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Bovine respiratory disease (BRD) is the most common disease in beef cattle and leads to considerable economic losses in both beef and dairy cattle. It is important to uncover the molecular mechanisms underlying BRD and to identify biomarkers for early identification of BRD cattle in order to address its impact on production and welfare. In this study, a longitudinal transcriptomic analysis was conducted using blood samples collected from 24 beef cattle at three production stages in the feedlot: 1) arrival (Entry group); 2) when identified as sick (diagnosed as BRD) and separated for treatment (Pulled); 3) prior to marketing (Close-out, representing healthy animals). Expressed genes were significantly different in the same animal among Entry, Pulled and Close-out stages (false discovery rate (FDR) < 0.01 & |Fold Change| > 2). Beef steers at both Entry and Pulled stages presented obvious difference in GO terms (FDR < 0.05) and affected biological functions (FDR < 0.05 & | Z -score| > 2) when compared with animals at Close-out. However, no significant functional difference was observed between Entry and Pulled animals. The interferon signaling pathway showed the most significant difference between animals at Entry/Pulled and Close-out stages (P <.001 & | Z -score| > 2), suggesting the animals initiated antiviral responses at an early stage of infection. Six key genes including IFI6 , IFIT3 , ISG15 , MX1 , and OAS2 were identified as biomarkers to predict and recognize sick cattle at Entry. A gene module with 169 co-expressed genes obtained from WGCNA analysis was most positively correlated (R = 0.59, P = 6E-08) with sickness, which was regulated by 11 transcription factors. Our findings provide an initial understanding of the BRD infection process in the field and suggests a subset of novel marker genes for identifying BRD in cattle at an early stage of infection. • Bovine respiratory disease (BRD) leads to considerable economic losses in both beef and dairy cattle. • Longitudinal transcriptomic analysis was conducted on blood samples from 24 beef cattle at three feedlot production stages. • Findings help increase understanding of the infection process and suggest marker genes to identify BRD early in infection. [ABSTRACT FROM AUTHOR]

Motivation Enhancing the utilization of human-inedible crop by-products by ruminants to produce high-quality milk for human consumption is an emerging global task. We performed a multi-omics-based study to decipher the regulatory biological processes of milk production when cows fed low-quality crop by-products with the aim to improve their utilization. Results Seven types of different high-throughput omics data were generated across three central organs [rumen, liver and mammary gland (MG)] and biofluids (rumen fluid and blood) that involved in milk production. The integrated multi-omics analysis including metabolomics, metagenomics and transcriptomics showed altered microbiome at compositional and functional levels, microbial metabolites in the rumen, down-regulated genes and associated functions in liver and MG. These changes simultaneously contributed to down-regulated three key metabolic nodes (propionate, glucose and amino acid) across these organs and biofluids that led to lowered milk yield and quality when cows consumed corn stover (CS). Hippuric acid was identified as a biomarker that led to low milk production in CS-fed cows, suggesting a future evaluation parameter related to the metabolic mechanism of low-quality forage utilization. This study unveils the milk production-related biological mechanism across different biofluids and tissues under a low-quality forage diet, which provides a novel understanding and potential improvement strategies for future crop by-products utilization and sustainable ruminant production. Availability and implementation The raw files of metagenomics, metabolomics, and transcriptomics data can be accessed at NCBI SRA (No. SRR5028206), EMBI-EBI (No. MTBLS411), and GEO (NO. GSE78524) databases respectively. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation Feed efficiency is an important trait for sustainable beef production that is regulated by the complex biological process, but the mode of action behinds it has not been clearly defined. Here, we aimed to elucidate the regulatory mechanisms of this trait through studying the landscape of the genome-wide gene expression of rumen, liver, muscle and backfat tissues, the key ones involved in the energy metabolism. Results The transcriptome of 189 samples across four tissues from 48 beef steers with varied feed efficiency were generated using Illumina HiSeq4000. The analysis of global gene expression profiles of four tissues, functional analysis of tissue-shared and -unique genes, co-expressed network construction of tissue-shared genes, weighted correlations analysis between gene modules and feed efficiency-related traits in each tissue were performed. Among four tissues, the transcriptome of muscle tissue was distinctive from others, while those of rumen and backfat tissues were similar. The associations between co-expressed genes and feed efficiency related traits at single or all tissues level exhibited that the gene expression in the rumen, liver, muscle and backfat were the most correlated with feed conversion ratio, dry matter intake, average daily gain and residual feed intake, respectively. The 19 overlapped genes identified from the strongest module–trait relationships in four tissues are potential generic gene markers for feed efficiency. Availability and implementation The distribution of gene expression data can be accessed at https://www.cattleomics.com/transcriptome. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Abstract The production of adequate and nutritious animal proteins for the increasing human population is an urgent global task. Therefore, enhancing the efficiency and sustainability of food animal production requires advanced analytical techniques. We propose the concept of “feedomics” for food animal research, an emerging field using omics technologies, to understand and uncover the mechanisms involved in many biological processes that determine animal productivity, product quality, and health as a result of the interactions among feed, environment, animal genetics, physiology, and its symbiotic microbiota. In this review, we summarize the findings to date based on the omics approaches including (meta)genomics, epigenomics, (meta)transcriptomics, proteomics, and metabolomics in food animal species and consider how these can be used to understand the processes from the “gate” to “plate”. We also highlight future directions for applying feedomics in fundamental and practical studies to improve the quantity, quality, safety and functional properties of food animal products. Graphical abstract Image 1 Highlights • Advanced analytical techniques create a new era of feed science and animal nutrition. • Feedomics consists of different molecular and cellular omics methods. • Feedomics improve the efficiency and sustainability of animal production for food security. • Feedomics contribute to animal and human health. [ABSTRACT FROM AUTHOR]

