ABSTRACT. The complement system helps in the direct lysis of invading pathogens and modulates phagocytic, humoral and cellular immune responses. Complement 4 is a critical component in complement activity and protection against many bacterial pathogens because it is essential to classical and lectin activation pathways. We used reverse transcription and PCR to investigate alternative splicing and expression of the complement component 4 (C4A) gene in Chinese Holstein cattle. The PCR products were cloned and sequenced. A novel splice variant involving intron 10 was identified, which we named C4A-AS. To examine how C4A gene activity is affected by bovine mastitis, six Chinese Holstein cattle were divided into healthy (nonmastitic) and Staphylococcus aureus-induced mastitic groups. Realtime quantitative PCR (qRT-PCR) revealed that the C4A-complete and C4A-AS transcripts are expressed at significantly different levels in healthy cows, while there were no significant differences in the mastitic group (P = 0.257). Expression of C4A-AS increased significantly when mastitis developed. We also examined the expression of C4A-complete and C4A-AS in several tissues (liver, heart, spleen, lung, kidney, tongue, and muscle). The two transcripts were expressed in all of these tissues but there were no significant differences in expression between healthy and mastitic cows. We therefore conclude that the C4A-complete transcript is the main transcript under normal physiological conditions, while C4A-AS is augmented when mastitis develops.
It has been known for a long time that microbial predictive models based on traditional plate methods are time-consuming and labour-intensive. Denaturing gradient gel electrophoresis (DGGE) has been widely used as a semiquantitative method to describe complex microbial community. In our study, we developed DGGE to quantify bacterial counts and simultaneously established two molecular predictive models to describe the growth and survival of two bacteria (Listeria monocytogenes and Vibrio parahaemolyticus) at 4 and 10°C. We demonstrated that PCR-DGGE could be used to construct growth models. This work provides a new approach to construct molecular predictive models and thereby facilitates predictive microbiology and QMRA (Quantitative Microbial Risk Assessment).
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