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Dairy cows are economically important livestock animals that supply milk for humans. The cow’s rumen is a complex and symbiotic ecosystem composed of diverse microorganisms, which has evolved to digest high-fiber diets. Subacute ruminal acidosis (SARA) is a major metabolic disease in lactating dairy cows caused by the excessive intake of high-concentrate diets. Here, we investigated the synergistic responses of rumen bacteria and epithelium to high-grain (HG)-induced SARA. Eight ruminally cannulated lactating Holstein cows were randomly assigned to 2 groups for a 3-week experiment and fed either a conventional (CON) diet or an HG diet. The results showed that the HG-feeding cows had a thickened rumen epithelial papilla with edge injury and a decreased plasma β-hydroxybutyrate concentration. The 16S rRNA gene sequencing results demonstrated that HG feeding caused changes in rumen bacterial structure and composition, which further altered rumen fermentation and metabolism. Cooccurrence network analysis revealed that the distribution of the diet-sensitive bacteria responded to the treatment (CON or HG) and that all diet-sensitive amplicon sequence variants showed low to medium degrees of cooccurrence. Metabolomics analysis indicated that the endothelial permeability-increasing factor prostaglandin E1 and the polyamine synthesis by-product 5′-methylthioadenosine were enriched under HG feeding. Transcriptome analysis suggested that cholesterol biosynthesis genes were upregulated in the rumen epithelium of HG cows. The gene expression changes, coupled with more substrate being available (total volatile fatty acids), may have caused an enrichment of intracellular cholesterol and its metabolites. All of these variations could coordinately stimulate cell proliferation, increase membrane permeability, and trigger epithelial inflammation, which eventually disrupts rumen homeostasis and negatively affects cow health. IMPORTANCE Dairy cows are economically important livestock animals that supply milk for humans. The cow’s rumen is a complex and symbiotic ecosystem composed of diverse microorganisms, which has evolved to digest high-fiber diets. In modern dairy production, SARA is a common health problem due to overfeeding of high-concentrate diets for an ever-increasing milk yield. Although extensive studies have been conducted on SARA, it remains unclear how HG feeding affects rumen cross talk homeostasis. Here, we identified structural and taxonomic fluctuation for the rumen bacterial community, an enrichment of certain detrimental metabolites in rumen fluid, and a general upregulation of cholesterol biosynthesis genes in the rumen epithelium of HG-feeding cows by multi-omics analysis. Based on these results, we propose a speculation to explain cellular events of coordinated rumen bacterial and epithelial adaptation to HG diets. Our work provides new insights into the exploitation of molecular regulation strategies to treat and prevent SARA.

Our aim was to simultaneously investigate the gut bacteria typical characteristic and conduct rumen metabolites profiling of high production dairy cows when compared to low‐production dairy cows. The bacterial differences in rumen fluid and feces were identified by 16S rDNA gene sequencing. The metabolite differences were identified by metabolomics profiling with liquid chromatography mass spectrometry (LC‐MS). The results indicated that the high‐production dairy cows presented a lower rumen bacterial richness and species evenness when compared to low‐production dairy cows. At the phylum level, the high‐production cows increased the abundance of Proteobacteria and decreased the abundance of Bacteroidetes, SR1, Verrucomicrobia, Euryarchaeota, Planctomycetes, Synergistetes, and Chloroflexi significantly (p < 0.05). At the genus level, the rumen fluid of the high‐production group was significantly enriched for Butyrivibrio, Lachnospira, and Dialister (p < 0.05). Meanwhile, rumen fluid of high‐production group was depleted for Prevotella, Succiniclasticum, Ruminococcu, Coprococcus,YRC22, CF231, 02d06, Anaeroplasma, Selenomonas, and Ruminobacter significantly (p < 0.05). A total of 92 discriminant metabolites were identified between high‐production cows and low‐production cows. Compared to rumen fluid of low‐production dairy cows, 10 differential metabolites were found up‐regulated in rumen fluid of high‐production dairy cows, including 6alpha‐Fluoropregn‐4‐ene‐3,20‐dione, 3‐Octaprenyl‐4‐hydroxybenzoate, disopyramide, compound III(S), 1,2‐Dimyristyl‐sn‐glycerol, 7,10,13,16‐Docosatetraenoic acid, ferrous lactate, 6‐Deoxyerythronolide B, vitamin D2, L‐Olivosyl‐oleandolide. The remaining differential metabolites were found down‐regulated obviously in high‐production cows. Metabolic pathway analyses indicated that most increased abundances of rumen fluid metabolites of high‐yield cows were related to metabolic pathways involving biosynthesis of unsaturated fatty acids, steroid biosynthesis, ubiquinone and other terpenoid‐quinone biosynthesis. Most down‐regulated metabolic pathways were relevant to nucleotide metabolism, energy metabolism, lipid metabolism and biosynthesis of some antibiotics. Our aim was to simultaneously investigated the gut bacteria typical characteristic and conduct rumen metabolites profiling of high‐production dairy cows when compared to low‐production dairy cows. The aim of the study was to explore the typical gut bacteria and rumen typical metabolites matter of high‐production airy cows. After that, the results can be applied in the low‐yield dairy cows to improve their milking performance.