The current study aimed to determine whether breed and feed efficiency affect the molecular mechanisms regulating beneficial and non-beneficial fatty acid profiles in subcutaneous adipose tissue of beef steers. Fatty acid profiling and RNA-Seq based transcriptome analysis were performed on subcutaneous adipose tissues collected from beef steers with three divergent breeds (Angus, ANG, n = 47; Charolais, CHAR, n = 48; Kinsella Composite, KC, n = 48) and different residual feed intake (RFI, a measure of feed efficiency). The comparison of fatty acid profiles showed that KC had higher beneficial FAs compared to the other two breeds. Distinct FA profiles between H-RFIfat and L-RFIfat steers was more obvious for KC steers, where H-RFIfat steers tended to have higher proportion of healthy FAs and lower proportion of the unhealthy FAs. A higher number of differentially expressed (DE) genes were observed for KC steers, whereas ANG and CHAR steers had a lower number of DE genes between H- and L-RFIfat steers. The association analyses of the gene expressions and FA profiles showed that 10 FA metabolism-associated genes together with the one upstream regulator (SREBF1) were associated with the proportion of C18:2n-6, total n-6, PUFA and PUFA/SFA for KC steers but not the other two breeds. Subcutaneous adipose tissue FA profiles and healthy FA index differed in cattle with divergent feed efficiency and such variation was unique for the three examined cattle breeds. Key FA metabolism-associated genes together with SREBF1 which is the upstream regulator of a set of genes involved in lipid metabolism may be of importance for genetic selection of meat with higher healthy FA index in beef cattle. [ABSTRACT FROM AUTHOR]

The transition period in dairy cows is a critical stage and peripartum oxidative status, negative energy balance (NEB), and inflammation are highly prevalent. Fecal microbial metabolism is closely associated with blood oxidative status and nonesterified fatty acids (NEFA) levels. Here, we investigated dynamic changes in total oxidative status markers and NEFA in blood, fecal microbiome, and metabolome of 30 dairy cows during transition (−21, −7, +7, +21 d relative to calving). Then the Bayesian network and 9 machine-learning algorithms were applied to dismantle their relationship. Our results show that the oxidative status indicator (OSI) of −21, −7, +7 d was higher than +21 d. The plasma concentration of NEFA peaked on +7 d. For fecal microenvironment, a decline in bacterial α diversity was observed at postpartum and in bacterial interactions at +7 d. Conversely, microbial metabolites involved in carbohydrate, lipid, and energy metabolism increased on +7 d. A correlation analysis revealed that 11 and 10 microbial metabolites contributed to OSI and NEFA variations, respectively (arc strength >0.5). The support vector machine (SVM) radial model showed the highest average predictive accuracy (100% and 88.9% in the test and external data sets) for OSI using 1 metabolite and 3 microbiota. The SVM radial model also showed the highest average diagnostic accuracy (100% and 91% in the test and external data sets) for NEFA with 2 metabolites and 3 microbiota. Our results reveal a relationship between variation in the fecal microenvironment and indicators of oxidative status, NEB, and inflammation, which provide a theoretical basis for the prevention and precise regulation of peripartum oxidative status and NEB. [ABSTRACT FROM AUTHOR]

