Mean Squared Residue Based Biclustering Algorithms for the Analysis of Gene Expression Data

Computational Biology is the research are that contributes to the analysis of biological data through the development of algorithms which will address significant research problems.The data from molecular biology includes DNA,RNA ,Protein and Gene expression data.Gene Expression Data provides the expression level of genes under different conditions.Gene expression is the process of transcribing the DNA sequence of a gene into mRNA sequences which in turn are later translated into proteins.The number of copies of mRNA produced is called the expression level of a gene.Gene expression data is organized in the form of a matrix. Rows in the matrix represent genes and columns in the matrix represent experimental conditions.Experimental conditions can be different tissue types or time points.Entries in the gene expression matrix are real values.Through the analysis of gene expression data it is possible to determine the behavioral patterns of genes such as similarity of their behavior,nature of their interaction,their respective contribution to the same pathways and so on. Similar expression patterns are exhibited by the genes participating in the same biological process.These patterns have immense relevance and application in bioinformatics and clinical research.Theses patterns are used in the medical domain for aid in more accurate diagnosis,prognosis,treatment planning.drug discovery and protein network analysis.To identify various patterns from gene expression data,data mining techniques are essential.Clustering is an important data mining technique for the analysis of gene expression data.To overcome the problems associated with clustering,biclustering is introduced.Biclustering refers to simultaneous clustering of both rows and columns of a data matrix. Clustering is a global whereas biclustering is a local model.Discovering local expression patterns is essential for identfying many genetic pathways that are not apparent otherwise.It is therefore necessary to move beyond the clustering paradigm towards developing approaches which are capable of discovering local patterns in gene expression data.A biclusters is a submatrix of the gene expression data matrix.The rows and columns in the submatrix need not be contiguous as in the gene expression data matrix.Biclusters are not disjoint.Computation of biclusters is costly because one will have to consider all the combinations of columans and rows in order to find out all the biclusters.The search space for the biclustering problem is 2 m+n where m and n are the number of genes and conditions respectively.Usually m+n is more than 3000.The biclustering problem is NP-hard.Biclustering is a powerful analytical tool for the biologist.The research reported in this thesis addresses the problem of biclustering.Ten algorithms are developed for the identification of coherent biclusters from gene expression data.All these algorithms are making use of a measure called mean squared residue to search for biclusters.The objective here is to identify the biclusters of maximum size with the mean squared residue lower than a given threshold. All these algorithms begin the search from tightly coregulated submatrices called the seeds.These seeds are generated by K-Means clustering algorithm.The algorithms developed can be classified as constraint based,greedy and metaheuristic.Constarint based algorithms uses one or more of the various constaints namely the MSR threshold and the MSR difference threshold.The greedy approach makes a locally optimal choice at each stage with the objective of finding the global optimum.In metaheuristic approaches particle Swarm Optimization(PSO) and variants of Greedy Randomized Adaptive Search Procedure(GRASP) are used for the identification of biclusters.These algorithms are implemented on the Yeast and Lymphoma datasets.Biologically relevant and statistically significant biclusters are identified by all these algorithms which are validated by Gene Ontology database.All these algorithms are compared with some other biclustering algorithms.Algorithms developed in this work overcome some of the problems associated with the already existing algorithms.With the help of some of the algorithms which are developed in this work biclusters with very high row variance,which is higher than the row variance of any other algorithm using mean squared residue, are identified from both Yeast and Lymphoma data sets.Such biclusters which make significant change in the expression level are highly relevant biologically.

β-glucosidases from Aspergillus unguis NII 08123: Molecular characterization, properties and applications

