The economics of inclusion body processing

Many recombinant proteins are often over-expressed in host cells, such as Escherichia coli, and are found as insoluble and inactive protein aggregates known as inclusion bodies (IBs). Recently, a novel process for IB extraction and solubilisation, based on chemical extraction, has been reported. While this method has the potential to radically intensify traditional IB processing, the process economics of the new technique have yet to be reported. This study focuses on the evaluation of process economics for several IB processing schemes based on chemical extraction and/or traditional techniques. Simulations and economic analysis were conducted at various processing conditions using granulocyte macrophage-colony stimulating factor, expressed as IBs in E. coli, as a model protein. In most cases, IB processing schemes based on chemical extraction having a shorter downstream cascade demonstrated a competitive economic edge over the conventional route, validating the new process as an economically more viable alternative for IB processing.

Relation between cell disruption conditions, cell debris particle size, and inclusion body release

The efficiency of physical separation of inclusion bodies from cell debris is related to cell debris size and inclusion body release and both factors should be taken into account when designing a process. In this work, cell disruption by enzymatic treatment with lysozyme and cellulase, by homogenization, and by homogenization with ammonia pretreatment is discussed. These disruption methods are compared on the basis of inclusion body release, operating costs, and cell debris particle size. The latter was measured with cumulative sedimentation analysis in combination with membrane-associated protein quantification by SDS-PAGE and a spectrophotometric pepticloglycan quantification method. Comparison of the results obtained with these two cell debris quantification methods shows that enzymatic treatment yields cell debris particles with varying chemical composition, while this is not the case with the other disruption methods that were investigated. Furthermore, the experiments show that ammonia pretreatment with homogenization increases inclusion body release compared to homogenization without pretreatment and that this pretreatment may be used to control the cell debris size to some extent. The enzymatic disruption process gives a higher product release than homogenization with or without ammonia pretreatment at lower operating costs, but it also yields a much smaller cell debris size than the other disruption process. This is unfavorable for centrifugal inclusion body purification in this case, where cell debris is the component going to the sediment and the inclusion body is the floating component. Nevertheless, calculations show that centrifugal separation of inclusion bodies from the enzymatically treated cells gives a high inclusion body yield and purity. (C) 2004 Wiley Periodicals, Inc.

Inherent size constraints on prokaryote gene networks due to "accelerating" growth

Networks exhibiting accelerating growth have total link numbers growing faster than linearly with network size and either reach a limit or exhibit graduated transitions from nonstationary-to-stationary statistics and from random to scale-free to regular statistics as the network size grows. However, if for any reason the network cannot tolerate such gross structural changes then accelerating networks are constrained to have sizes below some critical value. This is of interest as the regulatory gene networks of single-celled prokaryotes are characterized by an accelerating quadratic growth and are size constrained to be less than about 10,000 genes encoded in DNA sequence of less than about 10 megabases. This paper presents a probabilistic accelerating network model for prokaryotic gene regulation which closely matches observed statistics by employing two classes of network nodes (regulatory and non-regulatory) and directed links whose inbound heads are exponentially distributed over all nodes and whose outbound tails are preferentially attached to regulatory nodes and described by a scale-free distribution. This model explains the observed quadratic growth in regulator number with gene number and predicts an upper prokaryote size limit closely approximating the observed value. (c) 2005 Elsevier GmbH. All rights reserved.