Yabin Ma

Introduction: Mastitis is one of the costliest diseases in the dairy industry. Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) are the two most predominant pathogens. However, the specific molecular mechanisms underlying the interactions between these pathogens and bovine mammary epithelial cells, especially for two pathogenic co-infections, remain poorly understood.Methods: Here, this study employed high-throughput RNA sequencing to comprehensively analyze the gene expression changes in bovine mammary epithelial cells upon individual and co-infection with E. coli and S. aureus.Results: Transcriptomic analysis identified 282 differentially expressed genes (DEGs) in the E. coli-infected group (E group), with 246 upregulated and 36 downregulated genes. Notably, pro-inflammatory genes (CXCL8, GRO1, CCL20) were significantly induced, and functional enrichment analysis demonstrated robust activation of inflammatory pathways including TLR/NF-κB and IL-17 signaling cascades. In contrast, the S. aureus-infected group (S group) exhibited 354 DEGs (314 upregulated, 40 downregulated), featuring pathogen-specific upregulated genes (ESM1, IL18RAP). Functional annotation revealed predominant involvement of metabolic processes, particularly ATP metabolism and chaperone complex activities. The co-infection group (ES group) displayed 307 DEGs (277 upregulated, 30 downregulated), demonstrating a unique “inflammatory-metabolic” dual-mode signature that integrated inflammatory features from the E group with metabolic reprogramming characteristics of the S group. Protein-protein interaction network analysis further delineated pathogen-specific hubs: inflammatory mediators (CXCL8, CCL20, IL6) in the E group, molecular chaperones (CCT5, RUVBL1/2) in the S group, and a distinctive IL6-FBL-centered network in co-infection. These findings elucidate pathogen-specific molecular mechanisms at the transcriptomic level, particularly revealing a unique “inflammatory-metabolic” dual-mode regulatory network during co-infection states. These findings provide new insights into the pathogenesis of mastitis and provide a theoretical basis for developing targeted prevention and control strategies.

Our previous study identified the apolipoprotein M (APOM) and cytochrome P450 family 7 subfamily A polypeptide 1 (CYP7A1) genes as candidates for milk traits in dairy cattle, which were significantly up-regulated in liver tissue of Holstein cows between the dry and lactation periods. The two genes play critical roles in the peroxisome proliferator-activated receptor (PPAR) pathway. In this study, we further confirmed whether the APOM and CYP7A1 genes had significant genetic impacts on milk production traits in a Chinese Holstein population. By dual-direction sequencing of the polymerase chain reaction (PCR) products of the complete coding sequences and 2000 bp of the 5′ and 3′ flanking regions on pooled DNA sample, seven and three single nucleotide polymorphisms (SNPs) were identified in APOM and CYP7A1, respectively. With SAS 9.2, phenotype-genotype association analysis revealed such SNPs were significantly associated with at least one of the milk production traits, including 305-day milk yield, milk fat yield, milk fat percentage, milk protein yield, and milk protein percentage in the first and second lactations (p = APOM and thee SNPs in CYP7A1 formed one haplotype, respectively. The haplotypes were significantly associated with at least one of milk production traits as well (p = APOM and rs42765359 in CYP7A1, significantly impacted the gene transcriptional activity after mutation (p < 0.01) through changing the transcription factor binding sites by using luciferase assay experiments. Additionally, with RNAfold Web Server, rs110098953 and rs378530166 changed the mRNA secondary structures of APOM and CYP7A1 genes, respectively. In summary, our research is the first to demonstrate that APOM and CYP7A1 genes have significantly genetic effects on milk yield and composition traits, and the identified SNPs may serve as available genetic markers for genomic selection program in dairy cattle.

Fourier Transform Mid-Infrared Spectroscopy (FT-MIRS) can be used for quantitative detection of milk components. Here, milk samples of 458 Chinese Holstein cows from 11 provinces in China were collected and we established a total of 22 quantitative prediction models in milk fatty acids by FT-MIRS. The coefficient of determination of the validation set ranged from 0.59 (C18:0) to 0.76 (C4:0). The models were adopted to predict the milk fatty acids from 2138 cows and a new high-throughput computing software HiBLUP was employed to construct a multi-trait model to estimate and analyze genetic parameters in dairy cows. Finally, genome-wide association analysis was performed and seven novel SNPs significantly associated with fatty acid content were selected, investigated, and verified with the FarmCPU method, which stands for “Fixed and random model Circulating Probability Unification”. The findings of this study lay a foundation and offer technical support for the study of fatty acid trait breeding and the screening and grouping of characteristic dairy cows in China with rich, high-quality fatty acids. It is hoped that in the future, the method established in this study will be able to screen milk sources rich in high-quality fatty acids.

