Go with the flow-biology and genetics of the lactation cycle

A Comparative Analysis of the Gene Expression Profiles in the Mammary Glands of Lactating and Nonlactating Mares at the Second Month of Gestation

To investigate molecular regulation involved in lactation during pregnancy, this study focused on the transcriptomic profiles of mammary tissue from lactating and non-lactating Mongolian mares at the second month of gestation. A total of 4197 differentially expressed genes were identified by comparing mammary tissues from pregnant mares at two different states, including 1974 differentially expressed genes such as the milk protein-related genes a-s1-casein (CSN1S1), k-casein (CSN3), lactalbumin (LALBA), and lactoferrin (LTF), which were highly expressed in the lactating mares group, and overall, these differentially expressed genes were mainly associated with biological processes such as endoplasmic reticulum protein processing, the Toll-like receptor signaling pathway, steroid biosynthesis, cytokine-cytokine receptor interactions, and amino sugar and nucleotide glycolysis. These findings serve as a foundation for investigating the molecular underpinnings of lactation in pregnant equids.

Transcriptomic profiling of gastrointestinal tracts in dairy cattle during lactation reveals molecular adaptations for milk synthesis

During lactation, dairy cattle's digestive tract requires significant adaptations to meet the increased nutrient demands for milk production. As we attempt to improve milk-related traits through selective pressure, it is crucial to understand the biological functions of the epithelia of the rumen, small intestine, and colonic tissues in response to changes in physiological state driven by changes in nutrient demands for milk synthesis. In this study, we obtained a total of 108 transcriptome profiles from three tissues (epithelia of the colon, duodenum, and rumen) of five Holstein cows, spanning eight time points from the early, mid, late lactation periods to the dry period. On average 97.06% of reads were successfully mapped to the reference genome assembly ARS-UCD1.2. We analyzed 27,607 gene expression patterns at multiple periods, enabling direct comparisons within and among tissues during different lactation stages, including early and peak lactation. We identified 1645, 813, and 2187 stage-specific genes in the colon, duodenum, and rumen, respectively, which were enriched for common or specific biological functions among different tissues. Time series analysis categorized the expressed genes within each tissue into four clusters. Furthermore, when the three tissues were analyzed collectively, 36 clusters of similarly expressed genes were identified. By integrating other comprehensive approaches such as gene co-expression analyses, functional enrichment, and cell type deconvolution, we gained profound insights into cattle lactation, revealing tissue-specific characteristics of the gastrointestinal tract and shedding light on the intricate molecular adaptations involved in nutrient absorption, immune regulation, and cellular processes for milk synthesis during lactation.

Phenotypic and genomic modeling of lactation curves: A longitudinal perspective

Lactation curves, which describe the production pattern of milk-related traits over time, provide insightful information about individual cow health, resilience, and milk production efficiency. Key functional traits can be derived through lactation curve modeling, such as lactation peak and persistency. Furthermore, novel traits such as resilience indicators can be derived based on the variability of the deviations of observed milk yield from the expected lactation curve fitted for each animal. Lactation curve parameters are heritable, indicating that one can modify the average lactation curve of a population through selective breeding. Various statistical methods can be used for modeling longitudinal traits. Among them, the use of random regression models enables a more flexible and robust modeling of lactation curves compared with traditional models used to evaluate accumulated milk 305-d yield, as they enable the estimation of both genetic and environmental effects affecting milk production traits over time. In this symposium review, we discuss the importance of evaluating lactation curves from a longitudinal perspective and various statistical and mathematical models used to analyze longitudinal data. We also highlighted the key factors that influence milk production over time, and the potential applications of longitudinal analyses of lactation curves in improving animal health, resilience, and milk production efficiency. Overall, analyzing the longitudinal nature of milk yield will continue to play a crucial role in improving the production efficiency and sustainability of the dairy industry, and the methods and models developed can be easily translated to other longitudinal traits.

Estimation of genetic parameters for feed efficiency traits using random regression models in dairy cattle

Feed efficiency has become an increasingly important research topic in recent years. As feed costs rise and the environmental impacts of agriculture become more apparent, improving the efficiency with which dairy cows convert feed to milk is increasingly important. However, feed intake is expensive to measure accurately on large populations, making the inclusion of this trait in breeding programs difficult. Understanding how the genetic parameters of feed efficiency and traits related to feed efficiency vary throughout the lactation period is valuable to gain understanding into the genetic nature of feed efficiency. This study used 121,226 dry matter intake (DMI) records, 120,500 energy-corrected milk (ECM) records, and 98,975 metabolic body weight (MBW) records, collected on 7,440 first-lactation Holstein cows from 6 countries (Canada, Denmark, Germany, Spain, Switzerland, and the United States), from January 2003 to February 2022. Genetic parameters were estimated using a multiple-trait random regression model with a fourth-order Legendre polynomial for all traits. Weekly phenotypes for DMI were re-parameterized using linear regressions of DMI on ECM and MBW, creating a measure of feed efficiency that was genetically corrected for ECM and MBW, referred to as genomic residual feed intake (gRFI). Heritability (SE) estimates varied from 0.15 (0.03) to 0.29 (0.02) for DMI, 0.24 (0.01) to 0.29 (0.03) for ECM, 0.55 (0.03) to 0.83 (0.05) for MBW, and 0.12 (0.03) to 0.22 (0.06) for gRFI. In general, heritability estimates were lower in the first stage of lactation compared with the later stages of lactation. Additive genetic correlations between weeks of lactation varied, with stronger correlations between weeks of lactation that were close together. The results of this study contribute to a better understanding of the change in genetic parameters across the first lactation, providing insight into potential selection strategies to include feed efficiency in breeding programs.

Understanding Circular RNAs in Health, Welfare, and Productive Traits of Cattle, Goats, and Sheep

Circular RNAs (circRNAs) are unique noncoding RNA molecules, notable for their covalent closed-loop structures, which play a crucial role in regulating gene expression across a variety of biological processes. This review comprehensively synthesizes the existing knowledge of circRNAs in three key livestock species: Bos taurus (cattle), Ovis aries (sheep), and Capra hircus (goats). It focuses on their functional importance and emerging potential as biomarkers for disease detection, stress response, and overall physiological health. Specifically, it delves into the expression and functionality of circRNAs in these species, paying special attention to traits critical to livestock productivity such as milk production, meat quality, muscle development, wool production, immune responses, etc. We also address the current challenges faced in circRNA research, including the need for standardized methodologies and broader studies. By providing insights into the molecular mechanisms regulated by circRNAs, this review underscores their scientific and economic relevance in the livestock industry. The potential of circRNAs to improve animal health management and the quality of animal-derived products aligns with growing consumer concerns for animal welfare and sustainability. Thus, this paper aims to guide future research directions while supporting the development of innovative strategies in livestock management and breeding.

Identification and characterization of long non-coding RNAs in mammary gland tissues of Chinese Holstein cows

LncRNAs (Long non-coding RNA) is an RNA molecule with a length of more than 200 bp. LncRNAs can directly act on mRNA, thus affecting the expression of downstream target genes and proteins, and widely participate in many important physiological and pathological regulation processes of the body. In this study, RNA-Seq was performed to detect lncRNAs from mammary gland tissues of three Chinese Holstein cows, including three cows at 7 d before calving and the same three cows at 30 d postpartum (early lactation stage). A total of 1,905 novel lncRNAs were detected, 57.3% of the predicted lncRNAs are ≥ 500 bp and 612 lncRNAs are intronic lncRNAs. The exon number of lncRNAs ranged from 2 to 10. A total of 96 lncRNAs were significantly differentially expressed between two stages, of which 47 were upregulated and 49 were downregulated. Pathway analysis found that target genes were mainly concentrated on the ECM-receptor interaction, Jak-STAT signaling pathway, PI3K-Akt signaling pathway, and TGF-beta signaling pathway. This study revealed the expression profile and characteristics of lncRNAs in the mammary gland tissues of Holstein cows at non-lactation and early lactation periods, and provided a basis for studying the functions of lncRNAs in Holstein cows during different lactation periods.

