The Saccharomyces cerevisiae RNA-binding protein Rbp29 functions in cytoplasmic mRNA metabolism

Here we report that the Saccharomyces cerevisiae RBP29 (SGN1, YIR001C) gene encodes a 29-kDa cytoplasmic protein that binds to mRNA in vivo. Rbp29p can be co-immunoprecipitated with the poly(A) tail-binding protein Pab1p from crude yeast extracts in a dosageand RNA-dependent manner. In addition, recombinant Rbp29p binds preferentially to poly(A) with nanomolar binding affinity in vitro. Although RBP29 is not essential for cell viability, its deletion exacerbates the slow growth phenotype of yeast strains harboring mutations in the eIF4G genes TIF4631 and TIF4632. Furthermore, overexpression of RBP29 suppresses the temperaturesensitive growth phenotype of specific tif4631, tif4632, and pab1 alleles. These data suggest that Rbp29p is an mRNA-binding protein that plays a role in modulating the expression of cytoplasmic mRNA.

Multiscale analysis of protein functions and stochastic modelling of gene transcriptional regulatory networks

Genomic and proteomic analyses have attracted a great deal of interests in biological research in recent years. Many methods have been applied to discover useful information contained in the enormous databases of genomic sequences and amino acid sequences. The results of these investigations inspire further research in biological fields in return. These biological sequences, which may be considered as multiscale sequences, have some specific features which need further efforts to characterise using more refined methods. This project aims to study some of these biological challenges with multiscale analysis methods and stochastic modelling approach. The first part of the thesis aims to cluster some unknown proteins, and classify their families as well as their structural classes. A development in proteomic analysis is concerned with the determination of protein functions. The first step in this development is to classify proteins and predict their families. This motives us to study some unknown proteins from specific families, and to cluster them into families and structural classes. We select a large number of proteins from the same families or superfamilies, and link them to simulate some unknown large proteins from these families. We use multifractal analysis and the wavelet method to capture the characteristics of these linked proteins. The simulation results show that the method is valid for the classification of large proteins. The second part of the thesis aims to explore the relationship of proteins based on a layered comparison with their components. Many methods are based on homology of proteins because the resemblance at the protein sequence level normally indicates the similarity of functions and structures. However, some proteins may have similar functions with low sequential identity. We consider protein sequences at detail level to investigate the problem of comparison of proteins. The comparison is based on the empirical mode decomposition (EMD), and protein sequences are detected with the intrinsic mode functions. A measure of similarity is introduced with a new cross-correlation formula. The similarity results show that the EMD is useful for detection of functional relationships of proteins. The third part of the thesis aims to investigate the transcriptional regulatory network of yeast cell cycle via stochastic differential equations. As the investigation of genome-wide gene expressions has become a focus in genomic analysis, researchers have tried to understand the mechanisms of the yeast genome for many years. How cells control gene expressions still needs further investigation. We use a stochastic differential equation to model the expression profile of a target gene. We modify the model with a Gaussian membership function. For each target gene, a transcriptional rate is obtained, and the estimated transcriptional rate is also calculated with the information from five possible transcriptional regulators. Some regulators of these target genes are verified with the related references. With these results, we construct a transcriptional regulatory network for the genes from the yeast Saccharomyces cerevisiae. The construction of transcriptional regulatory network is useful for detecting more mechanisms of the yeast cell cycle.

Brca2 is involved in meiosis in Arabidopsis thaliana as suggested by its interaction with Dmc1

Two BRCA2-like sequences are present in the Arabidopsis genome. Both genes are expressed in flower buds and encode nearly identical proteins, which contain four BRC motifs. In a yeast two-hybrid assay, the Arabidopsis Brca2 proteins interact with Rad51 and Dmc1. RNAi constructs aimed at silencing the BRCA2 genes at meiosis triggered a reproducible sterility phenotype, which was associated with dramatic meiosis alterations. We obtained the same phenotype upon introduction of RNAi constructs aimed at silencing the RAD51 gene at meiosis in dmc1 mutant plants. The meiotic figures we observed strongly suggest that homologous recombination is highly disturbed in these meiotic cells, leaving aberrant recombination events to repair the meiotic double-strand breaks. The 'brca2' meiotic phenotype was eliminated in spo11 mutant plants. Our experiments point to an essential role of Brca2 at meiosis in Arabidopsis. We also propose a role for Rad51 in the dmc1 context.