Fatty acid is an important factor affecting the nutritional quality of milk. In this study, we collected and assessed 78,086 milk samples from 12,065 Chinese Holstein cows from 11 farms in Northern China from November 2019 to September 2022. The contents of eight fatty acid groups were predicted using FT-MIRS-based models. The contents of TFAs, SFAs, UFAs, MUFAs, PUFAs, and LCFAs in milk reached the highest at 96–125 DIM, and SCFA and MCFA contents reached the highest at 276–305 DIM. With the increase in somatic cell score, the contents of various fatty acid groups in milk gradually decreased, and the nutritional value of milk and flavor of dairy products gradually deteriorated. The contents of high-quality fatty acids in milk, particularly UFAs and MUFAs, were significantly higher in the non-pregnant state than in the pregnant state. However, SCFA and MCFA contents exhibited the opposite pattern. Our findings provided valuable information on the content and distribution range of fatty acid groups in milk from Chinese Holstein cows. Further analysis is warranted to explore the breeding of Chinese Holstein cows providing milk with abundant beneficial fatty acids.

Cattle rumination behavior is strongly correlated with its health. Current methods often rely on manual observation or wearable devices to monitor ruminating behavior. However, the manual monitoring of cattle rumination is labor-intensive, and wearable devices often harm animals. Therefore, this study proposes a non-contact method for monitoring cattle rumination behavior, utilizing an improved YOLOv8-pose keypoint detection algorithm combined with multi-condition threshold peak detection to automatically identify chewing counts. First, we tracked and recorded the cattle’s rumination behavior to build a dataset. Next, we used the improved model to capture keypoint information on the cattle. By constructing the rumination motion curve from the keypoint information and applying multi-condition threshold peak detection, we counted the chewing instances. Finally, we designed a comprehensive cattle rumination detection framework to track various rumination indicators, including chewing counts, rumination duration, and chewing frequency. In keypoint detection, our modified YOLOv8-pose achieved a 96% mAP, an improvement of 2.8%, with precision and recall increasing by 4.5% and 4.2%, enabling the more accurate capture of keypoint information. For rumination analysis, we tested ten video clips and compared the results with actual data. The experimental results showed an average chewing count error of 5.6% and a standard error of 2.23%, verifying the feasibility and effectiveness of using keypoint detection technology to analyze cattle rumination behavior. These physiological indicators of rumination behavior allow for the quicker detection of abnormalities in cattle’s rumination activities, helping managers make informed decisions. Ultimately, the proposed method not only accurately monitors cattle rumination behavior but also provides technical support for precision management in animal husbandry, promoting the development of modern livestock farming.

Efficient reproductive management of dairy cows depends primarily upon accurate estrus identification. However, the currently available estrus detection methods, such as visual observation, are poor. Hence, there is an urgent need to discover novel biomarkers in non-invasive bodily fluids such as milk to reliably detect estrus status. Proteomics is an emerging and promising tool to identify biomarkers. In this study, the proteomics approach was performed on milk sampled from estrus and non-estrus dairy cows to identify potential biomarkers of estrus. Dairy cows were synchronized and timed for artificial insemination, and the cows with insemination leading to conception were considered to be in estrus at the day of insemination (day 0). Milk samples of day 0 (estrus group) and day −3 (non-estrus group) from dairy cows confirming to be pregnant were collected for proteomic analysis using the tandem mass tags (TMT) proteomics approach. A total of 89 differentially expressed proteins were identified, of which 33 were upregulated and 56 were downregulated in the estrus milk compared with the non-estrus milk. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that acetyl coenzyme A carboxylase α (ACACA), apolipoprotein B (APOB), NAD(P)H steroid dehydrogenase-like (NSDHL), perilipin 2 (PLIN2), and paraoxonase 1 (PON1) participated in lipid binding, lipid storage, lipid localization, and lipid metabolic process, as well as fatty acid binding, fatty acid biosynthesis, and fatty acid metabolism, and these processes are well documented to be related to estrus regulation. These milk proteins are proposed as possible biomarkers of estrus in dairy cows. Further validation studies are required in a large population to determine their potential as estrus biomarkers.

Milk produced by dairy cows is a complex combination of many components. However, at present, changes in only a few milk components (e.g., fat, protein, and lactose) during the estrus cycle in dairy cows have been documented. Mid-infrared (MIR) spectroscopy is a worldwide method routinely used for milk analysis, as MIR spectra reflect the global composition of milk. Therefore, this study aimed to investigate the changes in milk MIR spectra and milk production traits (fat, protein, lactose, urea, total solids (TS), and solid not fat (SnF)) due to estrus. Cows that were successfully inseminated, leading to conception, were included. Cows confirmed to be pregnant were considered to be in estrus at the day of insemination (day 0). A general linear mixed model, which included the random effect of cows, the fixed classification effects of parity number, days in relation to estrus, as well as the interaction between parity number and days in relation to estrus, was applied to investigate the changes in milk production traits and 1060 milk infrared wavenumbers, ranging from 925 to 5011 cm⁻¹, of 371 records from 162 Holstein cows on the days before (day −3, day −2, and day −1) and on the day of estrus (day 0). The days in relation to estrus had a significant effect on fat, protein, urea, TS, and SnF, whose contents increased from day −3 to day 0. Lactose did not seem to be significantly influenced by the occurrence of estrus. The days in relation to estrus had significant effects on the majority of the wavenumbers. Besides, we found that some of the wavenumbers in the water absorption regions were significantly changed on the days before and on the day of estrus. This suggests that these wavenumbers may contain useful information. In conclusion, the changes in the milk composition due to estrus can be observed through the analysis of the milk MIR spectrum. Further analyses are warranted to more deeply explore the potential use of milk MIR spectra in the detection of estrus.