Microbial signature inferred from genomic breeding selection on milk urea concentration and its relation to proxies of nitrogen-utilization efficiency in Holsteins

Assessing milk urea nitrogen as an indicator of protein nutrition and nitrogen utilization efficiency: A meta-analysis

This meta-analysis aimed to assess the effectiveness of MUN as a tool for evaluating protein feeding and nitrogen (N) use efficiency in dairy cows. In this study, we selected 48 research papers published between January 2004 and April 2024, focusing on studies involving Holstein dairy cows with detailed dietary descriptions and results, including MUN, milk protein percentage, and yield, and dietary data on CP and NFC. We employed generalized linear fixed or mixed-effects models for data analysis, utilizing forest plots to visualize the estimated effects. On average, the cows included in the study were 121 DIM, produced 34.8 kg/d of milk, with milk protein at 3.16% and milk fat at 3.69%. The average MUN levels were 12.5 mg/dL, with urine N and fecal N excretions of 193 g/d and 196 g/d, respectively. The average DMI was 23.2 kg/d, with an N intake of 596 g/d. The dietary composition averaged 16.0% CP, 43.0% NFC, 33.6% NDF, 20.9% ADF, and 1.64 Mcal/kg of NEL. Our analysis revealed a close association among dietary NFC, CP, and MUN concentrations, identifying NFC and CP as key factors affecting MUN levels. When MUN levels ranged from 8 to 16 mg/dL, the dietary NFC/CP ratio was typically between 2.15 and 3.60. Furthermore, MUN exhibited a weak positive correlation with milk yield, milk protein percentage, and milk protein yield, a strong positive correlation with urine N excretion, and a negative correlation with the ratio of milk N to intake N. These findings imply that the dietary NFC/CP ratio significantly affects the MUN concentration. Further, it seems probable that by monitoring MUN, NFC, and CP levels together, dairy producers can achieve better balance of NFC and CP in diets, thereby enabling optimization of feed formulation and enhancement of the management of dairy cows.

Dynamic Changes in Rumen Microbial Diversity and Community Composition Within Rumen Fluid in Response to Various Storage Temperatures and Preservation Times

The aim of this study was to investigate the effects of storage temperature and preservation time on the microbial diversity and community composition of rumen fluid. Rumen fluid samples were collected from six Hu sheep fed on a high-forage diet and stored at -80 °C and -20 °C for intervals of 0, 7, 14, 30, 60, 120, and 240 days. DNA was extracted at each time point for 16S rRNA gene sequencing to evaluate the rumen microbial diversity and community composition. The results showed that storage temperature affected only the relative abundance of Proteobacteria, with no substantial impact on alpha-diversity or other microbial groups (p > 0.05), and no significant interaction effects were observed between storage temperature and preservation time (p > 0.05). Alpha-diversity indices such as Chao1, observed species, and PD whole tree showed dynamic changes after 7 days of storage, while the relative abundances of Verrucomicrobiota and Christensenellaceae R-7 group, as well as the energy metabolism metabolic pathway, exhibited significant alterations after 14 days of storage (p < 0.05). Notably, Patescibacteria, Rikenellaceae RC9 gut group, and Veillonellaceae UCG-001 abundances demonstrated significant changes after 240 days of storage (p < 0.05). Both principal coordinates analysis (PCoA) and non-metric multidimensional scaling (NMDS) showed distinct overlaps. This study suggests that storing rumen fluid at -80 °C and -20 °C does not influence rumen microbial diversity and community composition, whereas the storage time significantly impacts these factors, with most differences emerging after 14 days of preservation. Consequently, it is advised that the analysis of microbial diversity and community composition in rumen fluid samples be conducted within 14 days post-collection.

The role of milk urea nitrogen in nutritional assessment and its relationship with phenotype of dairy cows: A review

Urea is a small molecule that can readily cross the blood-milk barrier into milk, leading to a strong correlation between blood urea nitrogen (BUN) and milk urea nitrogen (MUN) concentrations. Although MUN is a minor component of milk, it is a valuable and cost-effective tool to flag potential nutrition-related problems in dairy herds. Many studies have suggested that intake of dietary protein and energy, as well as their synchronized release in the rumen, are major factors influencing MUN concentration. Therefore, measuring MUN can serve as a valuable indicator for improving nutritional management in dairy herds. Both excessively high and low MUN values are undesirable for dairy cows due to their negative effects on reproductive performance, health, and nitrogen use efficiency. Moreover, research indicates that MUN is a trait with low to moderate heritability and is positively correlated to nitrogen excretion. However, there are still inconsistencies regarding selecting cows with a low MUN phenotype can effectively reduce nitrogen excretion and affect other economic traits in dairy cows. This paper provides an overview of MUN's utility in nutritional assessment, presents its relationship with economically important milk traits, reproductive performance, health, and nitrogen emissions. It also describes the backgrounds of the gastrointestinal microbiota, intestine and kidney physiology in cows with different MUN concentrations, aiming to further enhance our understanding of MUN and provide a reference for optimal diets of cows.

