scholarly journals Polymorphism of CSN1S1 (g.12164G>A) and CSN2 (g.8913C>A) genes in pure and cross dairy goats

2021 ◽  
Vol 33 ◽  
pp. 02001
Author(s):  
Anneke Anggraeni ◽  
Lailatus Syifa ◽  
Oktiq Kurnia Sari ◽  
Andi Baso Lompengeng Ishak ◽  
Cece Sumantri
Keyword(s):  
Protein Content ◽  
Milk Protein ◽  
Dairy Goat ◽  
Selection Marker ◽  
Dairy Goats ◽  
Milk Protein Content ◽  
Pcr Rflp ◽  
Molecular Selection ◽  
Control Milk ◽  
Casein Genes

Casein genes directly control milk protein of animals. CSN1S1 (αS1-Casein) and CSN2 (β-Casein) genes influence on milk protein fractions. Genetic polymorphisms of CSN1S1 gene at g.12164G>A locus and CSN2 gene at g.8913C>A locus were identified by PCR-RFLP technique. Animal samples were pure dairy goats providing PE (5 hds.), Saanen (8 hds.) and their crosses providing Sapera (50% Saanen, 50% PE) (51 hds.) and SaanPE (75% Saanen, 25% PE) (3 hds.) from IRIAP dairy goat station. Allele frequency, genotype frequency, heterozygosity value, and Hardy-Weinberg (H-W) equilibrium value were analyzed by Popgen32 program. CSN1S1_g.12164G>A locus resulted in two alleles, i.e. G allele (192 bp, 145 bp, and 101 bp) and A allele (337 bp and 101 bp). The G allele from the highest frequenciest was successively Saanen (0.625), Sapera (0.578), PE (0.400), and SaanPE (0.333). Most dairy goats were heterozygote (Ho>He) and in H-W equilibrium (q2 count < q2P0.05). Whereas CSN2_g.8913C>A locus was monomorphic for possesing only C allele (233 bp and 162 bp), without A allele (416 bp). The existent g.12164G>A SNP of the CSN1S1 gene of could be a potencial molecular selection marker of milk protein content in dairy goat.

Genetic polymorphism at the CSN1S1 gene in Girgentana dairy goat breed

2013 ◽  
Vol 53 (5) ◽  
pp. 403 ◽  
Author(s):  
S. Mastrangelo ◽  
M. T. Sardina ◽  
M. Tolone ◽  
B. Portolano
Keyword(s):  
Protein Content ◽  
High Frequency ◽  
Low Frequency ◽  
Dairy Goat ◽  
Goat Breed ◽  
Dairy Goats ◽  
Selection Strategies ◽  
Milk Protein Content ◽  
Genotype Information ◽  
Drinking Milk

The aim of this work was to evaluate the variability of the αs1-casein locus in the endangered Girgentana dairy goat breed in order to define genetic improvement and a conservation program for this breed. The study was performed on 200 dairy goats by means of different PCR protocols. The most frequent alleles were A (0.590) and F (0.290) followed by B (0.065) and N (0.047). CSN1S1 E allele was identified with a very low frequency (0.008). The most common genotype was AF (0.365) followed by AA (0.340). The high frequency of the strong genotypes is associated with the production of milk with high fat and protein content and with optimal technological properties. In Girgentana goat breed, the CSN1S1 genotype information could be utilised in selection strategies for milk protein content and milk yield, in order to select genetic lines for the production of ‘drinking milk’ using weak and null genotypes, and for niche products using strong genotypes.

