ornithine and L-tryptophan of serum, and tetrahydroneopterin, palmitoyl-L-carnitine, S-lactoylglutathione of milk, which could provide more energy and substrate for MPY. Further, based on the identified modules of ruminal microbiome, as well as ruminal serum and milk metabolome using WGCNA, the SEM analysis indicated that the key ruminal microbial module1, which contains the hub genera of the network ([Ruminococcus] gauvreauii group and norank_f_Ruminococcaceae) and high abundance of bacteria (Prevotella and Ruminococcus), could regulate the MPY by module7 of rumen, module2 of blood, and module7 of milk, which contained L-tyrosine and L-tryptophan. Therefore, in order to more clearly reveal the process of rumen bacterial regulation of MPY, we established the path of SEM based on the L-tyrosine, L-tryptophan and related components. The SEM based on the metabolites suggested that [Ruminococcus] gauvreauii group could inhibit the energy supply of serum tryptophan to MPY by milk S-lactoylglutathione, which could enhance pyruvate metabolism. Norank_f_Ruminococcaceae could increase the ruminal L-tyrosine, which could provide the substrate for MPY. CONCLUSION: Our results indicated that the represented enterotype genera of Prevotella and Ruminococcus, and the hub genera of [Ruminococcus] gauvreauii group and norank_f_Ruminococcaceae could regulate milk protein synthesis by affecting the ruminal L-tyrosine and L-tryptophan. Moreover, the combined analysis of enterotype, WGCNA and SEM could be used to connect rumen microbial metabolism with host metabolism, which provides a fundamental understanding of the crosstalk between host and microorganisms in regulating the synthesis of milk composition.

A convolutional neural network model for survival prediction based on prognosis-related cascaded Wx feature selection.

Great advances in deep learning have provided effective solutions for prediction tasks in the biomedical field. However, accurate prognosis prediction using cancer genomics data remains challenging due to the severe overfitting problem caused by curse of dimensionality inherent to high-throughput sequencing data. Moreover, there are unique challenges to perform survival analysis, arising from the difficulty in utilizing censored samples whose events of interest are not observed. Convolutional neural network (CNN) models provide us the opportunity to extract meaningful hierarchical features to characterize cancer subtype and prognosis outcomes. On the other hand, feature selection can mitigate overfitting and reduce subsequent model training computation burden by screening out significant genes from redundant genes. To accomplish model simplification, we developed a concise and efficient survival analysis model, named CNN-Cox model, which combines a special CNN framework with prognosis-related feature selection cascaded Wx, with the advantage of less computation demand utilizing light training parameters. Experiment results show that CNN-Cox model achieved consistent higher C-index values and better survival prediction performance across seven cancer type datasets in The Cancer Genome Atlas cohort, including bladder carcinoma, head and neck squamous cell carcinoma, kidney renal cell carcinoma, brain low-grade glioma, lung adenocarcinoma (LUAD), lung squamous cell carcinoma, and skin cutaneous melanoma, compared with the existing state-of-the-art survival analysis methods. As an illustration of model interpretation, we examined potential prognostic gene signatures of LUAD dataset using the proposed CNN-Cox model. We conducted protein-protein interaction network analysis to identify potential prognostic genes and further analyzed the biological function of 13 hub genes, including ANLN, RACGAP1, KIF4A, KIF20A, KIF14, ASPM, CDK1, SPC25, NCAPG, MKI67, HJURP, EXO1, HMMR, whose high expression is significantly associated with poor survival of LUAD patients. These findings confirmed that CNN-Cox model is effective in extracting not only prognosis factors but also biologically meaningful gene features. The codes are available at the GitHub website: https://github.com/wangwangCCChen/CNN-Cox .

Long-term and combined effects of N-[2-(nitrooxy)ethyl]-3-pyridinecarboxamide and fumaric acid on methane production, rumen fermentation, and lactation performance in dairy goats.

BACKGROUND: In recent years, nitrooxy compounds have been identified as promising inhibitors of methanogenesis in ruminants. However, when animals receive a nitrooxy compound, a high portion of the spared hydrogen is eructated as gas, which partly offsets the energy savings of CH4 mitigation. The objective of the present study was to evaluate the long-term and combined effects of supplementation with N-[2-(nitrooxy)ethyl]-3-pyridinecarboxamide (NPD), a methanogenesis inhibitor, and fumaric acid (FUM), a hydrogen sink, on enteric CH4 production, rumen fermentation, bacterial populations, apparent nutrient digestibility, and lactation performance of dairy goats. RESULTS: Twenty-four primiparous dairy goats were used in a randomized complete block design with a 2 × 2 factorial arrangement of treatments: supplementation without or with FUM (32 g/d) or NPD (0.5 g/d). All samples were collected every 3 weeks during a 12-week feeding experiment. Both FUM and NPD supplementation persistently inhibited CH4 yield (L/kg DMI, by 18.8% and 18.1%, respectively) without negative influence on DMI or apparent nutrient digestibility. When supplemented in combination, no additive CH4 suppression was observed. FUM showed greater responses in increasing the molar proportion of propionate when supplemented with NPD than supplemented alone (by 10.2% vs. 4.4%). The rumen microbiota structure in the animals receiving FUM was different from that of the other animals, particularly changed the structure of phylum Firmicutes. Daily milk production and serum total antioxidant capacity were improved by NPD, but the contents of milk fat and protein were decreased, probably due to the bioactivity of absorbed NPD on body metabolism. CONCLUSIONS: Supplementing NPD and FUM in combination is a promising way to persistently inhibit CH4 emissions with a higher rumen propionate proportion. However, the side effects of this nitrooxy compound on animals and its residues in animal products need further evaluation before it can be used as an animal feed additive.