Tetrastigma hemsleyanum as a feed additive: modulating gut microbiota for enhancing nutritional transport and growth performance in Jinhua yellow chickens

Tetrastigma hemsleyanum (TH) has attracted much attention for its heat clearing and detoxification effects, but whether it can become an effective feed supplement in chickens remains unclear. Herein, a total of 120 male Jinhua yellow chickens (two-mth-old) were randomly divided into into four groups (CON, TH-L, TH-M, and TH-H) for a 56-day feeding trial to explore its effects on growth performance and underlying mechanism. Results revealed that dietary TH notably increased the average daily growth (ADG), and decreased the average daily feed intake (ADFI) and feed conversion ratio (FCR) in TH-H group during 29-56 days. Meanwhile, dietary TH improved the development of duodenum and notably increased the contents of essential amino acids and flavor amino acids, while the serum oxidation stress index as well as abdominal fat deposition were not affected in Jinhua yellow chickens. Additionally, TH supplementation notably increased gut microbiota richness, then selectively increased the colonization of potential probiotics and the microbial abundance associated to amino acid synthesis and metabolic pathways in duodenum. Furthermore, qPCR analysis results preliminarily verified that dietary TH not only enhanced intestinal amino acids (rBAT and EAAT3) and peptides (PepT1 and APN) transport but also alleviated the inflammation (IL-1β, IL-6 and IFN-γ), thus thereby improved intestinal development and growth performance in Jinhua yellow chickens. These findings demonstrated that TH is a feed additive that can improve growth performance, muscle amino acids composition and intestinal development.

Multi-tissue gene expression profiling of cows with a genetic predisposition for low and high milk urea levels

Milk urea (MU) concentration is proposed as an indicator trait for breeding toward reduced nitrogen (N) emissions and leaching in dairy. We selected 20 German Holstein cows based on MU breeding values, with 10 cows each having low (LMUg) and high (HMUg) MU genetic predisposition. Using RNA-seq, we characterized these cows to unravel molecular pathways governing post-absorptive body N pools focusing on renal filtration and reabsorption of nitrogenous compounds, hepatic urea formation and mammary gland N excretion. While we observed minor adjustments in cellular energy metabolism in different tissues associated with different MU levels, no transcriptional differences in liver ammonia detoxification were detected, despite significant differences in MU between the groups. Differential expression of AQP3 and SLC38A2 in the kidney provides evidence for higher urea concentration in the collecting duct of LMU cows than HMU cows. The mammary gland exhibited the most significant differences, particularly in tricarboxylic acid (TCA) cycle genes, amino acid transport, tRNA binding, and casein synthesis. These findings suggest that selecting for lower MU could lead to altered urinary urea (UU) handling and changes in milk protein synthesis. However, given the genetic variability in N metabolism components, the long-term effectiveness of MU-based selection in reducing N emissions remains uncertain.

Rumen microbiota helps Tibetan sheep obtain energy more efficiently to survive in the extreme environment of the Qinghai-Tibet Plateau

Introduction

T-sheep and H-sheep exhibit different environmental adaptability and production performance. The rumen microbiome has co-evolved with hosts and plays a vital role in nutrient digestion and energy metabolism. In our previous study, we found that T-sheep have a higher efficiency in energy metabolism than H-sheep, but the rumen microbial community remains unclear.

Methods

In this study, we determined the rumen bacterial profile and rumen fermentation parameters to reveal the bacterial profiles and predictive functions among breeds and diets with four different energy levels, as well as the correlation between bacterial profiles and rumen fermentation characteristics.

Results

The results showed that the rumen total volatile fatty acids (VFAs), acetate, butyrate, total branched-chain VFAs, iso-butyrate, and iso-valerate were higher in T-sheep than H-sheep. The alpha diversity of ruminal bacteria is not affected by dietary energy, but it shows a distinction between the sheep breeds. Specifically, T-sheep rumen bacteria exhibit higher alpha diversity than H-sheep. The beta diversity of ruminal bacteria is not influenced by dietary energy or sheep breeds, indicating similar communities of ruminal bacteria between different diets and sheep breeds. The phyla of Bacteroidetes and Firmicutes predominate in the rumen, with a higher relative abundance of Firmicutes observed in T-sheep than H-sheep. The two most abundant genera in the rumen were Prevotella 1 and Rikenellaceae RC9 gut group. Prevotella 1 is the predominant bacterial genus in the rumen of H-sheep, while the Rikenellaceae RC9 gut group dominates in the rumen of T-sheep. Microbial co-occurrence network analysis reveals that variations in rumen fermentation characteristics result from differences in module abundance, with a higher abundance of VFA-producing modules observed in the rumen of T-sheep. Microbial function prediction analysis showed that dietary energy rarely alters the functional composition of rumen bacteria. However, there were differences in the functions of rumen bacteria between sheep breeds, with T-sheep showing a greater emphasis on energy metabolism-related functions, while H-sheep showed a greater emphasis on protein metabolism-related functions.

Discussion

These findings provide evidence of the special rumen microbial community that helps T-sheep efficiently obtain energy from low-protein and low-energy diets, enabling them to survive in the extreme environment of the Qinghai-Tibet Plateau.

Interpretable machine learning reveals microbiome signatures strongly associated with dairy cow milk urea nitrogen

The gut microbiome plays an important role in the healthy and efficient farming of dairy cows. However, high-dimensional microbial information is difficult to interpret in a simplified manner. We collected fecal samples from 161 cows and performed 16S amplicon sequencing. We developed an interpretable machine learning framework to classify individuals based on their milk urea nitrogen (MUN) concentrations. In this framework, we address the challenge of handling high-dimensional microbial data imbalances and identify 9 microorganisms strongly correlated with MUN. We introduce the Shapley Additive Explanations (SHAP) method to provide insights into the machine learning predictions. The results of the study showed that the performance of the machine learning model improved (accuracy = 72.7%) after feature selection on high-dimensional data. Among the 9 microorganisms, g__Firmicutes_unclassified had the greatest impact in the model. This study provides a reference for precision animal husbandry.