Monooxygenases involved in the n-alkanes metabolism by Rhodococcus sp. BCP1: molecular characterization and expression of alkB gene

Bioremediation implies the use of living organisms, primarily microorganisms, to convert environmental contaminants into less toxic forms. The impact of the consequences of hydrocarbon release in the environment maintain a high research interest in the study of microbial metabolisms associated with the biodegradation of aromatic and aliphatic hydrocarbons but also in the analysis of microbial enzymes that can convert petroleum substrates to value-added products. The studies described in this Thesis fall within the research field that directs the efforts into identifying gene/proteins involved in the catabolism of n-alkanes and into studying the regulatory mechanisms leading to their oxidation. In particular the studies were aimed at investigating the molecular aspects of the ability of Rhodococcus sp. BCP1 to grow on aliphatic hydrocarbons as sole carbon and energy sources. We studied the ability of Rhodococcus sp. BCP1 to grow on gaseous (C2-C4), liquid (C5-C16) and solid (C17-C28) n-alkanes that resulted to be biochemically correlated with the activity of one or more monooxygenases. In order to identify the alkane monooxygenase that is involved in the n-alkanes degradation pathway in Rhodococcus sp. BCP1, PCR-based methodology was applied by using degenerate primers targeting AlkB monooxygenase family members. As result, a chromosomal region, including the alkB gene cluster, was cloned from Rhodococcus sp. BCP1 genome. We characterized the products of this alkB gene cluster and the products of the orfs included in the flanking regions by comparative analysis with the homologues in the database. alkB gene expression studies were carried out by RT-PCR and by the construction of a promoter probe vector containing the lacZ gene downstream of the alkB promoter. B-galactosidase assays revealed the alkB promoter activity induced by n-alkanes and by n-alkanes metabolic products. Furthermore, the transcriptional start of alkB gene was determined by primer extension procedure. A proteomic approach was subsequently applied to compare the protein patterns expressed by BCP1 growing on n-butane, n-hexane, n-hexadecane or n-eicosane with the protein pattern expressed by BCP1 growing on succinate. The accumulation of enzymes specifically induced on n-alkanes was determined. These enzymes were identified by tandem mass spectrometry (LC/MS/MS). Finally, a prm gene, homologue to the gene family coding for soluble di-iron monooxygenases (SDIMOs), has been isolated from Rhodococcus sp. BCP1 genome. This gene product could be involved in the degradation of gaseous n-alkanes in this Rhodococcus strain. The versatility in utilizing hydrocarbons and the discovery of new remarkable metabolic activities outline the potential applications of this microorganism in environmental and industrial biotechnologies.

New computational biology tools for the systematic analysis of the structure and expression of human genes

From the late 1980s, the automation of sequencing techniques and the computer spread gave rise to a flourishing number of new molecular structures and sequences and to proliferation of new databases in which to store them. Here are presented three computational approaches able to analyse the massive amount of publicly avalilable data in order to answer to important biological questions. The first strategy studies the incorrect assignment of the first AUG codon in a messenger RNA (mRNA), due to the incomplete determination of its 5' end sequence. An extension of the mRNA 5' coding region was identified in 477 in human loci, out of all human known mRNAs analysed, using an automated expressed sequence tag (EST)-based approach. Proof-of-concept confirmation was obtained by in vitro cloning and sequencing for GNB2L1, QARS and TDP2 and the consequences for the functional studies are discussed. The second approach analyses the codon bias, the phenomenon in which distinct synonymous codons are used with different frequencies, and, following integration with a gene expression profile, estimates the total number of codons present across all the expressed mRNAs (named here "codonome value") in a given biological condition. Systematic analyses across different pathological and normal human tissues and multiple species shows a surprisingly tight correlation between the codon bias and the codonome bias. The third approach is useful to studies the expression of human autism spectrum disorder (ASD) implicated genes. ASD implicated genes sharing microRNA response elements (MREs) for the same microRNA are co-expressed in brain samples from healthy and ASD affected individuals. The different expression of a recently identified long non coding RNA which have four MREs for the same microRNA could disrupt the equilibrium in this network, but further analyses and experiments are needed.

Differential expression analysis for sequence count data via mixtures of negative binomials

The recent advent of Next-generation sequencing technologies has revolutionized the way of analyzing the genome. This innovation allows to get deeper information at a lower cost and in less time, and provides data that are discrete measurements. One of the most important applications with these data is the differential analysis, that is investigating if one gene exhibit a different expression level in correspondence of two (or more) biological conditions (such as disease states, treatments received and so on). As for the statistical analysis, the final aim will be statistical testing and for modeling these data the Negative Binomial distribution is considered the most adequate one especially because it allows for "over dispersion". However, the estimation of the dispersion parameter is a very delicate issue because few information are usually available for estimating it. Many strategies have been proposed, but they often result in procedures based on plug-in estimates, and in this thesis we show that this discrepancy between the estimation and the testing framework can lead to uncontrolled first-type errors. We propose a mixture model that allows each gene to share information with other genes that exhibit similar variability. Afterwards, three consistent statistical tests are developed for differential expression analysis. We show that the proposed method improves the sensitivity of detecting differentially expressed genes with respect to the common procedures, since it is the best one in reaching the nominal value for the first-type error, while keeping elevate power. The method is finally illustrated on prostate cancer RNA-seq data.