Metabolomics for the Effect of Biotin and Nicotinamide on Transition Dairy Cows.

The objective of this study was to evaluate alterations in serum metabolites of transition dairy cows affected by biotin (BIO) and nicotinamide (NAM) supplementation. A total of 40 multiparous Holsteins were paired and assigned randomly within a block to one of the following four treatments: control (T0), 30 mg/day BIO (TB), 45 g/day NAM (TN), and 30 mg/day BIO + 45 g/day NAM (TB+N). Supplemental BIO and NAM were drenched on cows from 14 days before the expected calving date. Gas chromatography time-of-flight/mass spectrometry was used to analyze serum samples collected from eight cows in every groups at 14 days after calving. In comparison to T0, TB, TN, and TB+N had higher serum glucose concentrations, while non-esterified fatty acid in TN and TB+N and triglyceride in TB+N were lower. Adenosine 5'-triphosphate was significantly increased in TB+N. Both TN and TB+N had higher glutathione and lower reactive oxygen species. Moreover, TB significantly increased inosine and guanosine concentrations, decreased ß-alanine, etc. Certain fatty acid concentrations (including linoleic acid, oleic acid, etc.) were significantly decreased in both TN and TB+N. Some amino acid derivatives (spermidine in TN, putrescine and 4-hydroxyphenylethanol in TB+N, and guanidinosuccinic acid in both TN and TB+N) were affected. Correlation network analysis revealed that the metabolites altered by NAM supplementation were more complicated than those by BIO supplementation. These findings showed that both BIO and NAM supplementation enhanced amino acid metabolism and NAM supplementation altered biosynthesis of unsaturated fatty acid metabolism. The improved oxidative status and glutathione metabolism further indicated the effect of NAM on oxidative stress alleviation.

Leucine Affects α-Amylase Synthesis through PI3K/Akt-mTOR Signaling Pathways in Pancreatic Acinar Cells of Dairy Calves.

Dietary nutrient utilization, particularly starch, is potentially limited by digestion in dairy cow small intestine because of shortage of α-amylase. Leucine acts as an effective signal molecular in the mTOR signaling pathway, which regulates a series of biological processes, especially protein synthesis. It has been reported that leucine could affect α-amylase synthesis and secretion in ruminant pancreas, but mechanisms have not been elaborated. In this study, pancreatic acinar (PA) cells were used as a model to determine the cellular signal of leucine influence on α-amylase synthesis. PA cells were isolated from newborn Holstein dairy bull calves and cultured in Dulbecco's modifed Eagle's medium/nutrient mixture F12 liquid media containing four leucine treatments (0, 0.23, 0.45, and 0.90 mM, respectively), following α-amylase activity, zymogen granule, and signal pathway factor expression detection. Rapamycin, a specific inhibitor of mTOR, was also applied to PA cells. Results showed that leucine increased ( p < 0.05) synthesis of α-amylase as well as phosphorylation of PI3K, Akt, mTOR, and S6K1 while reduced ( p < 0.05) GCN2 expression. Inhibition of mTOR signaling downregulated the α-amylase synthesis. In addition, the extracellular leucine dosage significantly influenced intracellular metabolism of isoleucine ( p < 0.05). Overall, leucine regulates α-amylase synthesis through promoting the PI3K/Akt-mTOR pathway and reducing the GCN2 pathway in PA cells of dairy calves. These pathways form the signaling network that controls the protein synthesis and metabolism. It would be of great interest in future studies to explore the function of leucine in ruminant nutrition.

Ensembling Variable Selectors by Stability Selection for the Cox Model.

As a pivotal tool to build interpretive models, variable selection plays an increasingly important role in high-dimensional data analysis. In recent years, variable selection ensembles (VSEs) have gained much interest due to their many advantages. Stability selection (Meinshausen and Bühlmann, 2010), a VSE technique based on subsampling in combination with a base algorithm like lasso, is an effective method to control false discovery rate (FDR) and to improve selection accuracy in linear regression models. By adopting lasso as a base learner, we attempt to extend stability selection to handle variable selection problems in a Cox model. According to our experience, it is crucial to set the regularization region Λ in lasso and the parameter λmin properly so that stability selection can work well. To the best of our knowledge, however, there is no literature addressing this problem in an explicit way. Therefore, we first provide a detailed procedure to specify Λ and λmin. Then, some simulated and real-world data with various censoring rates are used to examine how well stability selection performs. It is also compared with several other variable selection approaches. Experimental results demonstrate that it achieves better or competitive performance in comparison with several other popular techniques.