Simple Summary: Serum biochemical parameters and hormones play a role in directly reflecting the physiological state and modulating the milk performance of dairy cows. However, the variability of the serum biochemical parameters and hormones in multiparous mid-lactation cows have not drawn much attention due to their supposedly stable states, especially for those cows under the same nutrition and management condition. The aim of this study was to evaluate the effects of days-in-milk (DIM, within the mid-lactation) and parity (ranging from 2–6) on serum biochemical parameters and hormone profiles based on a large cohort of dairy cows. The results showed that DIM and parity contribute to the variations in serum biochemical parameters and hormones related to protein status, energy supply, liver and kidney function, and oxidative stress of mid-lactation dairy cows, with the effect of DIM being dominant over parity. Our result suggested that the DIM periods and parity should be taken into consideration to optimize nutritional strategies in order to improve the milk performance traits more precisely. It is well known that serum biochemical parameters and hormones contribute greatly to the physiological and metabolic status of dairy cows. However, few studies have focused on the variation of these serum parameters in multiparous mid-lactation cows without the interference of diet and management. A total of 287 Holstein dairy cows fed the same diet and maintained under the same management regime were selected from a commercial dairy farm to evaluate the effects of days-in-milk (DIM) and parity on serum biochemical parameters and hormone profiles. Milk yield and milk protein content were affected by DIM and parity (p < 0.05). Milk protein yield showed a numerically decreasing trend with parity, and it was relatively constant in cows with parities between 2 and 4 but lower in cows with parity 6 (p = 0.020). Ten and five serum biochemical parameters related to protein status, energy metabolism, liver and kidney function, and oxidative stress were affected by DIM and parity, respectively (p < 0.05). Glucagon, insulin-like growth factor 1 concentration, and the revised quantitative insulin sensitivity check index were significantly different (p < 0.05) among cows with different DIM. Parity had no effect on hormone concentrations. An interaction between DIM and parity effect was only detected for glucagon concentration (p = 0.015), which showed a significantly increasing trend with DIM and overall decreasing trend with parity. In summary, DIM and parity played an important role in affecting the serum biochemical parameters and/or hormones of dairy cows, with serum parameters affected more by DIM than parity. [ABSTRACT FROM AUTHOR]

Background: The rumen is the hallmark organ of ruminants, playing a vital role in their nutrition and providing products for humans. In newborn suckling ruminants milk bypasses the rumen, while in adults this first chamber of the forestomach has developed to become the principal site of microbial fermentation of plant fibers. With the advent of single-cell transcriptomics, it is now possible to study the underlying cell composition of rumen tissues and investigate how this relates the development of mutualistic symbiosis between the rumen and its epithelium-attached microbes. Results: We constructed a comprehensive cell landscape of the rumen epithelium, based on single-cell RNA sequencing of 49,689 high-quality single cells from newborn and adult rumen tissues. Our single-cell analysis identified six immune cell subtypes and seventeen non-immune cell subtypes of the rumen. On performing cross-species analysis of orthologous genes expressed in epithelial cells of cattle rumen and the human stomach and skin, we observed that the species difference overrides any cross-species cell-type similarity. Comparing adult with newborn cattle samples, we found fewer epithelial cell subtypes and more abundant immune cells, dominated by T helper type 17 cells in the rumen tissue of adult cattle. In newborns, there were more fibroblasts and myofibroblasts, an IGFBP3+ epithelial cell subtype not seen in adults, while dendritic cells were the most prevalent immune cell subtype. Metabolism-related functions and the oxidation-reduction process were significantly upregulated in adult rumen epithelial cells. Using 16S rDNA sequencing, fluorescence in situ hybridization, and absolute quantitative real-time PCR, we found that epithelial Desulfovibrio was significantly enriched in the adult cattle. Integrating the microbiome and metabolome analysis of rumen tissues revealed a high co-occurrence probability of Desulfovibrio with pyridoxal in the adult cattle compared with newborn ones while the scRNA-seq data indicated a stronger ability of pyroxidal binding in the adult rumen epithelial cell subtypes. These findings indicate that Desulfovibrio and pyridoxal likely play important roles in maintaining redox balance in the adult rumen. Conclusions: Our integrated multi-omics analysis provides novel insights into rumen development and function and may facilitate the future precision improvement of rumen function and milk/meat production in cattle. [ABSTRACT FROM AUTHOR]

