Experiment Research Of Three Negative Correlation Patterns In Saccharomyces Cerevisiae

In the gene expression profiles, genes often appeared up-regulated and down-regulated phenomenon at the same time, and quantity are often similar. From a mathematical point of view, the up-regulated and down-regulated genes in the corresponding relationship are negative correlation. Consequently, our group will introduce a new concept: negative correlation patterns, which means that if gene subsets V1 and V2(form gene set V together) have opposite trends in a period, however, the genes within each subsets have a similar expression trend, in this situation, we can say subsets V1 and V2 are a negative correlation patterns. Based on this research, our group proposed NCFCA algorithm. After applicated it in the research of S.cerevisiae cell cycle gene expression data sets and the expression profile of stress response, we found that the minichromosome maintenance protein genes and core histone genes may be negative correlation patterns, the ribosomal protein genes and heat shock protein genes, starch and sucrose metabolism pathway genes and purine metabolism pathway genes could be also.This study is an experimental verification based on the theoretical calculation results. In this study, we measured the transcript stability of reference genes and selected them as internal control genes for normalization expression dat a. The dynamic expression of three groups of genes in S. cerivisiae during different conditions was investigated by the q PCR method and explored the relationship between them to find some evidence to support the negative correlation patterns.First of all, we use α factor, hydroxyurea, starvation induced method, density gradient centrifugation to synchronize the S.cerevisiae cells to find the best method for cells synchronization. After designing and choosing primer, the annealing temperature was optimized. Then make the standard curve, to gain primer ’s amplification efficiency and correlation coefficient. Expression level of reference genes and target genes were detected by q PCR. The stability of these five reference genes was evaluated by ge Norm software and stable reference genes were used to normalize gene expression of target genes. According to the different expression information of three pairs of genome, S.cerevisiae was grown in different environment respectively. At first, RNA extraction, reverse transcription, first strand c DNA synthsis and used it as template, q PCR was performed by the tested primer. Then GO function enrichment analysis was carried out on these three groups of genes. In the final, analyze the reasons of formation of these three groups of negative correlation patterns.The results of this study indicated that α factor treatment is the best method to synchronize the cell cycle. The results of ge Norm software indicated that ALG9 and ACT1 were the most stable reference genes in all samples, and they can normalize gene expression of target genes. Gene ontology analysis and q PCR results show that the MCM genes(MCM2, MCM3, MCM4, MCM5, MCM6, MCM7) and core histone genes(HHT1, HHT2, HHF1, HHF2, HTA2, HTA1, HTB2, HTB1) have some same functions: participate in the DN A conformational change, the assembly of protein- DNA complex, the assembly of cell macromolecular complex, the assembly and synthesis of cellular components. Those genes were up-regulated or down-regulated within the cell cycle, and show an opposite trend.Heat shock protein genes(HSP104, HSP12, HSP26, HSP31, HSP42, HSP60, HSP78, HSP82) and ribosomal protein genes(RPS5, RPS18 A, RPS18 B, RPS28 A, RPS28 B, RPL5, RPL18 A, RPL18 B, RPL35 A, RPL35B) were up-regulated and down-regulated with the temperature changes and shown opposite trend, each group of those genes have significant functional similarity, but no functional similarity between two groups of genes.The third group of genome are starch and sucrose metabolism pathway genes(GSY2,NTH1,GLC3,PGM2,TPS1,TPS2) and purine metabolism pathway genes(RNR1,ADE1,IMD3,IMD4,PRS2,PRS1,ADE13,PRS4,PRS3,PRS5,APT1,ADE6,ADE8,ADE2,ADE17,ADE4,AAH1). Each group of those genes has significant functional similarity, but in the process of oxidative stress showed an opposite expression. Starch and sucrose metabolism pathway genes expression in the oxidative stress are almost all activated, and purine metabolism pathway genes expression are suppressed.In addition, the reasons for the formation of these three pairs of negative correlation patterns were explained, they are due to the regulation of C lb-Cdk1 and TORC1. In conclusion, the experimental results proved that three groups of genes really showed negative correlation patterns, and be consistent with the theoretical calculation results. We also found that the reasons for the formation of this three negative correlation patterns are C lb-Cdk1 and TO RC1 respectively, but the specific mechanism needs further research.