Seasonal and age-related changes in sperm quality of farmed arctic charr (Salvelinus alpinus)

BACKGROUND

Substantial variation in male fertility is regularly observed in farmed Arctic charr. However, detailed investigations of its fluctuation during a reproductive season and across years are lacking. Furthermore, information about the effect of underlying genetic factors influencing sperm quality is scarce. The current study focused on seasonal and age-related factors that may affect sperm quality characteristics in males reared in natural and delayed photoperiods. Animals were sampled three times for two consecutive years, and sperm quality parameters were recorded using a computer-assisted sperm analysis (CASA) system. Thereafter, high-throughput sequencing technologies were applied, aiming to identify genomic regions related to the variation of sperm quality throughout the reproductive season.

RESULTS

An across-season variation in the recorded sperm quality parameters was evident. Overall, 29% and 42% of males from the natural and delayed spawning groups had a highly variable total progressive motility. Males at four years of age showed significantly higher sperm motility and velocities during the early October and November recordings compared to the following year when the same animals were five years of age. On the other hand, the opposite was observed regarding sperm concentration during the last sampling. A genome-wide FST scan detected SNP differentiation among males with high and low variability in total progressive motility (PM) on eight chromosomes (FST > 0.17), Genome wide windows with the highest FST contained SNPs in proximity (within 250 kb up- and downstream distance) to 16 genes with sperm quality biological functions in mammalian species.

CONCLUSION

Our findings provide a detailed view of seasonal, age-related, and genetic effects on sperm quality and can be used to guide decisions on broodstock selection and hatchery management.

Karrow N, Mao Y. Identification and Classification of Long Non-Coding RNAs in the Mammary Gland of the Holstein Cow

The mammary glands, responsible for milk secretion, are regulated at a local level by various hormones, growth factors, non-coding RNAs, and other elements. Recent research has discovered the presence of lncRNAs in these glands, with suggestions that they may be essential for the maintenance and function of mammary glands. Besides directly controlling the gene and protein expression, lncRNAs are believed to play a significant part in numerous physiological and pathological processes. This study focused on examining the mammary gland tissues of Chinese Holstein cows, to identify and categorize long non-coding RNAs (lncRNAs). The research intended to distinguish lncRNAs in the mammary tissues of Holstein cows and contrast them between lactation and non-lactation periods. In this study, mammary gland tissues were sampled from three Holstein cows in early lactation (n = 3, 30 days postpartum) and non-lactation (n = 3, 315 days postpartum) on a large dairy farm in Jiangsu province. Mammary tissue samples were collected during early lactation and again during non-lactation. In total, we detected 1905 lncRNAs, with 57.3% being 500 bp and 612 intronic lncRNAs. The exon count for lncRNAs varied from 2 to 10. It was observed that 96 lncRNA expressions markedly differed between the two stages, with 83 genes being upregulated and 53 downregulated. Enrichment analysis results revealed that Gene Ontology (GO) analysis was primarily abundant in cellular processes. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that target genes were predominantly abundant in metabolic pathways, fatty acid biosynthesis, the immune system, and glycosphingolipid biosynthesis. This study analyzed the expression profile and characteristics of lncRNAs in the mammary gland tissues of Holstein cows during both lactation and non-lactation stages, forming a foundation for further investigation into the functional roles of lncRNAs in Holstein cows throughout lactation.

Influence of BoLA-DRB3 Polymorphism and Bovine Leukemia Virus (BLV) Infection on Dairy Cattle Productivity

Enzootic bovine leukosis caused by the bovine leukemia virus (BLV) results in substantial damage to the livestock industry; however, we lack an effective cure or vaccine. BoLA-DRB3 polymorphism in BLV-infected cattle is associated with the proviral load (PVL), infectivity in the blood, development of lymphoma, and in utero infection of calves. Additionally, it is related to the PVL, infectivity, and anti-BLV antibody levels in milk. However, the effects of the BoLA-DRB3 allele and BLV infection on dairy cattle productivity remain poorly understood. Therefore, we investigated the effect of BLV infection and BoLA-DRB3 allele polymorphism on dairy cattle productivity in 147 Holstein dams raised on Japanese dairy farms. Our findings suggested that BLV infection significantly increased milk yield. Furthermore, the BoLA-DRB3 allele alone, and the combined effect of BLV infection and the BoLA-DRB3 allele had no effect. These results indicate that on-farm breeding and selection of resistant cattle, or the preferential elimination of susceptible cattle, does not affect dairy cattle productivity. Additionally, BLV infection is more likely to affect dairy cattle productivity than BoLA-DRB3 polymorphism.

Influence of lactation stage on heat production and macronutrient oxidation in dairy cows during a 24-hour fasting period

Understanding nutrient utilization and partitioning is essential for advancing the efficiency of dairy cattle. Our objective was to determine if dairy cows exposed to a 24-h fasting period differ in heat production (HP) and macronutrient oxidation at different stages of lactation. Twelve primiparous, lactating German Holstein dairy cows were used in a longitudinal study design spanning from 2013 to 2014. Dairy cows were housed in respiration chambers during 3 stages of the lactation cycle: early (mean ± SD; 28.8 ± 6.42 d), mid- (89.4 ± 4.52 d), and late (293 ± 7.76 d) lactation. Individual CO₂, O₂, and CH₄ gas exchanges were measured every 6 min for two 24-h periods, an ad libitum period and fasting period (RES). Blood was sampled at the start and end of the RES period. Gas measurements were used to calculate HP, net carbohydrate oxidation (COX), and net fat oxidation (FOX). Measurements were corrected with metabolic BW (kg of BW^0.75; cBW). The RES period for each stage of lactation was further subdivided into the start (RES_start) and end (RES_end) by averaging the first and last 2 h of the RES period. The net change was calculated as RES_end - RES_start. All energy variables differed among lactation stage within the RES period except for HP/cBW. As expected, COX, COX/cBW, COX/HP, HP, and HP/cBW, were greater at the RES_start compared with RES_end, whereas FOX, FOX/cBW, and FOX/HP were greater at the RES_end except for FOX and FOX/cBW during mid lactation, which was only a tendency for a difference. The net change for COX, COX/cBW, HP, HP/cBW, and FOX/cBW did not differ among stages of lactation. Despite detecting a tendency for a difference among stage of lactation for FOX, pairwise analysis revealed no differences. Plasma triglyceride, urea, and nonesterified fatty acid concentrations were greater at RES_end than RES_start. The net change for plasma glucose, urea, β-hydroxybutyrate, and nonesterified fatty acid concentrations were greater in early than late lactation. Our results demonstrate that despite differences in absolute measurements of energy variables and plasma metabolites, the change in whole-body macronutrient oxidation and HP as cows' transition from a fed-like state to a starvation-like state during a 24-h fasting period is consistent throughout lactation.

Intra- and Interspecies RNA-Seq Based Variants in the Lactation Process of Ruminants

The RNA-Seq data provides new opportunities for the detection of transcriptome variants' single nucleotide polymorphisms (SNPs) in various species and tissues. Herein, milk samples from two sheep breeds and two cow breeds were utilized to characterize the genetic variation in the coding regions in three stages (before-peak (BP), peak (P), and after-peak (AP)) of the lactation process. In sheep breeds Assaf and Churra, 100,462 and 97,768, 65,996 and 62,161, and 78,656 and 39,245 variants were observed for BP, P, and AP lactation stages, respectively. The number of specific variants was 59,798 and 76,419, 11,483 and 49,210, and 104,033 and 320,817 in cow breeds Jersy and Kashmiri, respectively, for BP, P, and AP stages. Via the transcriptome analysis of variation in regions containing QTL for fat, protein percentages, and milk yield, we detected a number of pathways and genes harboring mutations that could influence milk production attributes. Many SNPs detected here can be regarded as appropriate markers for custom SNP arrays or genotyping platforms to conduct association analyses among commercial populations. The results of this study offer new insights into milk production genetic mechanisms in cow and sheep breeds, which can contribute to designing suitable breeding systems for optimal milk production.