A high-grain (HG) diet can rapidly lower the rumen pH and thus modify the gastrointestinal microbiome in dairy cattle. Although the prevalence of antibiotic resistance is strongly linked with the gut microbiome, the influences of HG diet on animals’ gut resistome remain largely unexplored. Here, we examined the impact and mechanism of an HG diet on the fecal resistome in dairy cattle by metagenomically characterizing the gut microbiome. Eight lactating Holstein cattle were randomly allocated into two groups and fed either a conventional (CON) or HG diet for 3 weeks. The fecal microbiome and resistome were significantly altered in dairy cattle from HG, demonstrating an adaptive response that peaks at day 14 after the dietary transition. Importantly, we determined that feeding an HG diet specifically elevated the prevalence of resistance to aminoglycosides (0.11 vs 0.24 RPKG, P < 0.05). This diet-induced resistance increase is interrelated with the disproportional propagation of microbes in Lachnospiraceae, indicating a potential reservoir of aminoglycosides resistance. We further showed that the prevalence of acquired resistance genes was also modified by introducing a different diet, likely due to the augmented frequency of lateral gene transfer (LGT) in microbes (CON vs HG: 254 vs 287 taxa) such as Lachnospiraceae. Consequently, we present that diet transition is associated with fecal resistome modification in dairy cattle and an HG diet specifically enriched aminoglycosides resistance that is likely by stimulating microbial LGT. The increasing prevalence of antimicrobial resistance is one of the most severe threats to public health, and developing novel mitigation strategies deserves our top priority. High-grain (HG) diet is commonly applied in dairy cattle to enhance animals’ performance to produce more high-quality milk. We present that despite such benefits, the application of an HG diet is correlated with an elevated prevalence of resistance to aminoglycosides, and this is a combined effect of the expansion of antibiotic-resistant bacteria and increased frequency of lateral gene transfer in the fecal microbiome of dairy cattle. Our results provided new knowledge in a typically ignored area by showing an unexpected enrichment of antibiotic resistance under an HG diet. Importantly, our findings laid the foundation for designing potential dietary intervention strategies to lower the prevalence of antibiotic resistance in dairy production.

Background Bovine milk is an important source of nutrition for human consumption, and its quality is closely associated with the microbiota and metabolites in it. But there is limited knowledge about the milk microbiome and metabolome in cows with subacute ruminal acidosis. Methods Eight ruminally cannulated Holstein cows in mid lactation were selected for a 3-week experiment. The cows were randomly allocated into 2 groups, fed either a conventional diet (CON; 40% concentrate; dry matter basis) or a high-concentrate diet (HC; 60% concentrate; dry matter basis). Results The results showed that there was a decreased milk fat percentage in the HC group compared to the CON group. The amplicon sequencing results indicated that the alpha diversity indices were not affected by the HC feeding. At the phylum level, the milk bacteria were dominated by Proteobacteria, Actinobacteria, Bacteroidetes, and Firmicutes both in the CON and HC groups. At the genus level, the HC cows displayed an improved proportion of Labrys ( P  = 0.015) compared with the CON cows. Results of both the principal components analysis and partial least squares of discriminant analysis of milk metabolome revealed that samples of the CON and HC groups clustered separately. A total of 31 differential metabolites were identified between the two groups. Of these, the levels of 11 metabolites decreased (α-linolenic acid, prostaglandin E2, L -lactic acid, L -malic acid, 3-hydroxysebacic acid, succinyladenosine, guanosine, pyridoxal, L -glutamic acid, hippuric acid, and trigonelline), whereas the levels of the other 20 metabolites increased in the HC group with respect to the CON group ( P  < 0.05). Conclusion These results suggested that subacute ruminal acidosis less impacted the diversity and composition of milk microbiota, but altered the milk metabolic profiles, which led to the decline of the milk quality.

