融合基因表达差异的生物代谢变化预测

目的当前存在许多基于约束的优化建模方法利用全基因组的代谢网络来预测生物代谢流量分布,而几乎所有的这些建模方法都需要代谢物的摄取和分泌速率以及生物先验知识的信息,比如假设生物量或者ATP产量最大。但是由于多细胞生物代谢物的摄取和分泌速率的测量很困难,并且多细胞高等生物的不同组织通常有不同的代谢目标,所以很难确定一个合理的代谢目标来建模研究高等生物的代谢。本文利用基因表达的差异信息和代谢网络,能够预测单细胞或多细胞生物代谢流量的变化,不需要生物的先验知识和代谢物分泌或摄取速率的信息。方法模型假设在两点状态下,如果编码酶的基因表达量存在显著变化,则酶催化的反应的代谢流量也应该存在显著变化。利用微阵列基因组学数据和生物全基因组的代谢网络,通过使生物代谢流量的变化与基因表达的变化尽可能一致来建立优化模型,预测生物显著差异的代谢流量分布。结果模型利用在有氧恒化器中以不同稀释速率生长的大肠杆菌的基因表达数据,预测的代谢流量与实验中实际测量的代谢流量一致。结论本文提出的基于约束的建模方法可以简单准确地定性预测低等生物的代谢流量变化,为研究高等生物的代谢变化提供了有效途径。

Prediction of altered metabolism for expression differences in fusion genes

Objective Currently,there are many methods for constraint-based optimization modelling using whole-genome metabolic networks to predict the metabolic flux distribution.Most of these methods require rates of metabolite uptake and secretion,as well as a priori knowledge of biological parameters,including biomass and the highest level of ATP production.However,measuring the rates of metabolite uptake and secretion in complex multicellular organisms is very challenging and is further complicated by different tissues having different metabolic goals.This presents a significant obstacle in modelling the metabolism of higher organisns.This study predicts the changes in metabolic flux in both unicellular and multicellular organisms using differences in gene expression and metabolic networks,a method which does not require a priori knowledge or information on the rate of metabolite uptake or secretion of organisms.Methods The present study hypothesized that in a two-state model,significant changes in the expression of an enzyme-encoding gene should result in a significant change in the metabolic rate of the enzyme-catalysed reaction.Microarray analysis of genomic data and whole-genome metabolic networks was used to construct an optimization model that could predict significant changes in the distribution of metabolic flux by maximizing the consistency between changes in metabolic flux and gene expression.Results The nodel predicted significant changes in metabolic flux,which were consistent with the experimental measurements of metabolic flux changes in aerobic chemostat cultures of Escherichia coli under different dilution rates.Conclusions The present study proposes a constraint-based modelling method that can accurately predict qualitative changes in flux for lower organisms and provides an effective way to investigate the altered metabolism of higher organisms.