Exploring milk loss and variability during environmental perturbations across lactation stages as resilience indicators in Holstein cattle

Genetic selection for resilience is essential to improve the long-term sustainability of the dairy cattle industry, especially the ability of cows to maintain their level of production when exposed to environmental disturbances. Recording of daily milk yield provides an opportunity to develop resilience indicators based on milk losses and fluctuations in daily milk yield caused by environmental disturbances. In this context, our study aimed to explore milk loss traits and measures of variability in daily milk yield, including log-transformed standard deviation of milk deviations (Lnsd), lag-1 autocorrelation (Ra), and skewness of the deviations (Ske), as indicators of general resilience in dairy cows. The unperturbed dynamics of milk yield as well as milk loss were predicted using an iterative procedure of lactation curve modeling. Milk fluctuations were defined as a period of at least 10 successive days of negative deviations in which milk yield dropped at least once below 90% of the expected values. Genetic parameters of these indicators and their genetic correlation with economically important traits were estimated using single-trait and bivariate animal models and 8,935 lactations (after quality control) from 6,816 Chinese Holstein cows. In general, cows experienced an average of 3.73 environmental disturbances with a milk loss of 267 kg of milk per lactation. Each fluctuation lasted for 19.80 ± 11.46 days. Milk loss traits are heritable with heritability estimates ranging from 0.004 to 0.061. The heritabilities differed between Lnsd (0.135-0.250), Ra (0.008-0.058), and Ske (0.001-0.075), with the highest heritability estimate of 0.250 ± 0.020 for Lnsd when removing the first and last 10 days in milk in a lactation (Lnsd2). Based on moderate to high genetic correlations, lower Lnsd2 is associated with less milk losses, better reproductive performance, and lower disease incidence. These findings indicate that among the variables evaluated, Lnsd2 is the most promising indicator for breeding for improved resilience in Holstein cattle.

Milking It for All It's Worth: The Effects of Environmental Enrichment on Maternal Nurturance, Lactation Quality, and Offspring Social Behavior

Breastfeeding confers robust benefits to offspring development in terms of growth, immunity, and neurophysiology. Similarly, improving environmental complexity, i.e., environmental enrichment (EE), contributes developmental advantages to both humans and laboratory animal models. However, the impact of environmental context on maternal care and milk quality has not been thoroughly evaluated, nor are the biological underpinnings of EE on offspring development understood. Here, Sprague Dawley rats were housed and bred in either EE or standard-housed (SD) conditions. EE dams gave birth to a larger number of pups, and litters were standardized and cross-fostered across groups on postnatal day (P)1. Maternal milk samples were then collected on P1 (transitional milk phase) and P10 (mature milk phase) for analysis. While EE dams spent less time nursing, postnatal enrichment exposure was associated with heavier offspring bodyweights. Milk from EE mothers had increased triglyceride levels, a greater microbiome diversity, and a significantly higher abundance of bacterial families related to bodyweight and energy metabolism. These differences reflected comparable transcriptomic changes at the genome-wide level. In addition to changes in lactational quality, we observed elevated levels of cannabinoid receptor 1 in the hypothalamus of EE dams, and sex-dependent and time-dependent effects of EE on offspring social behavior. Together, these results underscore the multidimensional impact of the combined neonatal and maternal environments on offspring development and maternal health. Moreover, they highlight potential deficiencies in the use of "gold standard" laboratory housing in the attempt to design translationally relevant animal models in biomedical research.

Malonyl/Acetyltransferase (MAT) Knockout Decreases Triacylglycerol and Medium-Chain Fatty Acid Contents in Goat Mammary Epithelial Cells

Malonyl/acetyltransferase (MAT) is a crucial functional domain of fatty acid synthase (FASN), which plays a vital role in the de novo synthesis of fatty acids in vivo. Milk fatty acids are secreted by mammary epithelial cells. Mammary epithelial cells are the units of mammary gland development and function, and it is a common model for the study of mammary gland tissue development and lactation. This study aimed to investigate the effects of MAT deletion on the synthesis of triacylglycerol and medium-chain fatty acids. The MAT domain was knocked out by CRISPR/Cas9 in the goat mammary epithelial cells (GMECs), and in MAT knockout GMECs, the mRNA level of FASN was decreased by approximately 91.19% and the protein level decreased by 51.83%. The results showed that MAT deletion downregulated the contents of triacylglycerol and medium-chain fatty acids (p < 0.05) and increased the content of acetyl-Coenzyme A (acetyl-CoA) (p < 0.001). Explicit deletion of MAT resulted in significant drop of FASN, which resulted in downregulation of LPL, GPAM, DGAT2, PLIN2, XDH, ATGL, LXRα, and PPARγ genes in GMECs (p < 0.05). Meanwhile, mRNA expression levels of ACC, FASN, DGAT2, SREBP1, and LXRα decreased following treatment with acetyl-CoA (p < 0.05). Our data reveals that FASN plays critical roles in the synthesis of medium-chain fatty acids and triacylglycerol in GMECs.

Candidate Genes and Their Expressions Involved in the Regulation of Milk and Meat Production and Quality in Goats (Capra hircus)

Simple Summary

During the present decade, highly selected caprine farming has increased in popularity due to the hardiness and adaptability inherent to goats. Recent advances in genetics have enabled the improvement in goat selection efficiency. The present review explores how genetic technologies have been applied to the goat-farming sector in the last century. The main candidate genes related to economically relevant traits are reported. The major source of income in goat farming derives from the sale of milk and meat. Consequently, yield and quality must be specially considered. Meat-related traits were evaluated considering three functional groups (weight gain, carcass quality and fat profile). Milk traits were assessed in three additional functional groups (milk production, protein and fat content).

Abstract

Despite their pivotal position as relevant sources for high-quality proteins in particularly hard environmental contexts, the domestic goat has not benefited from the advances made in genomics compared to other livestock species. Genetic analysis based on the study of candidate genes is considered an appropriate approach to elucidate the physiological mechanisms involved in the regulation of the expression of functional traits. This is especially relevant when such functional traits are linked to economic interest. The knowledge of candidate genes, their location on the goat genetic map and the specific phenotypic outcomes that may arise due to the regulation of their expression act as a catalyzer for the efficiency and accuracy of goat-breeding policies, which in turn translates into a greater competitiveness and sustainable profit for goats worldwide. To this aim, this review presents a chronological comprehensive analysis of caprine genetics and genomics through the evaluation of the available literature regarding the main candidate genes involved in meat and milk production and quality in the domestic goat. Additionally, this review aims to serve as a guide for future research, given that the assessment, determination and characterization of the genes associated with desirable phenotypes may provide information that may, in turn, enhance the implementation of goat-breeding programs in future and ensure their sustainability.

Challenging Sustainable and Innovative Technologies in Cheese Production: A Review

It is well known that cheese yield and quality are affected by animal genetics, milk quality (chemical, physical, and microbiological), production technology, and the type of rennet and dairy cultures used in production. Major differences in the same type of cheese (i.e., hard cheese) are caused by the rennet and dairy cultures, which affect the ripening process. This review aims to explore current technological advancements in animal genetics, methods for the isolation and production of rennet and dairy cultures, along with possible applications of microencapsulation in rennet and dairy culture production, as well as the challenge posed to current dairy technologies by the preservation of biodiversity. Based on the reviewed scientific literature, it can be concluded that innovative approaches and the described techniques can significantly improve cheese production.

The Role of microRNAs in the Mammary Gland Development, Health, and Function of Cattle, Goats, and Sheep

Milk is an integral and therefore complex structural element of mammalian nutrition. Therefore, it is simple to conclude that lactation, the process of producing milk, is as complex as the mammary gland, the organ responsible for this biochemical activity. Nutrition, genetics, epigenetics, disease pathogens, climatic conditions, and other environmental variables all impact breast productivity. In the last decade, the number of studies devoted to epigenetics has increased dramatically. Reports are increasingly describing the direct participation of microRNAs (miRNAs), small noncoding RNAs that regulate gene expression post-transcriptionally, in the regulation of mammary gland development and function. This paper presents a summary of the current state of knowledge about the roles of miRNAs in mammary gland development, health, and functions, particularly during lactation. The significance of miRNAs in signaling pathways, cellular proliferation, and the lipid metabolism in agricultural ruminants, which are crucial in light of their role in the nutrition of humans as consumers of dairy products, is discussed.