Lignocellulosic biomass is probably the best alternative resource for biofuel production and it is composed mainly of cellulose, hemicelluloses and lignin. Cellulose is the most abundant among the three and conversion of cellulose to glucose is catalyzed by the enzyme cellulase. Cellulases are groups of enzymes act synergistically upon cellulose to produce glucose and comprise of endoglucanase, cellobiohydrolase and β-glucosidase. β -glucosidase assumes great importance due to the fact that it is the rate limiting enzyme. Endoglucanases (EG) produces nicks in the cellulose polymer exposing reducing and non reducing ends, cellobiohydrolases (CBH) acts upon the reducing or non reducing ends to liberate cellobiose units, and β - glucosidases (BGL) cleaves the cellobiose to liberate glucose completing the hydrolysis. . β -glucosidases undergo feedback inhibition by their own product- β glucose, and cellobiose which is their substrate. Few filamentous fungi produce glucose tolerant β - glucosidases which can overcome this inhibition by tolerating the product concentration to a particular threshold. The present study had targeted a filamentous fungus producing glucose tolerant β - glucosidase which was identified by morphological as well as molecular method. The fungus showed 99% similarity to Aspergillus unguis strain which comes under the Aspergillus nidulans group where most of the glucose tolerant β -glucosidase belongs. The culture was designated the strain number NII 08123 and was deposited in the NII culture collection at CSIR-NIIST. β -glucosidase multiplicity is a common occurrence in fungal world and in A.unguis this was demonstrated using zymogram analysis. A total 5 extracellular isoforms were detected in fungus and the expression levels of these five isoforms varied based on the carbon source available in the medium. Three of these 5 isoforms were expressed in higher levels as identified by the increased fluorescence (due to larger amounts of MUG breakdown by enzyme action) and was speculated to contribute significantly to the total _- β glucosidase activity. These isoforms were named as BGL 1, BGL3 and BGL 5. Among the three, BGL5 was demonstrated to be the glucose tolerant β -glucosidase and this was a low molecular weight protein. Major fraction was a high molecular weight protein but with lesser tolerance to glucose. BGL 3 was between the two in both activity and glucose tolerance.121 Glucose tolerant .β -glucosidase was purified and characterized and kinetic analysis showed that the glucose inhibition constant (Ki) of the protein is 800mM and Km and Vmax of the enzyme was found to be 4.854 mM and 2.946 mol min-1mg protein-1respectively. The optimumtemperature was 60°C and pH 6.0. The molecular weight of the purified protein was ~10kDa in both SDS as well as Native PAGE indicating that the glucose tolerant BGL is a monomeric protein.The major β -glucosidase, BGL1 had a pH and temperature optima of 5.0 and 60 °C respectively. The apparent molecular weight of the Native protein is 240kDa. The Vmax and Km was 78.8 mol min-1mg protein-1 and 0.326mM respectively. Degenerate primers were designed for glycosyl hydrolase families 1, 3 and 5 and the BGL genes were amplified from genomic DNA of Aspergillus unguis. The sequence analyses performed on the amplicons results confirmed the presence of all the three genes. Amplicon with a size of ~500bp was sequenced and which matched to a GH1 –BGL from Aspergillus oryzae. GH3 degenerate primers producing amplicons were sequenced and the sequences matched to β - glucosidase of GH3 family from Aspergillus nidulans and Aspergillus acculateus. GH5 degenerate primers also gave amplification and sequencing results indicated the presence of GH5 family BGL gene in the Aspergillus unguis genomic DNA.From the partial gene sequencing results, specific as well as degenerate primers were designed for TAIL PCR. Sequencing results of the 1.0 Kb amplicon matched Aspergillus nidulans β -glucosidase gene which belongs to the GH1 family. The sequence mainly covered the N-Terminal region of the matching peptide. All the three BGL proteins ie. BGL1, BGL3 and BGL5 were purified by chromatography an electro elution from Native PAGE gels and were subjected to MALDI-TOF mass spectrometric analysis. The results showed that BGL1 peptide mass matched to . β -glucosidase-I of Aspergillus flavus which is a 92kDa protein with 69% protein coverage. The glucose tolerant β -glucosidase BGL5 mass matched to the catalytic C-terminal domain of β -glucosidase-F from Emericella nidulans, but the protein coverage was very low compared to the size of the Emericella nidulans protein. While comparing the size of BGL5 from Aspergillus unguis, the protein sequence coverage is more than 80%. BGL F is a glycosyl hydrolase family 3 protein.The properties of BGL5 seem to be very unique, in that it is a GH3 β -glucosidase with a very low molecular weight of ~10kDa and at the same time having catalytic activity and glucose 122 tolerance which is as yet un-described in GH β -glucosidases. The occurrence of a fully functional 10kDA protein with glucose tolerant BGL activity has tremendous implications both from the points of understanding the structure function relationships as well as for applications of BGL enzymes. BGL-3 showed similarity to BGL1 of Aspergillus aculateus which was another GH3 β -glucosidase. It may be noted that though PCR could detect GH1, GH3 and GH5 β-glucosidases in the fungus, the major isoforms BGL1 BGL3 and BGL5 were all GH3 family enzymes. This would imply that β-glucosidases belonging to other families may also co-exist in the fungus and the other minor isoforms detected in zymograms may account for them. In biomass hydrolysis, GT-BGL containing BGL enzyme was supplemented to cellulase and the performances of blends were compared with a cocktail where commercial β- glucosidase was supplemented to the biomass hydrolyzing enzyme preparation. The cocktail supplemented with A unguis BGL preparation yielded 555mg/g sugar in 12h compared to the commercial enzyme preparation which gave only 333mg/g in the same period and the maximum sugar yield of 858 mg/g was attained in 36h by the cocktail containing A. unguis BGL. While the commercial enzyme achieved almost similar sugar yield in 24h, there was rapid drop in sugar concentration after that, indicating probably the conversion of glucose back to di-or oligosaccharides by the transglycosylation activity of the BGl in that preparation. Compared this, the A.unguis enzyme containing preparation supported peak yields for longer duration (upto 48h) which is important for biomass conversion to other products since the hydrolysate has to undergo certain unit operations before it goes into the next stage ie – fermentation in any bioprocesses for production of either fuels or chemicals.. Most importantly the Aspergillus unguis BGL preparation yields approximately 1.6 fold increase in the sugar release compared to the commercial BGL within 12h of time interval and 2.25 fold increase in the sugar release compared to the control ie. Cellulase without BGL supplementation. The current study therefore leads to the identification of a potent new isolate producing glucose tolerant β - glucosidase. The organism identified as Aspergillus unguis comes under the Aspergillus nidulans group where most of the GT-BGL producers belong and the detailed studies showed that the glucose tolerant β -glucosidase was a very low molecular weight protein which probably belongs to the glycosyl hydrolase family 3. Inhibition kinetic studies helped to understand the Ki and it is the second highest among the nidulans group of Aspergilli. This has promoted us for a detailed study regarding the mechanism of glucose tolerance. The proteomic 123 analyses clearly indicate the presence of GH3 catalytic domain in the protein. Since the size of the protein is very low and still its active and showed glucose tolerance it is speculated that this could be an entirely new protein or the modification of the existing β -glucosidase with only the catalytic domain present in it. Hydrolysis experiments also qualify this BGL, a suitable candidate for the enzyme cocktail development for biomass hydrolysis