Anti-mastitis SNV identification of NFκB1 in Chinese Holstein cows and the possible anti-inflammation role of NFκB1/p105 in bovine MECs

2020 ◽  
Vol 52 (11) ◽  
pp. 1191-1201
Author(s):  
Ling Chen ◽  
Rongfu Tian ◽  
Huilin Zhang ◽  
Xiaolin Liu
Keyword(s):  
Somatic Cell ◽  
Mrna Expression ◽  
Protein Content ◽  
Milk Protein ◽  
Holstein Cows ◽  
Chinese Holstein ◽  
Milk Protein Content ◽  
Chinese Holstein Cows ◽  
Anti Inflammation

Abstract NFκB1/p105 is the critical member of the NFκB family which can suppress inflammation, ageing, and cancer when p50/p50 homodimer is formed. Currently, the research about the role of NFκB1/p105 during cow mastitis is limited. Here, we analyzed the correlation of six single-nucleotide variants of the NFκB1 gene with somatic cell count, milk yield, milk fat content, and milk protein content in 547 Chinese Holstein cows, and explored the mRNA expression profiles of the NFκB family and ubiquitin ligases (βTrCP1, βTrCP2, KPC1, KPC2) in LPS-induced bovine mammary epithelial cells (MECs) by transcriptome-Seq. The association analysis showed that cows with SNV2-TT and SNV6-CC in the NFκB1 gene had significantly higher milk protein content (P &lt; 0.05), while cows with SNV5-TT in the NFκB1 gene had significantly lower somatic cell score (SCS), but CC genotype at SNV5 locus was not detected in our Holstein cows. The transcriptome-Seq results demonstrated the mRNA expression of NFκB1 was increased and peaked at 4 h post-induction, while the mRNA expressions of both KPC1 and BCL3 that promote the anti-inflammation function of NFκB1/p105 were decreased in LPS-induced bovine MECs. TNFAIP3, an inhibitor of both degradation and processing of p105 precursor, was markedly increased by more than 3 folds. Furthermore, bta-miR-125b which targets at the 3ʹUTR of TNFAIP3 was reduced by 50%. These results indicated that SNV5-TT of the NFκB1 gene with lower SCS may be an anti-mastitis genotype that could cope with infection more efficiently in Chinese Holstein cows. In addition, the anti-inflammation role of NFκB1/p105 seemed to be inhibited in LPS-induced-bovine MECs because the formation of the p50/p50 homodimer was arrested. This study provides a new perspective to understand the inflammatory mechanism in dairy mastitis.

scholarly journals The chemical composition and technological properties of milk obtained from the morning and evening milking

2008 ◽  
Vol 56 (5) ◽  
pp. 187-198 ◽  
Author(s):  
Martin Skýpala ◽  
Gustav Chládek
Keyword(s):  
Protein Content ◽  
Milk Yield ◽  
Milk Fat ◽  
Protein Production ◽  
Milk Protein ◽  
Fat Content ◽  
Total Solids ◽  
Milk Protein Content ◽  
Solids Content ◽  
Morning Milk

Milk yield varies during lactation, following what is termed a lactation curve. ŽIŽLAVSKÝ and MIKŠÍK (1988) recorded changes in milk yield within a day, too. TEPLÝ et al. (1979) a KOUŘIMSKÁ et al. (2007) published variation within a day ± 1.10 kg in milk yield, ± 0.75 % in milk fat content and ± 0.20 % in milk protein content. Milk yield of cows can be expressed in many different ways, for instance, in kilograms per lactation or in kilograms per day. A practical parameter describing milk production is milk yield (kg) per milking.The object of experiment were 12 cows of Holstein cattle on the first lactation from the 100-day of lactation to 200-day of lactation. The samples of milk were collected from January to May 2007, once a month from the morning and evening milking (milking interval 12 h ± 15 min.). The following parameters were monitored: milk production – milk yield (kg), milk protein production (kg), milk fat production (kg); milk composition – milk protein content (%), milk fat content (%), lactose content (%), milk solids-not-fat content (%), milk total solids content (%); technological properties of milk – ti­tra­tab­le acidity (SH), active acidity (pH), rennet coagulation time (s), quality of curd (class) and somatic cell count as a parameter of udder health.Highly significant differences were found (P < 0.01) between morning milk yield (15.7 kg) and evening milk yield (13.8 kg), between morning milk protein production (0.51 kg) and evening milk protein production (0.45 kg) and between evening milk fat content (4.41 %) and morning milk fat content (3.95 %). A significant difference (P < 0.05) was found between morning milk total solids content (12.62 %) and evening milk total solids content (12.07 %). No significant differences were found between morning (M) and evening (E) values of the remaining parameters: milk fat production (M 0.62 kg; E 0.60 kg), milk protein content (M 3.24 %; E 3.27 %), milk lactose content (M 4.78 %; E 4.86 %), milk solids-not-fat content (M 7.69 %; E 7.71 %), somatic cell count (M 80 000/1 mL; E 101 000/1 mL), titratable aci­di­ty (M 7.75 SH; E 7.64 SH), active acidity (M pH 6.58; E pH 6.61), rennet coagulation time (M 189 s.; E 191 s.), quality of curd (M 1.60 class; E 1.57 class).