Bovine milk somatic cell transcriptomic response to Staphylococcus aureus is dependent on strain genotype

Background

Mastitis is an economically important disease of dairy cows with Staphylococcus aureus a major cause worldwide. Challenge of Holstein-Friesian cows demonstrated that S. aureus strain MOK124, which belongs to Clonal Complex (CC)151, caused clinical mastitis, while strain MOK023, belonging to CC97, caused mild or subclinical mastitis. The aim of this study was to elucidate the molecular mechanisms of the host immune response utilising a transcriptomic approach. Milk somatic cells were collected from cows infected with either S. aureus MOK023 or MOK124 at 0, 24, 48, 72 and 168 h post-infection (hpi) and analysed for differentially expressed (DE) genes in response to each strain.

Results

In response to MOK023, 1278, 2278, 1986 and 1750 DE genes were found at 24, 48, 72 and 168 hpi, respectively, while 2293, 1979, 1428 and 1544 DE genes were found in response to MOK124 at those time points. Genes involved in milk production (CSN1, CSN10, CSN1S2, CSN2, a-LACTA and PRLR) were downregulated in response to both strains, with a more pronounced decrease in the MOK124 group. Immune response pathways such as NF-κB and TNF signalling were overrepresented in response to both strains at 24 hpi. These immune pathways continued to be overrepresented in the MOK023 group at 48 and 72 hpi, while the Hippo signalling, extracellular matrix interaction (ECM) and tight junction pathways were overrepresented in the MOK124 group between 48 and 168 hpi. Cellular composition analysis demonstrated that a neutrophil response was predominant in response to MOK124, while M1 macrophages were the main milk cell type post-infection in the MOK023 group.

Conclusions

A switch from immune response pathways to pathways involved in maintaining the integrity of the epithelial cell layer was observed in the MOK124 group from 48 hpi, which coincided with the occurrence of clinical signs in the infected animals. The higher proportion of M1 macrophages in the MOK023 group and lack of substantial neutrophil recruitment in response to MOK023 may indicate immune evasion by this strain. The results of this study highlight that the somatic cell transcriptomic response to S. aureus is dependent on the genotype of the infecting strain.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08135-7.

Genetic evaluation of growth using random regression models

The variability in growth traits provides enormous scope for improvement through selection and breeding. However, growth is a longitudinal trait measured repeatedly on the animal and random regression models (RRM) have been found to be suitable for modeling the trait as a growth curve. RRM accommodate repeated records for traits which change gradually and continually, over time, and do not require stringent assumptions about constancy of variances and correlations. RRM has the advantage that, variance components can be estimated for any point in the trajectory of the growth curve and genetic parameters could be estimated for any age class within the range of ages included in the study. RRM is suitable for group breeding schemes and field performance recording systems where the growth data will be uneven and for varying age points. Worldwide, several studies on use of the tool, RRM in growth of various livestock species are available, but literature on such studies is scanty from India. The methodology used, data requirement, assumptions, validity, software available and application of RRM in the field are discussed based on the earlier reports.

Animal development in the secondary classroom: linking basic science to livestock production

The field of life sciences encompasses a myriad of disciplines that collectively provide insight toward the intrinsic framework of life. Developmental physiology is one of these disciplines that can describe the origins of life at the molecular, cellular, tissue, and organismal level. However, organismal development is a continual process that transcends conception and progresses throughout the lifetime of an organism. In this Illumination, we discuss opportunities that secondary-level life science educators have when teaching developmental physiology through an agricultural lens. Specifically, we propose teaching about the origins of meat and milk, as a nontraditional approach for introducing developmental physiology to students. To justify this notion, we explore how novel research in livestock production focuses on meeting food demands imposed by our growing global population. In addition, we link these concepts to commonly employed standards in secondary-level science classrooms across the United States. In conclusion, the science of livestock production provides a window of opportunity for secondary-level physiology instructors to teach developmental physiology in a form that can readily adhere to institutionally employed standards.

Genomic studies of milk-related traits in water buffalo (Bubalus bubalis) based on single-step genomic best linear unbiased prediction and random regression models

Genomic selection has been widely implemented in many livestock breeding programs, but it remains incipient in buffalo. Therefore, this study aimed to (1) estimate variance components incorporating genomic information in Murrah buffalo; (2) evaluate the performance of genomic prediction for milk-related traits using single- and multitrait random regression models (RRM) and the single-step genomic best linear unbiased prediction approach; and (3) estimate longitudinal SNP effects and candidate genes potentially associated with time-dependent variation in milk, fat, and protein yields, as well as somatic cell score (SCS) in multiple parities. The data used to estimate the genetic parameters consisted of a total of 323,140 test-day records. The average daily heritability estimates were moderate (0.35 ± 0.02 for milk yield, 0.22 ± 0.03 for fat yield, 0.42 ± 0.03 for protein yield, and 0.16 ± 0.03 for SCS). The highest heritability estimates, considering all traits studied, were observed between 20 and 280 d in milk (DIM). The genetic correlation estimates at different DIM among the evaluated traits ranged from -0.10 (156 to 185 DIM for SCS) to 0.61 (36 to 65 DIM for fat yield). In general, direct selection for any of the traits evaluated is expected to result in indirect genetic gains for milk yield, fat yield, and protein yield but also increase SCS at certain lactation stages, which is undesirable. The predicted RRM coefficients were used to derive the genomic estimated breeding values (GEBV) for each time point (from 5 to 305 DIM). In general, the tuning parameters evaluated when constructing the hybrid genomic relationship matrices had a small effect on the GEBV accuracy and a greater effect on the bias estimates. The SNP solutions were back-solved from the GEBV predicted from the Legendre random regression coefficients, which were then used to estimate the longitudinal SNP effects (from 5 to 305 DIM). The daily SNP effect for 3 different lactation stages were performed considering 3 different lactation stages for each trait and parity: from 5 to 70, from 71 to 150, and from 151 to 305 DIM. Important genomic regions related to the analyzed traits and parities that explain more than 0.50% of the total additive genetic variance were selected for further analyses of candidate genes. In general, similar potential candidate genes were found between traits, but our results suggest evidence of differential sets of candidate genes underlying the phenotypic expression of the traits across parities. These results contribute to a better understanding of the genetic architecture of milk production traits in dairy buffalo and reinforce the relevance of incorporating genomic information to genetically evaluate longitudinal traits in dairy buffalo. Furthermore, the candidate genes identified can be used as target genes in future functional genomics studies.

Factors influencing milk osteopontin concentration based on measurements from Danish Holstein cows

Our objective was to determine the content of the bioactive protein osteopontin (OPN) in bovine milk and identify factors influencing its concentration. OPN is expressed in many tissues and body fluids, with by far the highest concentrations in milk. OPN plays a role in immunological and developmental processes and it has been associated with several milk production traits and lactation persistency in cows. In the present study, we report the development of an enzyme linked immunosorbent assay (ELISA) for measurement of OPN in bovine milk. The method was used to determine the concentration of OPN in milk from 661 individual Danish Holstein cows. The median OPN level was determined to 21.9 mg/l with a pronounced level of individual variation ranging from 0.4 mg/l to 67.8 mg/l. Breeding for increased OPN in cow's milk is of significant interest, however, the heritability of OPN in milk was found to be relatively low, with an estimated value of 0.19 in the current dataset. The variation explained by the herd was also found to be low suggesting that OPN levels are not affected by farm management or feeding. Interestingly, the concentration of OPN was found to increase with days in milk and to decrease with parity.

Comparison of three methodologies for the genetic study of lactation persistency in Holstein cattle from Antioquia

Persistency is the rate of decrease after milk production peak, mathematical models such as Wood's can be used to estimate it for describing the lactation curve and its rate of descent; random regression models are also useful, as they describe the genetic lactation curve for each animal. The objective of this study was to compare Best Linear Unbiased Prediction (BLUP), marker-assisted BLUP (MBLUP) model and random regression model (RRM) to estimate genetic parameters and breeding values for the lactation persistency curve. 4,658 test day measurements were available for 733 individuals, from which lactation curves were described to calculate persistency, estimating genetic parameters and values for this trait through BLUP and MBLUP. A similar process was done for RRM, where persistency was estimated from the genetic lactation curve. The heritability obtained using RRM was 0.51, greater than that obtained by BLUP (0.29) and MBLUP (0.21). The reliability of the genetic value for persistency in bulls was greater when RRM was used, but there was no correlation between the genetic values of different models. The highest heritability for persistency and the more reliable genetic values for bulls were achieved under the RRM, it allows positioning this methodology as an important tool for genetic evaluation of persistency.

