Crystal structure of the alpha(6)beta(6) holoenzyme of propionyl-coenzyme A carboxylase.

Propionyl-coenzyme A carboxylase (PCC), a mitochondrial biotin-dependent enzyme, is essential for the catabolism of the amino acids Thr, Val, Ile and Met, cholesterol and fatty acids with an odd number of carbon atoms. Deficiencies in PCC activity in humans are linked to the disease propionic acidaemia, an autosomal recessive disorder that can be fatal in infants. The holoenzyme of PCC is an alpha(6)beta(6) dodecamer, with a molecular mass of 750 kDa. The alpha-subunit contains the biotin carboxylase (BC) and biotin carboxyl carrier protein (BCCP) domains, whereas the beta-subunit supplies the carboxyltransferase (CT) activity. Here we report the crystal structure at 3.2-A resolution of a bacterial PCC alpha(6)beta(6) holoenzyme as well as cryo-electron microscopy (cryo-EM) reconstruction at 15-A resolution demonstrating a similar structure for human PCC. The structure defines the overall architecture of PCC and reveals unexpectedly that the alpha-subunits are arranged as monomers in the holoenzyme, decorating a central beta(6) hexamer. A hitherto unrecognized domain in the alpha-subunit, formed by residues between the BC and BCCP domains, is crucial for interactions with the beta-subunit. We have named it the BT domain. The structure reveals for the first time the relative positions of the BC and CT active sites in the holoenzyme. They are separated by approximately 55 A, indicating that the entire BCCP domain must translocate during catalysis. The BCCP domain is located in the active site of the beta-subunit in the current structure, providing insight for its involvement in the CT reaction. The structural information establishes a molecular basis for understanding the large collection of disease-causing mutations in PCC and is relevant for the holoenzymes of other biotin-dependent carboxylases, including 3-methylcrotonyl-CoA carboxylase (MCC) and eukaryotic acetyl-CoA carboxylase (ACC).

RNA-SEQUENCING APPLICATIONS: GENE EXPRESSION QUANTIFICATION AND METHYLATOR PHENOTYPE IDENTIFICATION

My dissertation focuses on two aspects of RNA sequencing technology. The first is the methodology for modeling the overdispersion inherent in RNA-seq data for differential expression analysis. This aspect is addressed in three sections. The second aspect is the application of RNA-seq data to identify the CpG island methylator phenotype (CIMP) by integrating datasets of mRNA expression level and DNA methylation status. Section 1: The cost of DNA sequencing has reduced dramatically in the past decade. Consequently, genomic research increasingly depends on sequencing technology. However it remains elusive how the sequencing capacity influences the accuracy of mRNA expression measurement. We observe that accuracy improves along with the increasing sequencing depth. To model the overdispersion, we use the beta-binomial distribution with a new parameter indicating the dependency between overdispersion and sequencing depth. Our modified beta-binomial model performs better than the binomial or the pure beta-binomial model with a lower false discovery rate. Section 2: Although a number of methods have been proposed in order to accurately analyze differential RNA expression on the gene level, modeling on the base pair level is required. Here, we find that the overdispersion rate decreases as the sequencing depth increases on the base pair level. Also, we propose four models and compare them with each other. As expected, our beta binomial model with a dynamic overdispersion rate is shown to be superior. Section 3: We investigate biases in RNA-seq by exploring the measurement of the external control, spike-in RNA. This study is based on two datasets with spike-in controls obtained from a recent study. We observe an undiscovered bias in the measurement of the spike-in transcripts that arises from the influence of the sample transcripts in RNA-seq. Also, we find that this influence is related to the local sequence of the random hexamer that is used in priming. We suggest a model of the inequality between samples and to correct this type of bias. Section 4: The expression of a gene can be turned off when its promoter is highly methylated. Several studies have reported that a clear threshold effect exists in gene silencing that is mediated by DNA methylation. It is reasonable to assume the thresholds are specific for each gene. It is also intriguing to investigate genes that are largely controlled by DNA methylation. These genes are called “L-shaped” genes. We develop a method to determine the DNA methylation threshold and identify a new CIMP of BRCA. In conclusion, we provide a detailed understanding of the relationship between the overdispersion rate and sequencing depth. And we reveal a new bias in RNA-seq and provide a detailed understanding of the relationship between this new bias and the local sequence. Also we develop a powerful method to dichotomize methylation status and consequently we identify a new CIMP of breast cancer with a distinct classification of molecular characteristics and clinical features.