Prediction of milk protein content from elements of the Metabolisable Protein System and the effect of ether extract on prediction precision of models

1999 ◽  
Vol 1999 ◽  
pp. 206-206
Author(s):  
E. Smoler
Keyword(s):  
Protein Content ◽  
Predictive Models ◽  
Protein Concentration ◽  
Protein Production ◽  
Milk Protein ◽  
Ether Extract ◽  
Milk Protein Content ◽  
Negative Effect ◽  
Dietary Components ◽  
Protein Feed

Carbohydrates and proteins are the major dietary components supplying metabolisable protein for milk protein production. However, ether extract (EE) or fats have sometimes been related to negative effect on milk protein concentration (Spörndly, 1989; Smoler, 1996). Models for the prediction of milk protein concentration from combinations of carbohydrate and protein feed components have been constructed by Smoler (1996). In order to reduce collinearity among predictors and verify EE's negative effect on predictions of milk protein concentration, predictive models based on carbohydrate and protein dietary components were compared to those based on the same components but with the addition of EE.

The relationship between milk progesterone and milk traits during estrus in dairy Cows

2018 ◽  
Author(s):  
A Gavelis ◽  
V. Þilaitis ◽  
A Juozaitis ◽  
V. Juozaitienë ◽  
G. Urbonavièius ◽  
...  
Keyword(s):  
Dairy Cows ◽  
Protein Content ◽  
Milk Yield ◽  
Milk Fat ◽  
Milk Protein ◽  
Milk Traits ◽  
Subclinical Ketosis ◽  
Yield Level ◽  
Milk Protein Content ◽  
The Relationship

The aim of this study was to evaluate relationship between milk progesterone concentration (P4) and milk traits at the start of estrus time and 12h after start of the estrus in dairy cows. The 96 milk samples of 48 Lithuanian dairy cows without reproduction disorders and 90–100 days after calving were evaluated. Cows were classified into two groups based on milk yield per day: less than 30 kg (n=20) and e”30 kg (n=28). Data were categorized by milk fat and protein content at the start estrus and 12h after start of estrus to evaluate relationship between P4 and milk traits examined. P4 at estrus time in dairy cows was significantly positively correlated with milk yield (P less than 0.001), whereas it was negatively correlated with milk protein (P less than 0.05-P less than 0.01) and fat at 12h after start of estrus. Dairy cows with F/P from 1.0 to 1.5 had the lowest P4 in milk. Results of the pregnancy in dairy cows were related with lower P4 and milk yield level (P less than 0.001), higher milk fat (P less than 0.05) and milk protein content (P less than 0.001). These cows had 1.90 times lower prevalence of the signs of subclinical ketosis (P less than 0.05) at estrus time when compared with non-pregnant cows. As a result, it was clearly demonstrated that P4 in dairy cows can help to evaluate and improve the reproductive properties of cows.

scholarly journals Productive characteristics and body measurements of alpine goats raised under smallholder production systems in central Serbia