Weighted gene co-expression network analysis identifies modules and functionally enriched pathways in the lactation process

The exponential growth in knowledge has resulted in a better understanding of the lactation process in a wide variety of animals. However, the underlying genetic mechanisms are not yet clearly known. In order to identify the mechanisms involved in the lactation process, various mehods, including meta-analysis, weighted gene co-express network analysis (WGCNA), hub genes identification, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment at before peak (BP), peak (P), and after peak (AP) stages of the lactation processes have been employed. A total of 104, 85, and 26 differentially expressed genes were identified based on PB vs. P, BP vs. AP, and P vs. AP comparisons, respectively. GO and KEGG pathway enrichment analysis revealed that DEGs were significantly enriched in the "ubiquitin-dependent ERAD" and the "chaperone cofactor-dependent protein refolding" in BP vs. P and P vs. P, respectively. WGCNA identified five significant functional modules related to the lactation process. Moreover, GJA1, AP2A2, and NPAS3 were defined as hub genes in the identified modules, highlighting the importance of their regulatory impacts on the lactation process. The findings of this study provide new insights into the complex regulatory networks of the lactation process at three distinct stages, while suggesting several candidate genes that may be useful for future animal breeding programs. Furthermore, this study supports the notion that in combination with a meta-analysis, the WGCNA represents an opportunity to achieve a higher resolution analysis that can better predict the most important functional genes that might provide a more robust bio-signature for phenotypic traits, thus providing more suitable biomarker candidates for future studies.

Bayesian Analysis of the Association between Casein Complex Haplotype Variants and Milk Yield, Composition, and Curve Shape Parameters in Murciano-Granadina Goats

Considering casein haplotype variants rather than SNPs may maximize the understanding of heritable mechanisms and their implication on the expression of functional traits related to milk production. Effects of casein complex haplotypes on milk yield, milk composition, and curve shape parameters were used using a Bayesian inference for ANOVA. We identified 48 single nucleotide polymorphisms (SNPs) present in the casein complex of 159 unrelated individuals of diverse ancestry, which were organized into 86 haplotypes. The Ali and Schaeffer model was chosen as the best fitting model for milk yield (Kg), protein, fat, dry matter, and lactose (%), while parabolic yield-density was chosen as the best fitting model for somatic cells count (SCC × 103 sc/mL). Peak and persistence for all traits were computed respectively. Statistically significant differences (p < 0.05) were found for milk yield and components. However, no significant difference was found for any curve shape parameter except for protein percentage peak. Those haplotypes for which higher milk yields were reported were the ones that had higher percentages for protein, fat, dry matter, and lactose, while the opposite trend was described by somatic cells counts. Conclusively, casein complex haplotypes can be considered in selection strategies for economically important traits in dairy goats.

Associations between the Bovine Myostatin Gene and Milk Fatty Acid Composition in New Zealand Holstein-Friesian × Jersey-Cross Cows

Simple Summary

The gene that encodes myostatin influences more than one trait, and its expression has been observed in skeletal muscle, as well as the mammary gland. In this study, association analysis revealed that variation in the bovine myostatin gene affects milk fatty acid composition, raising the possibility that this genetic variation may be utilized to increase the amount of unsaturated fatty acid and decrease the amount of saturated fatty acid in milk.

Abstract

The myostatin gene (MSTN), which encodes the protein myostatin, is pleiotropic, and its expression has been associated with both increased and decreased adipogenesis and increased skeletal muscle mass in animals. In this study, the polymerase chain reaction, coupled with single strand conformation polymorphism analysis, was utilized to reveal nucleotide sequence variation in bovine MSTN in 410 New Zealand (NZ) Holstein-Friesian × Jersey (HF × J)-cross cows. These cows ranged from 3 to 9 years of age and over the time studied, produced an average 22.53 ± 2.18 L of milk per day, with an average milk fat content of 4.94 ± 0.17% and average milk protein content of 4.03 ± 0.10%. Analysis of a 406-bp amplicon from the intron 1 region, revealed five nucleotide sequence variants (A–E) that contained seven nucleotide substitutions. Using general linear mixed-effect model analyses the AD genotype was associated with reduced C10:0, C12:0, and C12:1 levels when compared to levels in cows with the AA genotype. These associations in NZ HF × J cross cows are novel, and they suggest that this variation in bovine MSTN could be explored for increasing the amount of milk unsaturated fatty acid and decreasing the amount of saturated fatty acid.

Goat Genomic Resources: The Search for Genes Associated with Its Economic Traits

Goat plays a crucial role in human livelihoods, being a major source of meat, milk, fiber, and hides, particularly under adverse climatic conditions. The goat genomics related to the candidate gene approach is now being used to recognize molecular mechanisms that have different expressions of growth, reproductive, milk, wool, and disease resistance. The appropriate literature on this topic has been reviewed in this article. Several genetic characterization attempts of different goats have reported the existence of genotypic and morphological variations between different goat populations. As a result, different whole-genome sequences along with annotated gene sequences, gene function, and other genomic information of different goats are available in different databases. The main objective of this review is to search the genes associated with economic traits in goats. More than 271 candidate genes have been discovered in goats. Candidate genes influence the physiological pathway, metabolism, and expression of phenotypes. These genes have different functions on economically important traits. Some genes have pleiotropic effect for expression of phenotypic traits. Hence, recognizing candidate genes and their mutations that cause variations in gene expression and phenotype of an economic trait can help breeders look for genetic markers for specific economic traits. The availability of reference whole-genome assembly of goats, annotated genes, and transcriptomics makes comparative genomics a useful tool for systemic genetic upgradation. Identification and characterization of trait-associated sequence variations and gene will provide powerful means to give positive influences for future goat breeding program.

Transcriptome signature of two lactation stages in Ghezel sheep identifies using RNA-Sequencing

The expression of genes and their regulation during lactation in Ghezel sheep breed remains less understood. To explore the underlying molecular mechanism of the lactation process in the mammary gland, transcriptome profiles of Iranian fat-tailed Ghezel sheep breed milk at two stages, before (BF) and after peak (AF) stages of lactation were investigated. Functional impacts of differentially expressed genes (DEGs) between BF and AF stages were surveyed using Gene Ontology (GO) and Protein-Protein Interaction (PPI) network analysis. Totally, 75 DEGs were identified between BF and AF stages of lactation. The RNA-Seq results were validated by Q-RT-PCR. Gene ontology of DEGs mainly enriched in metabolic process and oxidative phosphorylation. PPI network analysis also highlighted the contribution of peroxisome proliferator-activated receptors (PPAR) signaling, oxidative phosphorylation and metabolic pathways in the lactation process. Intriguingly, the genes involved in fat metabolism dominantly down-regulated at AF stage. Our results provide new insight into transcriptional changes and add to growing body of knowledge on the lactation process in fat-tailed sheep breeds.

Genome-wide association study for genotype by lactation stage interaction of milk production traits in dairy cattle

Substantial evidence demonstrates that the genetic background of milk production traits changes during lactation. However, most GWAS for milk production traits assume that genetic effects are constant during lactation and therefore might miss those quantitative trait loci (QTL) whose effects change during lactation. The GWAS for genotype by lactation stage interaction are aimed at explicitly detecting the QTL whose effects change during lactation. The purpose of this study was to perform GWAS for genotype by lactation stage interaction for milk yield, lactose yield, lactose content, fat yield, fat content, protein yield, and somatic cell score to detect QTL with changing effects during lactation. For this study, 19,286 test-day records of 1,800 first-parity Dutch Holstein cows were available and cows were genotyped using a 50K SNP panel. A total of 7 genomic regions with effects that change during lactation were detected in the GWAS for genotype by lactation stage interaction. Two regions on Bos taurus autosome (BTA)14 and BTA19 were also significant based on a GWAS that assumed constant genetic effects during lactation. Five regions on BTA4, BTA10, BTA11, BTA16, and BTA23 were only significant in the GWAS for genotype by lactation stage interaction. The biological mechanisms that cause these changes in genetic effects are still unknown, but negative energy balance and effects of pregnancy may play a role. These findings increase our understanding of the genetic background of lactation and may contribute to the development of better management indicators based on milk composition.