Microalgal biomass as a fermentation feedstock for bioethanol production

BACKGROUND The increasing cost of fossil fuels as well as the escalating social and industrial awareness of the environmental impacts associated with the use of fossil fuels has created the need for more sustainable fuel options. Bioethanol, produced from renewable biomass such as sugar and starch materials, is believed to be one of these options, and it is currently being harnessed extensively. However, the utilization of sugar and starch materials as feedstocks for bioethanol production creates a major competition with the food market in terms of land for cultivation, and this makes bioethanol from these sources economically less attractive. RESULT This study explores the suitability of microalgae (Chlorococum sp.) as a substrate for bioethanol production via yeast (Saccharomycesbayanus)under different fermentation conditions. Results show a maximum ethanol concentration of 3.83 g L -1 obtained from 10 g L-1 of lipid-extracted microalgae debris. CONCLUSION This productivity level (∼38% w/w), which is in keeping with that of current production systems endorses microalgae as a promising substrate for bioethanol production.

Mixture models of nucleotide sequence evolution that account for heterogeneity in the substitution process across sites and across lineages

Molecular phylogenetic studies of homologous sequences of nucleotides often assume that the underlying evolutionary process was globally stationary, reversible, and homogeneous (SRH), and that a model of evolution with one or more site-specific and time-reversible rate matrices (e.g., the GTR rate matrix) is enough to accurately model the evolution of data over the whole tree. However, an increasing body of data suggests that evolution under these conditions is an exception, rather than the norm. To address this issue, several non-SRH models of molecular evolution have been proposed, but they either ignore heterogeneity in the substitution process across sites (HAS) or assume it can be modeled accurately using the distribution. As an alternative to these models of evolution, we introduce a family of mixture models that approximate HAS without the assumption of an underlying predefined statistical distribution. This family of mixture models is combined with non-SRH models of evolution that account for heterogeneity in the substitution process across lineages (HAL). We also present two algorithms for searching model space and identifying an optimal model of evolution that is less likely to over- or underparameterize the data. The performance of the two new algorithms was evaluated using alignments of nucleotides with 10 000 sites simulated under complex non-SRH conditions on a 25-tipped tree. The algorithms were found to be very successful, identifying the correct HAL model with a 75% success rate (the average success rate for assigning rate matrices to the tree's 48 edges was 99.25%) and, for the correct HAL model, identifying the correct HAS model with a 98% success rate. Finally, parameter estimates obtained under the correct HAL-HAS model were found to be accurate and precise. The merits of our new algorithms were illustrated with an analysis of 42 337 second codon sites extracted from a concatenation of 106 alignments of orthologous genes encoded by the nuclear genomes of Saccharomyces cerevisiae, S. paradoxus, S. mikatae, S. kudriavzevii, S. castellii, S. kluyveri, S. bayanus, and Candida albicans. Our results show that second codon sites in the ancestral genome of these species contained 49.1% invariable sites, 39.6% variable sites belonging to one rate category (V1), and 11.3% variable sites belonging to a second rate category (V2). The ancestral nucleotide content was found to differ markedly across these three sets of sites, and the evolutionary processes operating at the variable sites were found to be non-SRH and best modeled by a combination of eight edge-specific rate matrices (four for V1 and four for V2). The number of substitutions per site at the variable sites also differed markedly, with sites belonging to V1 evolving slower than those belonging to V2 along the lineages separating the seven species of Saccharomyces. Finally, sites belonging to V1 appeared to have ceased evolving along the lineages separating S. cerevisiae, S. paradoxus, S. mikatae, S. kudriavzevii, and S. bayanus, implying that they might have become so selectively constrained that they could be considered invariable sites in these species.