Genomic and proteomic perspectives in cell culture engineering

Journal of Biotechnology 94 (2002) 73 – 92 www.elsevier.com/locate/jbiotec

Genomic and proteomic perspectives in cell culture engineering Rashmi Korke a, Anette Rink b, Teck Keong Seow c, Maxey C.M. Chung c,d, Craig W. Beattie b, Wei-Shou Hu a,* a

Department of Chemical Engineering and Materials Science, Uni6ersity of Minnesota, Minneapolis, MN, USA b Department of Animal Biotechnology, Uni6ersity of Ne6ada, Reno, NV, USA c Bioprocessing Technology Centre, National Uni6ersity of Singapore, Singapore d Department of Biochemistry, National Uni6ersity of Singapore, Singapore Received 16 November 2000; received in revised form 16 August 2001; accepted 24 August 2001

Abstract In the last few years, the number of biologics produced by mammalian cells have been steadily increasing. The advances in cell culture engineering science have contributed significantly to this increase. A common path of product and process development has emerged in the last decade and the host cell lines frequently used have converged to only a few. Selection of cell clones, their adaptation to a desired growth environment, and improving their productivity has been key to developing a new process. However, the fundamental understanding of changes during the selection and adaptation process is still lacking. Some cells may undergo irreversible alteration at the genome level, some may exhibit changes in their gene expression pattern, while others may incur neither genetic reconstruction nor gene expression changes, but only modulation of various fluxes by changing nutrient/metabolite concentrations and enzyme activities. It is likely that the selection of cell clones and their adaptation to various culture conditions may involve alterations not only in cellular machinery directly related to the selected marker or adapted behavior, but also those which may or may not be essential for selection or adaptation. The genomic and proteomic research tools enable one to globally survey the alterations at mRNA and protein levels and to unveil their regulation. Undoubtedly, a better understanding of these cellular processes at the molecular level will lead to a better strategy for ‘designing’ producing cells. Herein the genomic and proteomic tools are briefly reviewed and their impact on cell culture engineering is discussed. © 2002 Elsevier Science B.V. All rights reserved. Keywords: Cell culture; Genomic; Proteomic; Bioreactor; Adaptation

1. Introduction

* Corresponding author. Tel.: + 1-612-625-0546; fax: +1612-626-7246; http://hugroup.cems.umn.edu. E-mail address: [email protected] (W.-S. Hu).

Mammalian cell culture is widely used for manufacturing many recombinant proteins for therapeutic, diagnostic and research uses. Recently many new cell culture products joined classical

0168-1656/02/$ - see front matter © 2002 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 8 - 1 6 5 6 ( 0 1 ) 0 0 4 2 0 - 5

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ones such as erythropoietin, factor VIII and tissue plasminogen activator in the list of approved biologics for clinical use. Notable examples are antibodies against respiratory syncitial virus, antibodies for preventing platelet aggregation during angioplasty and for inhibiting binding of tumor necrosis factor (TNF) to TNF receptor in rheumatoid arthritis. Two decades ago, it was the development of those then-new potential recombinant therapeutic proteins that propelled the transition of cell culture technology from a relatively empirical field to a new inter-disciplinary profession. Prior to the arrival of those recombinant products, cell culture process was primarily used for the production of viral vaccines, cytokines and other biologics. Roller bottles were the norm for manufacturing, although large stirred tanks were also used in some processes. Nascent efforts in cell line and defined-medium development and in culture system characterization seemed to be undertaken for satisfying inquisitive minds rather than being compelled by commercial needs of technology development. The advent of first generation rDNA cell culture products transformed the landscape by drawing a large number of scientists and engineers into the profession and driving a steady infusion of resources from both private and public sectors in the past two decades. The rapid growth in our knowledge of the physiology and molecular biology of cells in culture is astounding. The speed at which engineering knowhow in cell culture expanded is unprecedented in bioprocessing history. Within a short span of a little over a decade, the host cell lines were well characterized; the introduction of heterologous genes and the generation of stable clones became routine; the adaptation of cells to grow in suspension and serum-free medium became common practice. As cell culture processing for rDNA products became mature, technology also began to influence the production of traditional products, especially viral vaccines for human and veterinary applications. The development of viral vectors for gene therapy, for which the manufacturing process resembles that for viral vaccines, also contributed to bring new cell culture processing technology to that more traditional sector. The

arrival of genomic and proteomic technologies opens up many new opportunities for exploration. We believe they may well introduce another era of innovations and growth in cell culture process technology. In this paper, we will briefly review the major issues in cell culture processing and assess the potential impact of genomics and proteomics.