Single-step genome-wide association for longitudinal traits of Canadian Ayrshire, Holstein, and Jersey dairy cattle

Estimating single nucleotide polymorphism (SNP) effects over time is essential to identify and validate candidate genes (or quantitative trait loci) associated with time-dependent variation of economically important traits and to better understand the underlying mechanisms of lactation biology. Therefore, in this study, we aimed to estimate time-dependent effects of SNP and identifying candidate genes associated with milk (MY), fat (FY), and protein (PY) yields, and somatic cell score (SCS) in the first 3 lactations of Canadian Ayrshire, Holstein, and Jersey breeds, as well as suggest their potential pattern of phenotypic effect over time. Random regression coefficients for the additive direct genetic effect were estimated for each animal using single-step genomic BLUP, based on 2 random regression models: one considering MY, FY, and PY in the first 3 lactations and the other considering SCS in the first 3 lactations. Thereafter, SNP solutions were obtained for random regression coefficients, which were used to estimate the SNP effects over time (from 5 to 305 d in lactation). The top 1% of SNP that showed a high magnitude of SNP effect in at least 1 d in lactation were selected as relevant SNP for further analyses of candidate genes, and clustered according to the trajectory of their SNP effects over time. The majority of SNP selected for MY, FY, and PY increased the magnitude of their effects over time, for all breeds. In contrast, for SCS, most selected SNP decreased the magnitude of their effects over time, especially for the Holstein and Jersey breeds. In general, we identified a different set of candidate genes for each breed, and similar genes were found across different lactations for the same trait in the same breed. For some of the candidate genes, the suggested pattern of phenotypic effect changed among lactations. Among the lactations, candidate genes (and their suggested phenotypic effect over time) identified for the second and third lactations were more similar to each other than for the first lactation. Well-known candidate genes with major effects on milk production traits presented different suggested patterns of phenotypic effect across breeds, traits, and lactations in which they were identified. The candidate genes identified in this study can be used as target genes in studies of gene expression.

Polymorphism association and expression analysis of alpha-lactalbumin (LALBA) gene during lactation in Nili Ravi buffalo

Alpha-lactalbumin has been reported as a highly polymorphic gene that potentially alters the gene expression and is associated with milk composition in dairy breeds. Current study was conducted in two phases. In the first phase, polymorphisms identification in alpha-lactalbumin (LALBA) gene and its association with milk composition was performed. To identify the genetic polymorphism, Nili Ravi buffaloes at their second lactation were selected from Government livestock farm (Buffalo Research Institute, Pattoki). Genomic DNA was extracted from blood samples. After PCR amplification, products were sequenced, and data was analyzed. Results showed that the identified polymorphisms at chromosomal position 34310940 were found associated with major whey protein. In the second phase of study, milk samples were collected from five healthy mastitis-free Nili Ravi buffaloes in their second lactation for expression analysis of alpha-lactalbumin gene at their transition (day 15), mid (day 90), and late (day 250) lactation. Gene expression was observed highest in transition phase with a gradual decrease of expression in mid and late phase of lactation. Further studies are needed to explore the regulation of milk production genes and their translational efficiency during the course of lactation.

Invited review: Advances and applications of random regression models: From quantitative genetics to genomics

An important goal in animal breeding is to improve longitudinal traits; that is, traits recorded multiple times during an individual's lifetime or physiological cycle. Longitudinal traits were first genetically evaluated based on accumulated phenotypic expression, phenotypic expression at specific time points, or repeatability models. Until now, the genetic evaluation of longitudinal traits has mainly focused on using random regression models (RRM). Random regression models enable fitting random genetic and environmental effects over time, which results in higher accuracy of estimated breeding values compared with other statistical approaches. In addition, RRM provide insights about temporal variation of biological processes and the physiological implications underlying the studied traits. Despite the fact that genomic information has substantially contributed to increase the rates of genetic progress for a variety of economically important traits in several livestock species, less attention has been given to longitudinal traits in recent years. However, including genomic information to evaluate longitudinal traits using RRM is a feasible alternative to yield more accurate selection and culling decisions, because selection of young animals may be based on the complete pattern of the production curve with higher accuracy compared with the use of traditional parent average (i.e., without genomic information). Moreover, RRM can be used to estimate SNP effects over time in genome-wide association studies. Thus, by analyzing marker associations over time, regions with higher effects at specific points in time are more likely to be identified. Despite the advances in applications of RRM in genetic evaluations, more research is needed to successfully combine RRM and genomic information. Future research should provide a better understanding of the temporal variation of biological processes and their physiological implications underlying the longitudinal traits.

Cohort-level disease prediction by extrapolation of individual-level predictions in transition dairy cattle

Dairy cattle experience metabolic stress during the transition from late gestation to early lactation resulting in higher risk for several economically important diseases (e.g. mastitis, metritis, and ketosis). Metabolic stress is described as a physiological state composed of 3 processes: nutrient metabolism, oxidative stress, and inflammation. Current strategies for monitoring transition cow nutrient metabolism include assessment of plasma non-esterified fatty acids and beta-hydroxybutyrate concentrations around the time of calving. Although this method is effective at identifying cows with higher disease risk, there is often not enough time to implement intervention strategies to prevent health disorders from occurring around the time of calving. Previously, we published predictive models for early lactation diseases at the individual cow level at dry-off. However, it is unknown if predictive probabilities from individual-level models can be aggregated to the cohort level to predict cohort-level incidence. Therefore, our objective was to test different data aggregation methods using previously published models that represented the 3 components of metabolic stress (nutrient metabolism, oxidative stress, and inflammation). We included 277 cows from five Michigan dairy herds for this prospective cohort study. On each farm, two to four calving cohorts were formed, totaling 18 cohorts. We measured biomarker data at dry-off and followed the cows until 30 days post-parturition for cohort disease incidence, which was defined as the number of cows: 1) having one or more clinical transition disease outcome, and/or 2) having an adverse health event (abortion or death of calf or cow) within each cohort. We tested three different aggregation methods that we refer to as the p-central, p-dispersion, and p-count methods. For the p-central method, we calculated the averaged predicted probability within each cohort. For the p-dispersion method, we calculated the standard deviation of the predicted probabilities within a cohort. For the p-count method, we counted the number of cows above a specified threshold of predicted probability within each cohort. We built four sets of models: one for each aggregation method and one that included all three aggregation methods (p-combined method). We found that the p-dispersion method was the only method that produced viable predictive models. However, these models tended to overestimate incidence in cohorts with low observed counts and underestimate risk in cohorts with high observed counts.

Efficient multivariate analysis algorithms for longitudinal genome-wide association studies

Motivation

Current dynamic phenotyping system introduces time as an extra dimension to genome-wide association studies (GWAS), which helps to explore the mechanism of dynamical genetic control for complex longitudinal traits. However, existing methods for longitudinal GWAS either ignore the covariance among observations of different time points or encounter computational efficiency issues.

Results

We herein developed efficient genome-wide multivariate association algorithms for longitudinal data. In contrast to existing univariate linear mixed model analyses, the proposed method has improved statistic power for association detection and computational speed. In addition, the new method can analyze unbalanced longitudinal data with thousands of individuals and more than ten thousand records within a few hours. The corresponding time for balanced longitudinal data is just a few minutes.

Availability and implementation

A software package to implement the efficient algorithm named GMA (https://github.com/chaoning/GMA) is available freely for interested users in relevant fields.

Supplementary information

Supplementary data are available at Bioinformatics online.