Holstein dairy cows, Chinese Luxi Yellow cattle, Chinese Laoshan dairy goats, and Chinese Bohai Black cattle were selected for the study. The 16S rDNA sequencing technique was used to analyze the microflora in the digestive tract. The rumen flora in high milk‐yield Holstein dairy cows showed significantly higher proportions of Treponema, Butyrivibrio, Coprococcus, Shuttleworthia, Lachnospira, and Selenomonas, compared with the rumen flora in Chinese Bohai Black cattle and Chinese Luxi Yellow cattle (p < 0.05). In addition, the abundances of Succiniclasticum, Ruminococcus, and Fibrobacter in the rumen fluid of high‐yield dairy cows were significantly higher than those in rumen flora of dairy goats. Compared with ruminal flora in Chinese Luxi Yellow cattle, the rumen flora in high‐yield dairy cattle showed significantly higher Prevotella. Compared with the rumen flora in Chinese Laoshan dairy goats, Chinese Bohai Black cattle, and Chinese Luxi Yellow cattle, the flora in high‐yielding dairy cows showed significantly lower proportions of CF231, 02d06, Oscillospira, RFN20, Desulfovibrio, Methanobrevibacter, and SHD‐231. In addition, compared with the rumen flora in dairy goats, the rumen flora in high‐yielding dairy cattle displayed significantly lower proportion of Enterococcus. Compared with the rumen flora in Chinese Bohai Black cattle, the flora in high‐yielding dairy cattle exhibited significantly lower Ruminococcus, YRC22, Pseudobutyrivibrio, L7A_E11, BF311, p‐75‐a5, and Dehalobacterium. Compared with the rumen flora in Chinese Luxi Yellow cattle, the flora in the high‐yield dairy cows also displayed significantly lower proportions of Ruminococcus, YRC22, BF311, Paludibacter, and Dehalobacterium. In this study, Holstein dairy cows, Chinese Luxi Yellow cattle, Chinese Laoshan dairy goats and Chinese Bohai Black cattle were selectd as the research object. The 16 SrDNA sequencing technique was used to analyze the microflora in the digestive tract.

Foundation models have emerged as a powerful tool for many AI problems. Despite the tremendous success of foundation models, effective adaptation to new tasks, particularly those with limited labels, remains an open question and lacks theoretical understanding. An emerging solution with recent success in vision and NLP involves finetuning a foundation model on a selection of relevant tasks, before its adaptation to a target task with limited labeled samples. In this paper, we study the theoretical justification of this multitask finetuning approach. Our theoretical analysis reveals that with a diverse set of related tasks, this multitask finetuning leads to reduced error in the target task, in comparison to directly adapting the same pretrained model. We quantify the relationship between finetuning tasks and target tasks by diversity and consistency metrics, and further propose a practical task selection algorithm. We substantiate our theoretical claims with extensive empirical evidence. Further, we present results affirming our task selection algorithm adeptly chooses related finetuning tasks, providing advantages to the model performance on target tasks. We believe our study shed new light on the effective adaptation of foundation models to new tasks that lack abundant labels. Our code is available at https://github.com/OliverXUZY/Foudation-Model_Multitask.

We address the challenging problem of deep representation learning--the efficient adaption of a pre-trained deep network to different tasks. Specifically, we propose to explore gradient-based features. These features are gradients of the model parameters with respect to a task-specific loss given an input sample. Our key innovation is the design of a linear model that incorporates both gradient and activation of the pre-trained network. We show that our model provides a local linear approximation to an underlying deep model, and discuss important theoretical insights. Moreover, we present an efficient algorithm for the training and inference of our model without computing the actual gradient. Our method is evaluated across a number of representation-learning tasks on several datasets and using different network architectures. Strong results are obtained in all settings, and are well-aligned with our theoretical insights.