The nutritional components and quality of milk are influenced by the rumen microbiota and its metabolites at different lactation stages. Hence, rumen fluid and milk samples from 6 dairy cows fed the same diet were collected during peak lactation, early mid-lactation, and later mid-lactation. Untargeted metabolomics and 16S rRNA sequencing were applied for analyzing milk and rumen metabolites, as well as rumen microbial composition, respectively. The levels of lipid-related metabolites, l-glutamate, glucose-1-phosphate, and acetylphosphate in milk exhibited lactation-dependent attenuation. Maltol, N-acetyl-d-glucosamine, and choline, which are associated with milk flavor or coagulation properties, as well as l-valine, lansioside A, clitocine, and ginsenoside La, increased significantly in early mid-lactation and later mid-lactation, especially in later mid-lactation. The obvious increase in rumen microbial diversities (ACE and Shannon indices) were observed in early mid-lactation compared with peak lactation. Twenty-one differential bacterial genera of the rumen were identified, with Succinivibrionaceae_UCG-001, Candidatus Saccharimonas, Fibrobacter, and SP3-e08 being significantly enriched in peak lactation. Rikenellaceae_RC9_gut_group, Eubacterium_ruminantium_group, Lachnospira, Butyrivibrio, Eubacterium_hallii_group, and Schwartzia were most significantly enriched in early mid-lactation. In comparison, only 2 bacteria (unclassified_f__Prevotellaceae and Prevotellaceae_UCG-001) were enriched in later mid-lactation. For rumen metabolites, LysoPE(16:0), l-glutamate, and l-tyrosine had higher levels in peak lactation, whereas PE(17:0/0:0), PE(16:0/0:0), PS(18:1(9Z)/0:0), l-phenylalanine, dulcitol, 2-(methoxymethyl)furan, and 3-phenylpropyl acetate showed higher levels in early mid-lactation and later mid-lactation. Multiomics-integrated analysis revealed that a greater abundance of Fibrobacter contributed to phospholipid content in milk by increasing ruminal acetate, l-glutamate, and LysoPE(16:0). Prevotellaceae_UCG-001 and unclassified_f_Prevotellaceae provide substrates for milk metabolites of the same category by increasing ruminal l-phenylalanine and dulcitol contents. These results demonstrated that milk metabolomic fingerprints and critical functional metabolites during lactation, and the key bacteria in rumen related to them. These findings provide new insights into the development of functional dairy products.

For the agroecosystems of the dairy cow industry, dietary carbohydrate (starch, neutral detergent fiber [NDF]) and fat could directly affect rumen methane emissions and host energy utilization. However, the relationships among diet, lactation performance, and methane emissions need to be further determined to assist dairy farms to adjust diet formulations and feeding strategies for environmental and production management. A meta-analysis was conducted in the current study to explore quantitative patterns of dietary fat and carbohydrate at different levels in balancing lactation performance and environment sustainability of dairy cows, and to establish a methane emission prediction model using the artificial neural network (ANN) model. The results showed that the regression relationship between dietary fat, carbohydrate and methane emissions could be shown by the following models: methane = 106.78 + (14.86 × DMI), R² = 0.80; methane = 443.17 – (46.41 × starch/NDF), R² = 0.76; and methane = 388.91 + (31.40 × fat) – (5.42 × fat²), R² = 0.80. The regression relationships between dietary fat, carbohydrate and lactation performance could be shown by the following models: milk fat yield = 1.08 + (0.43 × starch/NDF) – [0.34 × (starch/NDF)²], R² = 0.79; milk protein yield = 0.68 + (0.15 × fat) – (0.016 × fat²), R² = 0.82. In the structural equation model, we found that when formulating dietary carbohydrates and fats, it was necessary to balance the relationship between methane emissions and lactation performance. Specifically, dietary starch/NDF was lower than 0.63 (extremum point) and dietary fat was between 2.89% and 4.69% (extremum point), it could ensure that the aim of methane emission reduction (methane emissions decrease with increasing dietary starch/NDF and fat) was achieved without losing lactation performance of dairy cows (lactation performance increase with increasing dietary starch/NDF and fat). Finally, we established the ANN model to predict methane emissions (training set: R² = 0.62; validation set: R² = 0.61).

A major hypothesis for the induction of autoimmunity invokes the enhanced display of previously hidden (cryptic) epitopes under inflammatory conditions leading to the activation of self-reactive T cells. However, there is meager data that directly validate the influence of specific immune mediators on the upregulation of the presentation of cryptic determinants in vivo. We tested the effect on well-defined cryptic epitopes of hen eggwhite lysozyme (HEL) of the availability locally of a cytokine (IL-2, IL-4, IL-6, IL-10, TNF-α or granulocyte-macrophage colony-stimulating factor) at the antigen delivery site, or of the pretreatment of the immunogen with a cathepsin (Cat B, D, L or S) prior to use in vivo. Each of the three mouse strains (H-2^b/d/k) tested revealed a unique profile of T-cell reactivity to different cryptic epitopes of HEL in response to a particular cytokine or cathepsin. These results provide proof of principle for the reversal of crypticity of self-epitopes by immune mediators in the local milieu. Moreover, co-immunization with an antigen and a cytokine offers a simple and reliable tool for studying the role of cryptic epitopes in autoimmunity. Our results also strengthen the rationale for the use of inhibitors of cytokine/cathepsin activity in the treatment of autoimmune diseases.