2. A brief review of cell culture bioengineering issues In the past decade the number of cell culture based products has been increasing rapidly. Nevertheless, the host cell lines used for their production are still confined to a few. The small repertoire of cell lines for bioprocessing has resulted in a convergence of process development issues and allowed for a typical path of cell culture process and product development to emerge. Since suspension culture in a stirred tank is the preferred mode of cultivation, in most cases, cells are initially adapted to growth in suspension. This is followed by medium development to allow for serum-free or even animal component-free medium growth. Subsequent refinement of cultivation conditions, possibly through fed-batch cultivation or perfusion culture, aims to enhance productivity or product quality. It is not unusual to switch a culture from a growth phase to a production phase for enhancing the productivity. In the following section, some of those issues will be briefly reviewed.

2.1. Selection of producing cells Normal diploid cells are the predominant workhorses for manufacturing human viral vaccines (Fletcher et al., 1998). The use of a continuous cell line for human vaccine production is still rather limited. Safety concerns often thwart the use of immortalized cells, or cells adapted to suspension growth (Petricciani, 1992). No transformed cells are used for that purpose at all. However, for gene therapy applications most investigations or current clinical trials employ a continuous or even transformed cell line in viral

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vector production (Wu and Ataai, 2000). In contrast, most of the mammalian recombinant proteins are produced using continuous cell lines (Chu and Robinson, 2001), which are more amenable to genetic manipulation than normal diploid cell strains. The cell strains and cell lines most commonly used in the production of cell culture products are listed in Table 1. For virus production, the repertoire of cell lines for manufacturing is relatively large although many are used for veterinary vaccine manufacturing. In contrast, the number of cell lines used for the production of rDNA proteins and viruses for gene therapy is rather small. For these applications, extensive genetic manipulations and relatively laborious clone selection and culture condition adaptation are performed for each product (Geisse et al., 1996). It is, therefore, advantageous to have only a small number of host cell lines. Once the methodology of cell line and process development is established, it is likely to be generally applicable to a new product. With the small number of cell lines in the repertoire as common host cells, it is also easier to muster resources to genetically introduce desired characteristics. In the last decade, much effort has been put forth that has aimed to introduce desirable characteristics into those commonly used cell lines, whereas few attempts have been made to search for a better host line. A notable example of the latter is the development of PER helper cells for adenovirus

production (Fallaux et al., 1998). The new cell line confers the helper function, which allows production of the replication defective viral vector, but is void of any overlapping sequences with the vector. This prevents the potential homologous recombination between adenovector and the helper cells, thus ensuring the safe production of replication deficient adenovirus. Some of the examples of improving characteristics of cell lines are the introduction of E2F-1 into CHO cells to disturb cell cycle regulation and to alter their growth factor requirements in culture (Lee et al., 1996b) or introduction of genes interfering with apoptotic regulation in an attempt to enhance the viability of cells in culture (Alnemri et al., 1992; Itoh et al., 1995; Murray et al., 1996; Singh et al., 1996). For production of recombinant products, the selection of a stable and high producing clone after the introduction of a heterologous gene encoding for the product is crucial to its success. The difference in the specific productivity of the initial cell pool after transfection and the final stable producing clone may be as much as two orders of magnitude. Many factors affect the specific productivity of the cell and the stability of the heterologous gene like the integration site, copy number of the gene or genes and the number of replication cycles the cells remain stable. The introduced heterologous genes and the selection marker are usually integrated in the chromosome of the stable clones (Wurm, 1990; Wurm et al.,

