| Key genes represent genes that are extremely important for organisms' living activity and decide their specific phenotype or adaptability to environments.Such genes also include essential genes deciding living and growth of cells or species,and all essential genes involved in this dissertation are genes essential for growth under optimized living conditions of cells.Focusing on those vital genes existing in prokaryotes ans eukaryotes,this dissertation launched following innovative researches.This thesis launched comparative genomics researches based on aerobe and anaerobe of prokaryotes firstly.Through studies on genome level,transcriptomics level and phylogenetic developmental level,we compared and analyzed clusters of orthologous groups(COG)of 147 aerobe and 147 anaerobe.Combining enzyme information after annotation in KEGG and literature mining methods,we further located27 COGs that might influence oxygen usage.The comparative analysis using protein interaction and the comparison of step length in metabolism networks both supported that they can be related to oxygen usage.Considering the divergence time of organisms used and the phylogenetic tree,we validated the time of appearance of oxygen and oxygen-using genes.Secondly,we researched the issues surrounding the recognization of essential genes.Essential genes are a particular class of key genes since they own the importance and essentiality for organisms to maintain their living activity.Their loss can lead cells to death or cease their growth.This thesis updated a standard software named Geptop constructed to predict essential prokaryotic genes and increase the quantity of referred organisms.The original formula for calculating is improved,and the running speed of this prediction software is advanced through the introduction of multi-processing.The updated Geptop can obtain a notable increase of AUC(Area Under Curve),especially reaching 0.956 when predicting essential genes of Escherichia coli.This new version also showed certain stability when changing the genetic distance between genomes.Then we investigated the database of essential genes of multiple species.Essential genes used to be stored in a single gene,while orthologous essential genes can be clustered into different groups divided by genes' function and evolutionary persistence.This thesis updated one database-CEG(Cluster of Essential Genes)constructed by our group in 2013 and added 13 prokaryotes,especially added the drug-related information of essential prokaryotic genes.We grouped them into clusters for newly added eukaryotic essential genes and especially grouped human essential genes into clusters,including multiple cell-lines.Updated CEG also added CEG?Match 2.0 for predicting essential eukaryotic genes,which can realize functions of predicting the essentiality of genes by inputting sequences and gene names.The last part is the theoretical prediction and experimental validation of essential genes of human cancer cell lines.This thesis used the feature-integration method to predict theoretically.Thus totally 958 features were adopted,and the AUC reached 0.96.We also adopted CRISPR-Cas9 technology to validate newly predicted 181 essential genes in 7 cell lines,including Hela.The results of Hela validated the reliability of the predictions.To sum up,our study investigates a series of studies surrounding key genes of different organisms from prokaryotes to eukaryotes and human.We explored and detected the key genes influencing the adaptability of microorganisms to oxygen environment,and also updated the algorithm named Geptop for predicting the prokaryotic essential genes group.Furthermore,the updated database of CEG containing prokaryotic and eukaryotic essential genes is constructed.Finally,to predict human essential genes missed before,we re-predicted essential genes theoretically using integrated features.Our work could help people understand the genetic construction of organisms and environmental adaptation,and benefit the selection of drug-targeted genes. |
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