Meta-analysis of mammary RNA seq datasets reveals the molecular understanding of bovine lactation biology

A better understanding of the biology of lactation, both in terms of gene expression and the identification of candidate genes for the production of milk and its components, is made possible by recent advances in RNA seq technology. The purpose of this study was to understand the synthesis of milk components and the molecular pathways involved, as well as to identify candidate genes for milk production traits within whole mammary transcriptomic datasets. We performed a meta-analysis of publically available RNA seq transcriptome datasets of mammary tissue/milk somatic cells. In total, 11 562 genes were commonly identified from all RNA seq based mammary gland transcriptomes. Functional annotation of commonly expressed genes revealed the molecular processes that contribute to the synthesis of fats, proteins, and lactose in mammary secretory cells and the molecular pathways responsible for milk synthesis. In addition, we identified several candidate genes responsible for milk production traits and constructed a gene regulatory network for RNA seq data. In conclusion, this study provides a basic understanding of the lactation biology of cows at the gene expression level.

Buffalo milk transcriptome: A comparative analysis of early, mid and late lactation

The expression of genes and their regulation during lactation in buffaloes remains less understood. To understand the interplay of various genes and pathways, the milk transcriptome from three lactation stages of Murrah buffalo was analyzed by RNA sequencing. The filtered reads were mapped to the Bubalus bubalis as well as Bos taurus reference assemblies. The average mapping rate to water buffalo and Btau 4.6 reference sequence, was 75.5% and 75.7% respectively. Highly expressed genes (RPKM > 3000), throughout lactation included CSN2, CSN1S1, CSN3, LALBA, SPP1 and TPT1. A total of 12833 transcripts were common across all the stages, while 271, 205 and 418 were unique to early, mid and late lactation respectively. Majority of the genes throughout lactation were linked to biological functions like protein metabolism, transport and immune response. A discernible shift from metabolism in early stage to metabolism and immune response in mid stage, and an increase in immune response functions in late lactation was observed. The results provide information of candidate genes and pathways involved in the different stages of lactation in buffalo. The study also identified 14 differentially expressed and highly connected genes across the three lactation stages, which can be used as candidates for future research.

Cloning and characterization of Bubaline mammary miRNAs: An in silico approach

MicroRNAs (miRNAs) are small non-coding RNAs ~ 19-25 nucleotides long that are involved in the regulation of gene expression. They negatively regulate the gene expression via inhibition or complete degradation of mRNAs by binding the complementary target sequences in 3' untranslated region. The present investigation was aimed at profiling of miRNAs expressed in the Bubaline mammary tissue at dry stage of lactation cycle. Small RNAs were isolated from freshly collected mammary tissues and T4 RNA ligase was used to ligate the enriched miRNAs with 3' and 5' linker sequences in two separate reactions. cDNA copies were synthesized from linkered small RNAs followed by the PCR amplification. The PCR products were resolved on 15% non-denaturing polyacrylamide gel electrophoresis by gelstar staining. The PCR products were cloned using pGEM®-T easy vector system and the desired clones (with linkered small RNA sequences) were confirmed using restriction digestion of plasmids with EcoRI. Out of 15 Bubaline small RNA sequences, eight sequences (Seq. ID I-VIII) matched the size range of miRNA molecules i.e., 18-26 nucleotides. The Bubaline small RNA sequences II and III showed partial alignment with various mammalian and non-mammalian miRNAs. The small RNA sequences obtained in the present study did not show any perfect match with already reported mRNA, rRNA or tRNA sequences in different databases. Hence, only the Bubaline small RNA sequences that showed partial homology with miRNAs were considered as putative Bubaline miRNAs. The present study established the basic repertoire of miRNAs expressed at dry stage of lactation in Bubaline mammary gland.

Comparative transcriptome analysis of mammary epithelial cells at different stages of lactation reveals wide differences in gene expression and pathways regulating milk synthesis between Jersey and Kashmiri cattle

Jersey and Kashmiri cattle are important dairy breeds that contribute significantly to the total milk production of the Indian northern state of Jammu and Kashmir. The Kashmiri cattle germplasm has been extensively diluted through crossbreeding with Jersey cattle with the goal of enhancing its milk production ability. However, crossbred animals are prone to diseases resulting to unsustainable milk production. This study aimed to provide a comprehensive transcriptome profile of mammary gland epithelial cells at different stages of lactation and to find key differences in genes and pathways regulating milk traits between Jersey and Kashmiri cattle. Mammary epithelial cells (MEC) isolated from milk obtained from six lactating cows (three Jersey and three Kashmiri cattle) on day 15 (D15), D90 and D250 in milk, representing early, mid and late lactation, respectively were used. RNA isolated from MEC was subjected to next-generation RNA sequencing and bioinformatics processing. Casein and whey protein genes were found to be highly expressed throughout the lactation stages in both breeds. Largest differences in differentially expressed genes (DEG) were between D15 vs D90 (1,805 genes) in Kashmiri cattle and, D15 vs D250 (3,392 genes) in Jersey cattle. A total of 1,103, 1,356 and 1,397 genes were differentially expressed between Kashmiri and Jersey cattle on D15, D90 and D250, respectively. Antioxidant genes like RPLPO and RPS28 were highly expressed in Kashmiri cattle. Differentially expressed genes in both Kashmiri and Jersey were enriched for multicellular organismal process, receptor activity, catalytic activity, signal transducer activity, macromolecular complex and developmental process gene ontology terms. Whereas, biological regulation, endopeptidase activity and response to stimulus were enriched in Kashmiri cattle and, reproduction and immune system process were enriched in Jersey cattle. Most of the pathways responsible for regulation of milk production like JAK-STAT, p38 MAPK pathway, PI3 kinase pathway were enriched by DEG in Jersey cattle only. Although Kashmiri has poor milk production efficiency, the present study suggests possible physicochemical and antioxidant properties of Kashmiri cattle milk that needs to be further explored.

Using geometric morphometrics for the genetics analysis of shape and size of lactation curves in Israeli first-parity Holstein cattle

Our objective was to combine the methods of geometric morphometrics and multivariate quantitative genetics to genetic evaluation of the size and shape of lactation curves of milk of 3,492 Israeli first-parity Holstein cattle. Lactation records were treated as morphological data, for which 2 different lactation shape functions were evaluated, one depicted by a line graph and the other by an orbital graph. The lactation curves from both shape functions were represented by 2-dimensional Cartesian landmark coordinates. The 2 sets of landmarks were then analyzed individually for each shape function with geometric morphometrics to separate variation into components of size and shape. The analysis yielded 2 size measures and 2 sets of shape variables, and they were the inputs to estimate variance components using the MTC REML individual animal model program. Variance components were also estimated for the 305-d lactation production as a reference. Shape variables showed negligible correlation with 305-d production, providing evidence of size and shape of lactation curve as separate characters. The size measure derived from the orbital-depicted lactation curve had equal heritability (0.39 ± 0.01; ± standard error) and complete genetic and environmental correlations with 305-d production, whereas the size measure derived from the line-depicted lactation curve showed low heritability (0.09 ± 0.01) and environmental correlation (0.02 ± 0.004) and relative high genetic correlation with 305-d production (0.48 ± 0.04). This may validate both the orbital graph to depict lactation records and the use of geometric morphometrics to split variation of lactation curve into size and shape components. The maximal heritability for shape of lactation curve was 0.55 for orbital- and 0.56 for line-depicted lactation curves. The respective patterns of variations were visualized as shape changes from the mean shape in the data set. Geometric morphometrics are well grounded within the theory of shape analysis and can be paired with conventional methods in the field to characterize the patterns of phenotypic and genetic variation of shape and size of lactation curve in dairy cattle.