2015 ◽  
Vol 31 (2) ◽  
pp. 245-253 ◽  
Author(s):  
N. Maksimovic ◽  
F. Bauman ◽  
M.P. Petrovic ◽  
V.C. Petrovic ◽  
D. Ruzic-Muslic ◽  
...  
Keyword(s):  
Body Weight ◽  
Protein Content ◽  
Milk Fat ◽  
Milk Protein ◽  
Production Systems ◽  
Smallholder Farms ◽  
Milk Protein Content ◽  
Average Body ◽  
Linear Body ◽  
Average Body Weight

The purpose of this study was to evaluate present level of productivity and determine linear body traits of Alpine goats raised in Serbia on smallholder farms. Data were collected from 22 smallholder farms located in Belgrade district, with total of 330 purebred Alpine does 2-9 years of age, 145 yearling does and 476 kids. Traits measured were: body weight of does, body weight of kids at birth, 30 days of age and at weaning (90-120 days), prolificacy of mature and yearling (primiparous) does, six linear body traits of does (wither height, body length, hearth girth, chest depth, chest width, pelvic width) and milk production (milk yield, milk fat and milk protein content). The analysis showed the average body weight of does to be 54.96 kg, while the average body weight of kids at birth, 30 days of age and weaning was 2.73 kg, 8.7 kg and 18.3 kg., respectively. Prolificacy was 144% in mature and 125% in yearling does. Measurements of linear body traits were: wither height 67.87 cm, body length 71.92 cm, hearth girth 81.79 cm, chest depth 32.93, chest width 21.49 cm and pelvic width 17.63 cm. Among dairy production traits, following results were obtained: lactation length 220.73 days, total milk yield 531.66 kg, milk fat content 3.33% and milk protein content 3.16%. It was concluded that the overall productivity of Alpine goats raised under smallholder production systems in Serbia is satisfying. Giving the fact that these animals are usually kept under poor conditions, many of these productive traits are very good.

scholarly journals Production and composition of milk per Holstein and Jersey cow from two farms in northwest Rio Grande do Sul

2020 ◽  
Vol 21 ◽  
Author(s):  
Júlia Laize Bandeira CALGARO ◽  
Júnior FIORESI ◽  
João Pedro VELHO ◽  
Fernanda Hammes STROEHER ◽  
Dileta Regina Moro ALESSIO ◽  
...  
Keyword(s):  
Somatic Cell ◽  
Protein Content ◽  
Milk Production ◽  
Body Condition ◽  
Milk Protein ◽  
Body Condition Score ◽  
Dry Extract ◽  
Milk Protein Content ◽  
Condition Score ◽  
Negative Relationships

ABSTRACT The aim of the present study was to monitor cow milk quality and composition in two farms in the Noroeste Rio-grandense mesoregion, located in the municipalities of Palmeira das Missões and Pinhal - RS. Both herds were mixed, with animals of the Holstein (70%) and Jersey (30%) breeds. The following overall parameters were evaluated: body condition score (BCS), udder dirtiness, and calving order, and the following milk composition factors were measured: total dry extract (TDE), defatted dry extract (DDE), milk lactose, fat, and protein contents, casein, milk urea nitrogen (MUN), and somatic cell count (SCC). Multivariate statistical analysis was performed, and four factors were identified representing combinations of the measured variables. The first factor comprised negative relationships between milk production and cow breed, milk fat content, and milk protein content. The second factor comprised the positive relationships between lactation days and body condition score and milk protein content. The third factor represented the negative relationships between milk lactose content and SCC score, calving order, and BCS. The fourth factor was composed of the positive relationship between delivery order and udder dirtiness. Cluster analysis revealed that individual cows could be categorized into three groups. Monitoring the breed, calving order, body condition score, lactation days, milk production, fat, protein, and lactose contents, somatic cell counts, and udder dirtiness in cows allows greater control of the herd, allowing potential shortcomings to be rectified quickly and economic losses to production to be minimized.

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