The dairy industry uses lipase extensively for hydrolysis of milk fat. Lipase is used in the modification of the fatty acid chain length, to enhance the flavours of various chesses. Therefore finding the unlimited source of lipase is a concern of dairy industry. Due to the importance of lipase, this study was an attempt to express the lipase from Burkholderia cepacia in Lactococcus lactis. To achieve this, a gene associated with lipase transport was amplified and subcloned in inducible pNZ8148 vector, and subsequently transformed into Lc. lactis NZ9000. The enzyme assay as well as SDS-PAGE and western blotting were carried out to analysis the recombinant lipase expression. Nucleotide sequencing of the DNA insert from the clone revealed that the lipase activity corresponded to an open reading frame consisting of 1092 bp coding for a 37·5-kDa size protein. Blue colour colonies on nile blue sulphate agar and sharp band on 37·5-kD size on SDS-PAGE and western blotting results confirm the successful expression of lipase by Lc. lactis. The protein assay also showed high expression, approximately 152·2 μg/ml.h, of lipase by recombinant Lc. lactis. The results indicate that Lc. lactis has high potential to overproduce the recombinant lipase which can be used commercially for industrially purposes.
Milk producers in Malaysia make extensive use of crossbred Sahiwal Friesian dairy cattle. These animals have, however, been found susceptible to lactation failure. A survey of cows in an experimental herd of F1 Sahiwal Friesian animals indicated that, in 30% of animals, milk yield decreased to negligible levels within the first 8 weeks post partum. Lactation failure was associated with a progressive increase in the amount of residual milk left in the udder after normal milking. By week 3 of lactation, residual milk volume was significantly greater than that in animals that, based on previous lactation history were not susceptible to lactation failure, and accounted for up to 30% of milk available at the morning milking. The cellular consequences of residual milk accumulation were evident in the activities of acetyl-CoA carboxylase, fatty acid synthetase and galactosyltransferase, key enzyme markers of cellular differentiation, which decreased in glands undergoing lactation failure and were lower than values measured in tissue of control cows. Mammary cell number, estimated by tissue DNA content, was also reduced in animals undergoing lactation failure. These indices of mammary development indicate that lactation failure is the result of premature involution in susceptible animals. Premature involution is a predictable consequence of progressive milk stasis in failing lactation, and attributable to an increase in autocrine feedback by inhibitory milk constituents. The progressive increase in residual milk is, on the other hand, unlikely to be attributable to impaired mammary development. Measurements of milk storage during milk accumulation showed no differences between control and lactation failure cows in the distribution of milk between alveolar and cisternal storage compartments. We conclude that lactation failure in Sahiwal Friesian cows is due to a failure of milk removal, and probably the result of an impaired milk ejection reflex rather than to the glands' milk storage characteristics.
Nutritional interventions are now recommended as strategies to delay Alzheimer's disease (AD) progression. The present study evaluated the neuroprotective effect (anti-inflammation) of lactic acid bacteria (either Lactobacillus fermentum LAB9 or L. casei LABPC) fermented cow's milk (CM) against lipopolysaccharide (LPS)-activated microglial BV2 cells in vitro. The ability of CM-LAB in attenuating memory deficit in LPS-induced mice was also investigated. ICR mice were orally administered with CM-LAB for 28 d before induction of neuroinflammation by LPS. Learning and memory behaviour were assessed using the Morris Water Maze Test. Brain tissues were homogenised for measurement of acetylcholinesterase (AChE), antioxidative, lipid peroxidation (malondialdehyde (MDA)) and nitrosative stress (NO) parameters. Serum was collected for cytokine analysis. CM-LAB9 and CM-LABPC significantly (P < 0·05) decreased NO level but did not affect CD40 expression in vitro. CM-LAB attenuated LPS-induced memory deficit in mice. This was accompanied by significant (P < 0·05) increment of antioxidants (SOD, GSH, GPx) and reduction of MDA, AChE and also pro-inflammatory cytokines. Unfermented cow's milk (UCM) yielded greater cytokine lowering effect than CM-LAB. The present findings suggest that attenuation of LPS-induced neuroinflamation and memory deficit by CM-LAB could be mediated via anti-inflammation through inhibition of AChE and antioxidative activities.
In this research communication we compare three different approaches for developing dry matter intake (DMI) prediction models based on milk mid-infrared spectra (MIRS), using data collected from a research herd over five years. In dairy production, knowledge of individual DMI could be important and useful, but DMI can be difficult and expensive to measure on most commercial farms as cows are commonly group-fed. Instead, this parameter is often estimated based on the age, body weight, stage of lactation and body condition score of the cow. Recently, milk MIRS have also been used as a tool to estimate DMI. There are different methods available to create prediction models from large datasets. The main data used were total DMI calculated as a 3-d average, coupled with milk MIRS data available fortnightly. Data on milk yield and lactation stage parameters were also available for each animal. We compared the performance of three prediction approaches: partial least-squares regression, support vector machine regression and random forest regression. The full milk MIRS alone gave low to moderate prediction accuracy (R2 = 0.07-0.40), regardless of prediction modelling approach. Adding more variables to the model improved R2 and decreased the prediction error. Overall, partial least-squares regression proved to be the best method for predicting DMI from milk MIRS data, while MIRS data together with milk yield and concentrate DMI at 3-30 d in milk provided good prediction accuracy (R2 = 0.52-0.65) regardless of the prediction tool used.