Cross-Species Meta-Analysis of Transcriptomic Data in Combination With Supervised Machine Learning Models Identifies the Common Gene Signature of Lactation Process

Lactation, a physiologically complex process, takes place in mammary gland after parturition. The expression profile of the effective genes in lactation has not comprehensively been elucidated. Herein, meta-analysis, using publicly available microarray data, was conducted identify the differentially expressed genes (DEGs) between pre- and post-peak milk production. Three microarray datasets of Rat, Bos Taurus, and Tammar wallaby were used. Samples related to pre-peak (n = 85) and post-peak (n = 24) milk production were selected. Meta-analysis revealed 31 DEGs across the studied species. Interestingly, 10 genes, including MRPS18B, SF1, UQCRC1, NUCB1, RNF126, ADSL, TNNC1, FIS1, HES5 and THTPA, were not detected in original studies that highlights meta-analysis power in biosignature discovery. Common target and regulator analysis highlighted the high connectivity of CTNNB1, CDD4 and LPL as gene network hubs. As data originally came from three different species, to check the effects of heterogeneous data sources on DEGs, 10 attribute weighting (machine learning) algorithms were applied. Attribute weighting results showed that the type of organism had no or little effect on the selected gene list. Systems biology analysis suggested that these DEGs affect the milk production by improving the immune system performance and mammary cell growth. This is the first study employing both meta-analysis and machine learning approaches for comparative analysis of gene expression pattern of mammary glands in two important time points of lactation process. The finding may pave the way to use of publically available to elucidate the underlying molecular mechanisms of physiologically complex traits such as lactation in mammals.

Highly accurate sequence imputation enables precise QTL mapping in Brown Swiss cattle

Background

Within the last few years a large amount of genomic information has become available in cattle. Densities of genomic information vary from a few thousand variants up to whole genome sequence information. In order to combine genomic information from different sources and infer genotypes for a common set of variants, genotype imputation is required.

Results

In this study we evaluated the accuracy of imputation from high density chips to whole genome sequence data in Brown Swiss cattle. Using four popular imputation programs (Beagle, FImpute, Impute2, Minimac) and various compositions of reference panels, the accuracy of the imputed sequence variant genotypes was high and differences between the programs and scenarios were small. We imputed sequence variant genotypes for more than 1600 Brown Swiss bulls and performed genome-wide association studies for milk fat percentage at two stages of lactation. We found one and three quantitative trait loci for early and late lactation fat content, respectively. Known causal variants that were imputed from the sequenced reference panel were among the most significantly associated variants of the genome-wide association study.

Conclusions

Our study demonstrates that whole-genome sequence information can be imputed at high accuracy in cattle populations. Using imputed sequence variant genotypes in genome-wide association studies may facilitate causal variant detection.

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4390-2) contains supplementary material, which is available to authorized users.

Meta-analysis of sequence-based association studies across three cattle breeds reveals 25 QTL for fat and protein percentages in milk at nucleotide resolution

Background

Genotyping and whole-genome sequencing data have been generated for hundreds of thousands of cattle. International consortia used these data to compile imputation reference panels that facilitate the imputation of sequence variant genotypes for animals that have been genotyped using dense microarrays. Association studies with imputed sequence variant genotypes allow for the characterization of quantitative trait loci (QTL) at nucleotide resolution particularly when individuals from several breeds are included in the mapping populations.

Results

We imputed genotypes for 28 million sequence variants in 17,229 cattle of the Braunvieh, Fleckvieh and Holstein breeds in order to compile large mapping populations that provide high power to identify QTL for milk production traits. Association tests between imputed sequence variant genotypes and fat and protein percentages in milk uncovered between six and thirteen QTL (P < 1e-8) per breed. Eight of the detected QTL were significant in more than one breed. We combined the results across breeds using meta-analysis and identified a total of 25 QTL including six that were not significant in the within-breed association studies. Two missense mutations in the ABCG2 (p.Y581S, rs43702337, P = 4.3e-34) and GHR (p.F279Y, rs385640152, P = 1.6e-74) genes were the top variants at QTL on chromosomes 6 and 20. Another known causal missense mutation in the DGAT1 gene (p.A232K, rs109326954, P = 8.4e-1436) was the second top variant at a QTL on chromosome 14 but its allelic substitution effects were inconsistent across breeds. It turned out that the conflicting allelic substitution effects resulted from flaws in the imputed genotypes due to the use of a multi-breed reference population for genotype imputation.

Conclusions

Many QTL for milk production traits segregate across breeds and across-breed meta-analysis has greater power to detect such QTL than within-breed association testing. Association testing between imputed sequence variant genotypes and phenotypes of interest facilitates identifying causal mutations provided the accuracy of imputation is high. However, true causal mutations may remain undetected when the imputed sequence variant genotypes contain flaws. It is highly recommended to validate the effect of known causal variants in order to assess the ability to detect true causal mutations in association studies with imputed sequence variants.

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4263-8) contains supplementary material, which is available to authorized users.

Traditional Galactagogue Foods and Their Connection to Human Milk Volume in Thai Breastfeeding Mothers

BACKGROUND

Thai traditional galactagogue consumption is still observed today. However, there are few scientific studies that describe this practice. Research aim: The aim of this study was to describe the connection between traditional galactagogue consumption and human milk volume.

METHODS

Self-reported maternal surveys ( N = 36) were conducted of mothers and their infants who breastfeed exclusively. The mothers were interviewed about traditional galactagogue consumption and intake of protein-rich foods using a semiquantitative food-frequency questionnaire. They were also assessed for energy and nutrient intake using the 24-hr dietary recall method. Their infants were between 1 and 3 months of age and were test weighed for 24 hr to measure their mother's own milk volume. Partial correlation was used to test the relationship between galactagogue consumption and milk volume by controlling the infants' birth weight, weight-for-age, maternal energy, and carbohydrate intake.

RESULTS

The results revealed that consumption of some traditional galactagogues was significantly correlated to human milk volume, including banana flower, lemon basil, Thai basil, bottle gourd, and pumpkin ( p < .05). Furthermore, there were significant correlations between consumption of some kinds of protein and milk volume, including egg tofu, chicken, fish, and seafood ( p < .05). Maternal energy and carbohydrate intake were related to milk volume ( p < .05), but protein intake was not.

CONCLUSION

Certain kinds of traditional galactagogues and proteins are associated with human milk volume. However, studies related to the active ingredients in these galactagogues are required to secure a recommendation about use of traditional galactagogues among breastfeeding mothers.

Co-Expression Network and Pathway Analyses Reveal Important Modules of miRNAs Regulating Milk Yield and Component Traits

Co-expression network analyses provide insights into the molecular interactions underlying complex traits and diseases. In this study, co-expression network analysis was performed to detect expression patterns (modules or clusters) of microRNAs (miRNAs) during lactation, and to identify miRNA regulatory mechanisms for milk yield and component traits (fat, protein, somatic cell count (SCC), lactose, and milk urea nitrogen (MUN)) via miRNA target gene enrichment analysis. miRNA expression (713 miRNAs), and milk yield and components (Fat%, Protein%, lactose, SCC, MUN) data of nine cows at each of six different time points (day 30 (D30), D70, D130, D170, D230 and D290) of an entire lactation curve were used. Four modules or clusters (GREEN, BLUE, RED and TURQUOISE) of miRNAs were identified as important for milk yield and component traits. The GREEN and BLUE modules were significantly correlated (|r| > 0.5) with milk yield and lactose, respectively. The RED and TURQUOISE modules were significantly correlated (|r| > 0.5) with both SCC and lactose. In the GREEN module, three abundantly expressed miRNAs (miR-148a, miR-186 and miR-200a) were most significantly correlated to milk yield, and are probably the most important miRNAs for this trait. DDR1 and DDHX1 are hub genes for miRNA regulatory networks controlling milk yield, while HHEX is an important transcription regulator for these networks. miR-18a, miR-221/222 cluster, and transcription factors HOXA7, and NOTCH 3 and 4, are important for the regulation of lactose. miR-142, miR-146a, and miR-EIA17-14144 (a novel miRNA), and transcription factors in the SMAD family and MYB, are important for the regulation of SCC. Important signaling pathways enriched for target genes of miRNAs of significant modules, included protein kinase A and PTEN signaling for milk yield, eNOS and Noth signaling for lactose, and TGF β, HIPPO, Wnt/β-catenin and cell cycle signaling for SCC. Relevant enriched gene ontology (GO)-terms related to milk and mammary gland traits included cell differentiation, G-protein coupled receptor activity, and intracellular signaling transduction. Overall, this study uncovered regulatory networks in which miRNAs interacted with each other to regulate lactation traits.