Analysis And Applications Of Omics Data For Biological Part Prediction And Genome-scale Metabolic Model Reconstruction In Zymomonas Mobilis

The rapid development of sequencing technology has resulted in the continuous production and accumulation of a large amount of systems biology data,which brings opportunities for life science research and challenges for processing and mining big bio-data as well.Zymomonas mobilis is a Gram-negative bacterium known to produce ethanol naturally using the Entner-Doudoroff（ED）pathway under anaerobic condition.Due to the special physiological characteristics,Z.mobilis has been developing as cell factories for producing cellulosic ethanol and diverse biochemicals.A mount of systems biology data has been keeping on accumulation during research to understand physiology,metabolism,and genetic characteristics of Z.mobilis,including genomic,transcriptomic,proteomic,metabolomic,and phenomics data as well as metabolic network models.However,these systems biology data have not been fully analyzed and mined,while new datasets are rapidly and continuously generated at the same time.Methods to process and analyze these datasets efficiently are extremely needed,which will help the rational design and promote the applications of cell factory of Z.mobilis.In this study,we established a genome resequencing and transcriptome sequencing analysis process workflow based on next-generation sequencing data using an acidic-p H-tolerant mutant strain of Z.mobilis obtained by adaptive evolution as an example.By comprehensive analysis the differences at the genome and transcriptional levels,the possible underlying mechanism of the mutant strains tolerance to low p H environment was proposed,and the phenotype of acidic p H tolerance was associated with mutations in four related genes.Third-generation sequencing and second-generation sequencing data of Z.mobilis self-flocculating strain ZM401 were then combined,and an automatic analysis workflow was established to detect single nucleotide variation（SNVs）and structural variations（SVs）.Combining with the transcriptomic data,we suggested that the SNVs of ZMO1082 and ZMO1055 may be the key factors to the self-flocculation characteristics of ZM401.Based on above works,an automatic data collection,processing,and analysis pipeline was established.588 transcriptome and 4 proteome samples including those from public databases and laboratory databases of Z.mobilis were then collected and processed for subsequent data mining,which also laid the foundation for construction of systems biology database of Z.mobilis.This study also mined and predicted biological elements using accumulated omics data and bioinformatics approaches.Combining transcriptomic and proteomic data,38 promoters of varying strength were predicted,including 19 strong,10 medium,and 9 weak ones.Further experimental verification was carried out using the dual fluorescent reporter gene system,and the results showed that there was a high correlation between the predicted results and the experimental results with an R² of 0.7.Meanwhile,10 ethanol-inducible promoters were predicted using the transcriptomic and proteomic data from ethanol stress and growth-related datasets with three of them verified.In addition,this study predicted and verified 7 pairs of s RNA-UTR interactions combining bioinformatics analysis and the dual fluorescent reporter gene system.Except for 3 pairs that could not be obtained in the experiment,the rest 4 of them were validated using the dual fluorescent reporter gene system,which was consistent with the predicted results.This study enriches the biological part library of Z.mobilis and demonstrates the feasibility of predicting and selecting candidate biological parts based on omics datasets and bioinformatics tools.To addressing the problems,such as of incomplete genome information or error of unlimited energy generation that existed in the published models,the latest Z.mobilis metabolic network model was established based on the latest genomic information as well as Biolog phenotypic and biochemical experimental datasets.After manual iterative curation,a high-quality genome-scale metabolic network model i ZMYF＿516 was obtained,which contains 516genes,1389 reactions,1433 metabolites,and 3 cell compartments.This model is the best one containing the largest number of genes,reactions,and metabolites as well as the highest MEMOTE score.This model laid the foundation for future model improvement to integrate enzymatic,transcriptomic,or proteomic constraints,and provided strategy for future metabolic engineering.Based on this model,the xylose metabolism and CO₂ fixation using Gnd–Entner–Doudoroff（GED）cycle pathway in Z.mobilis were simulated and analyzed,and the results suggested that the yield of succinate,citrate,pyruvate,acetate,and acetoin of CO₂ fixation strain were improved 2 folds than the reference strain,which indicated the potential target products in CO₂ fixation strain.Finally,based on this model,a minimal genome metabolic network model was proposed,which containing 220 genes only without affecting biomass synthesis.In summary,the automatic analysis workflow for genome and transcriptome data generated from high-throughput sequencing technology were established in this study,which was applied to the genome and/or transcriptome analysis of a variety of microorganisms.Subsequently,using an automated spider,systems biology data of Z.mobilis in public database were collected and analyzed.Different biological parts were then mined and predicted using transcriptomic and proteomic data and bioinformatics methods,and the accuracy of the prediction was verified by experiments.Finally,based on the genomic and phenotypic data,a high-quality genome-scale metabolic network model and a minimal genome model were established,which laid the foundation for metabolic engineering and genome synthesis of Z.mobilis.This study established an efficient automatic analysis process for genotype-phenotype association studies of Z.mobilis,and provided a guidance to collect,integrate,mine omics datasets of other microorganisms for biological-part prediction and diverse applications in metabolic engineering and genome engineering.