This study aimed to investigate the effects of rumen-protected methionine (RPM) on the production performance of lactating dairy goats. Thirty first-time lactating Guanzhong dairy goats with identical kidding dates and comparable body weights (41.17 ± 3.05 kg) were randomly assigned to two groups: (1) CON: basal diet and (2) RPM: basal diet + 7.5 g/day RPM. The duration of the experiment was 21 days. Compared with the CON group, the RPM group presented a significant increase in milk yield, 4% fat-corrected milk (FCM), and feed efficiency; however, no significant difference was observed in dry feed intake (DMI). Moreover, milk fat, protein, lactose, and SNF production was greater in the PRM group than in the CON group. Compared with the CON group, the RPM group presented higher nonesterified fatty acid (NEFA) and very-low-density lipoprotein (VLDL) levels, and no significant differences in the other metabolites were detected. The concentrations of acetate, propionate, and total volatile fatty acids (TVFAs) in the feces of the RPM group were significantly greater than those in the CON group; however, no significant differences were detected in the concentrations of isobutyrate, butyrate, and valerate. Furthermore, genera such as Muribaculaceae, Bifidobacterium, and Christensenellaceae were significantly enriched in the feces of the RPM group. Concurrently, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the RPM group was significantly enriched in pathways associated with amino acid synthesis, the immune system, and energy metabolism. In summary, dietary supplementation with RPM improved the lipid metabolism function of the liver, increased the abundance of beneficial bacteria such as Muribaculaceae and Bifidobacterium in the colon, and enriched microbial functions related to energy and amino acid metabolism, thereby enhancing colon fermentation and host metabolic status, ultimately improving the production performance of lactating dairy goats. These findings elucidate the positive effects of RPM on the production performance and metabolic health of dairy goats, potentially offering new perspectives and strategies for optimizing dairy production.

Immune inhibitory receptors (IRs) play a critical role in the regulation of immune responses to various respiratory viral infections. However, in coronavirus disease 2019 (COVID-19), the roles of these IRs in immune modulation, metabolic reprogramming, and clinical characterization remain to be determined. Through consensus clustering analysis of IR transcription in the peripheral blood of patients with COVID-19, we identified two distinct IR patterns in patients with COVID-19, which were named IR_cluster1 and IR_cluster2. Compared to IR_cluster1 patients, IR_cluster2 patients with lower expressions of immune inhibitory receptors presented with a suppressed immune response, lower nutrient metabolism, and worse clinical manifestations or prognosis. Considering the critical influence of the integrated regulation of multiple IRs on disease severity, we established a scoring system named IRscore, which was based on principal component analysis, to evaluate the combined effect of multiple IRs on the disease status of individual patients with COVID-19. Similar to IR_cluster2 patients, patients with high IRscores had longer hospital-free days at day 45, required ICU admission and mechanical ventilatory support, and presented higher Charlson comorbidity index and SOFA scores. A high IRscore was also linked to acute infection phase and absence of drug intervention. Our investigation comprehensively elucidates the potential role of IR patterns in regulating the immune response, modulating metabolic processes, and shaping clinical manifestations of COVID-19. All of this evidence suggests the essential role of prognostic stratification and biomarker screening based on IR patterns in the clinical management and drug development of future emerging infectious diseases such as COVID-19.

The rumen microbiome plays a pivotal role in nutrient metabolism and host health, ultimately influencing production performance in ruminants, including dairy goats. Although enterotypes-distinct clusters of gut microbiota-have been identified, their effect on rumen fermentation and milk production in dairy goats remains poorly understood. This study aimed to characterize rumen microbial enterotypes and investigate their associations with fermentation characteristics and milk production traits in lactating dairy goats. A comprehensive analysis of 141 lactating dairy goats identified 2 rumen enterotypes: RE1 (n = 31) and RE2 (n = 110). Goats with the RE1 enterotype exhibited significantly higher milk yield, milk protein content, milk AA concentrations, and elevated concentrations of rumen total VFA, propionate, butyrate, and AA compared with the RE2 microbiota. The RE1 microbiota was dominated by Firmicutes taxa, including Oscillospiraceae NK4A214 group, Christensenellaceae R-7 group, and Ruminococcus, and was characterized by a lower acetate percentage and acetate-to-propionate ratio. In contrast, the RE2 microbiota was enriched with Bacteroidetes-affiliated genera, such as Prevotella, Rikenellaceae RC9 gut group, and Prevotellaceae UCG-003. Co-occurrence network analysis uncovered distinct interactions and keystone taxa between the 2 enterotypes, revealing multiple negative correlations between taxa enriched in the RE1 microbiota and those enriched in the RE2 microbiota. Notably, concentrations of propionate, butyrate, and AA were positively correlated with the abundance of RE1-dominant taxa and their corresponding microbial modules. Furthermore, structural equation modeling revealed that specific genera were directly linked with milk protein yield through their correlations with ruminal AA concentrations. These findings demonstrate a strong linkage between rumen enterotype, fermentation efficiency, and lactational performance in dairy goats. Enterotypes may serve as microbial markers for identifying high-performance animals and offer new opportunities to enhance lactation performance through precision feeding and microbiome-informed management strategies in dairy goats.