Browsing by Subject "Bioinformatics"

Lipoxygenase enzymes, which contribute significantly to storage protein in legume seeds have been reported to cause the emission of volatile compounds associated with the generation of off-flavours. This is an are important factor limiting the acceptance of faba bean (Vicia faba) I foods. This study aimed at using bioinformatic tools to identify seed-borne lipoxygenase (LOX) genes and to design a biological tool using molecular techniques to find changes in sequence in faba bean lines. LOX gene mining by Exonerate sequence comparison on the whole genome sequence of faba bean was used to identify six LOX genes containing Polycystin-1, Lipoxygenase, Alpha-Toxin (PLAT) and/or LH2 LOX domains. Their sequence properties, evolutionary relationships, important conserved LOX motifs and subcellular location were analysed. The LOX gene proteins identified contained 272 – 853 amino acids (aa). The molecular weight ranged from 23.67 kDa in Gene 6 to 96.45 kDA in Gene 1. All the proteins had isoelectric points in the acidic range except Genes 6 and 7 which were alkaline. Only one gene had both LOX conserved domains with aa sequence length similar with that found in soybean and pea LOX genes and isoelectric properties with soybean LOX3. Phylogenetic analysis indicated that the genes were clustered into 9S LOX and 13S LOX types alongside other seed LOX genes in some legumes. Five motifs were found, and sequence analysis showed that three genes (Gene 1, 2 and 3) contained the 38-aa residue motif that includes five histidine residues [His-(X)4-His-(X)4-His-(X)17-His-(X)8-His]. The subcellular localization of the lipoxygenase proteins was predicted to be primarily the cytoplasm and chloroplast. Primers covering ~1.2 kb were designed, based on the conserved region of Genes 1, 2 and 3 nucleotide sequences. Gel electrophoresis showed the PCR amplification of the seed LOX gene at the expected region for twelve faba bean lines. Phylogenetic analysis showed evolutionary divergence among faba bean lines for sequenced and amplified region of their respective seed LOX alleles.

Multiple Myeloma (MM) is the second most common hematologic malignancy. Despite the advancements in treatment approaches in the last decade, the prevalence of refractory disease leading to relapsed cases has been a major challenge. A wide range of intricate genetic heterogeneity demonstrated by myeloma patients is a credible explanation for the diverse treatment responses observed in patients sharing the same treatment regimens. Pertaining to this, the study aims to identify predictive gene expression biomarkers that forecast response to BCL2 inhibitor venetoclax and treatment outcome to proteasome inhibitor bortezomib. In this study, samples from MM patients were characterized into sensitive and resistant, (1) based on ex vivo response to venetoclax treatment (Resistant n=21; Sensitive n=21), and (2) based on their bortezomib treatment outcome in clinical profiles (Resistant n=12; Sensitive n=15). Associations between the different gene expressions and drug responses were studied using statistical and bioinformatic tools. As a result, we identified that significant (p-value <0.05) overexpression of 36 genes and downregulation of 38 genes appeared to confer resistance to venetoclax drug response in MM patients. Additionally, the functional association of these genes with pathways was determined using a pathway enrichment tool. Furthermore, the study provided evidence that cytogenetic alterations t(11;14) and t(4;14) are significantly (p-value <0.05) associated with differing venetoclax response in MM patients. These findings demonstrated that gene expression biomarkers and chromosomal translocations play a significant role in regulating venetoclax drug response in MM, which can be further utilized to personalize treatments for patients. The knowledge obtained from this work best applies in personalized medicine; whereby fitting treatments to an individual patient’s genomic landscape will enhance patient outcome.

The emergence of antibiotic resistance is a growing concern globally. The horizontal spread of antimicrobial resistance genes (ARG) causes multi drug resistant strains that can be harmful to human- and animal health. This risk must be considered when new bacterial strains are used in the plant protection industry, therefore this master’s thesis presents a new bioinformatical method to evaluate the potential of ARG to horizontally transfer to another bacteria. This thesis will walk through analyses that can be used to hunt for ARGs and mobile genetic elements (MGE) in bacterial whole genome sequence data. Also, this thesis presents a new computational analysis tool called MGEradar that reveals MGEs that are linked to a specific ARG. Also, this thesis presents a simple metod for studying the prevalence of an ARG among other bacterial strains of the suspect species. MGEradar, the prevalence analysis and the bioinformatical pipeline can be helpful tools to evaluate the intrinsicness and mobility of an ARG. For the evaluation of the bioinformatical method, the genomes of Escherichia coli and Bacillus thuringiensis are examined to determine the MGEs associated with two ARGs, mcr-1 and fosB.