[Display omitted] • Uncovered the changes in milk metabolome and rumen microbiome after RPM addition. • α-ketoglutaric is the key improved functional metabolite after RPM addition. • Acetate-producing bacteria are significantly changed after RPM addition. • Multi-omics revealed the association between the changed metabolites and bacteria. Cow's milk is a highly-nutritious dairy product part of human diet worldwide. Rumen-protected methionine (RPM) is widely used to improve lactation performance of dairy cows, but understanding of the effects of RPM on milk nutrients composition are still limited. In this study, twenty mid-lactating dairy cows were supplemented with 20 gm/day RPM for 8 weeks to investigate the responses of milk nutritional composition to RPM. Metabolomics was applied for analyzing milk metabolites and 16S rRNA gene sequencing was used for analysis of rumen microbial composition. Milk fat content and yield were significantly increased after RPM supplementation. Totally 443 compounds belonging to 15 classes were identified, among which 15 metabolites were significantly changed. The functional nutrient α-ketoglutaric acid were significantly increased in the milk after RPM supplementation. We found 48 significantly differing bacterial genera in the rumen after supplementing RPM. Multi-omics integrated analysis revealed the higher abundance of Acetobacter , unclassified_f_Lachnospiraceae and Saccharofermentan contributed to the improved milk fat. In addition, the enriched abundance of Thermoactinomyces , Asteroleplasma , and Saccharofermentan showed positive correlations with higher α-ketoglutaric acid of milk. Our results uncover the metabolomic fingerprint and the key functional metabolites in the milk after supplementing RPM in dairy cows, as well as the key rumen bacteria associated with them. These findings provide novel insights into the development of functional dairy products that enriched the functional nutrient α-ketoglutaric acid or high milk fat. [ABSTRACT FROM AUTHOR]

Simple Summary: It is difficult to obtain feed intake of dairy cows in experiments since all cows are raised in a free-stall barn in commercial dairy farms nowadays. Therefore, it is necessary to develop a simple, accurate, and reliable feed intake prediction model to replace direct measurement. In this study, we generated a forecasting model to predict daily dry matter intake (DMI) of dairy cows in mid and late lactation based on the feed intake of first 2 h after feeding (DMI-2h). The proposed prediction equation was: DMI (kg/day) = 8.499 + 0.2725 × DMI-2h (kg/day) + 0.2132 × Milk yield (kg/day) + 0.0095 × Body weight (kg/day) (R2 = 0.46). Compared with NRC model (2001), our model shows higher accuracy and precision on predicting daily DMI of dairy cows with fed ration three times per day in mid and late lactation period. This prediction model could be used as an alternative approach for researchers who have difficulty in measuring DMI in dairy cows' experiments. The objective of this study was to evaluate the feasibility of using the dry matter intake of first 2 h after feeding (DMI-2h), body weight (BW), and milk yield to estimate daily DMI in mid and late lactating dairy cows with fed ration three times per day. Our dataset included 2840 individual observations from 76 cows enrolled in two studies, of which 2259 observations served as development dataset (DDS) from 54 cows and 581 observations acted as the validation dataset (VDS) from 22 cows. The descriptive statistics of these variables were 26.0 ± 2.77 kg/day (mean ± standard deviation) of DMI, 14.9 ± 3.68 kg/day of DMI-2h, 35.0 ± 5.48 kg/day of milk yield, and 636 ± 82.6 kg/day of BW in DDS and 23.2 ± 4.72 kg/day of DMI, 12.6 ± 4.08 kg/day of DMI-2h, 30.4 ± 5.85 kg/day of milk yield, and 597 ± 63.7 kg/day of BW in VDS, respectively. A multiple regression analysis was conducted using the REG procedure of SAS to develop the forecasting models for DMI. The proposed prediction equation was: DMI (kg/day) = 8.499 + 0.2725 × DMI-2h (kg/day) + 0.2132 × Milk yield (kg/day) + 0.0095 × BW (kg/day) (R2 = 0.46, mean bias = 0 kg/day, RMSPE = 1.26 kg/day). Moreover, when compared with the prediction equation for DMI in Nutrient Requirements of Dairy Cattle (2001) using the independent dataset (VDS), our proposed model shows higher R2 (0.22 vs. 0.07) and smaller mean bias (−0.10 vs. 1.52 kg/day) and RMSPE (1.77 vs. 2.34 kg/day). Overall, we constructed a feasible forecasting model with better precision and accuracy in predicting daily DMI of dairy cows in mid and late lactation when fed ration three times per day. [ABSTRACT FROM AUTHOR]