Table 1 Major cell lines used in the production of biologics Cell line

Description

Products

MRC-5, WI-38

Normal diploid human fibroblast Monkey kidney epithelial cells Swine testicular epithelial cells Chinese hamster ovary cells

Human viral vaccines

Vero ST CHO BHK 293 Myelomas, hybridomas

Baby hamster kidney cells Transformed human kidney epithelial cells Transformed mouse B cells

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Human and veterinary viral vaccines Veterinary viral vaccines rDNA proteins (tissue plasminogen activator, erythropoietin, many monoclonal antibodies, Factor VIII) Factor VIII, Veterinary viral vaccines rDNA proteins (Protein C), adenovirus Many monoclonal antibodies

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1992; Wurm and Petropoulos, 1994). The site of chromosomal integration affects both stability and productivity. It has been reported that integration near the telomere of the chromosome gives rise to a higher rate of dislodging from the chromosome and loss of productivity. The locus of integration on the chromosome may also affect or be affected by the expression of adjacent genes. Thus, a high producer may not have multiple copies of the heterologous genes, but one that is integrated at a favorable site. It has been shown that retrotargeting may help in efficient integration of a heterologous gene in a transcriptionally active site (Wurm et al., 1996). A number of genetic modification tools used to improve productivity of mammalian cells have been reviewed recently (Fussenegger et al., 1999). In addition to gene integration and transcription, translation, protein processing and the secretion apparatus affect the expression level of a gene product. Stable productivity for antibody producing cells is complex, since separate genes, which are integrated and regulated independently, encode heavy and light chains. Secretion of the mature antibody into the supernatant depends on accurate assembly of the chains in the endoplasmic reticulum (Martial-Gros et al., 1999; Strutzenberger et al., 1999). Abundant endoplasmic reticulum and mitochondria may have a positive effect on productivity. Cloning of b-Galactoside a-2,6-Sialyltransferase (Lee et al., 1989b) and b-1,4-galactosyltransferase, a-2,3-Sialyltransferase (Weikert et al., 1999) into CHO cells had a positive effect on increasing sialic acid content in glycosylated protein. Increased sialic acid content in glycoprotein is generally associated with improved pharmacokinetic performance of the protein. The results reinforced the notion that protein-processing apparatus does affect product quality.

2.2. Adaptation of cells to process culture en6ironment After transfecting host cells with the heterologous gene and the selection of desired clones, cells are adapted to grow in the culture environment used in production. Adaptation generally

involves passaging cells over a relatively long period to allow them to gradually develop the ability to grow under a new chemical and physical environment. Presence or absence of a physical surface or support for attachment is probably the most drastic difference in the cell’s cultivation conditions. Most normal diploid cells used for virus production are strictly anchorage dependent (Hayflick and Moorhead, 1961; Jacobs et al., 1970). They are cultivated on surfaces such as parallel plates, or revolving multiple plates, or on microcarriers when a stirred tank reactor is used (Spier and Kadouri, 1997). Some cells like myelomas and hybridomas are suspension cells that can be readily grown in a mixing vessel. Most cell lines commonly used for recombinant protein production are derived from adherent cells (Sinacore et al., 2000). Although not being strictly anchorage dependent, they often prefer adherent growth given a compatible surface. However, for large-scale operations, suspension growth using a stirred tank is the preferred mode of cultivation. It thus necessitates adaptation of these anchoragepreferred cells to grow in suspension (Sinacore et al., 2000). In some cases, host cells are adapted to suspension growth prior to transfection of a vector containing the heterologous gene. More often than not, the adaptation is carried out after clones are selected. When cultivated in suspension, the unadapted cells either fail to grow or form large aggregates with extensive cell–cell contacts and intercellular adhesion (Peshwa et al., 1993). The viability of cells in large aggregates is often rather low. Adapted cells, on the other hand, appear almost like regular suspension cells, such as hybridoma, when cultivated in a stirred tank or shaken flask. Not all suspension-adapted cells bear the same characteristics in terms of their ability to adhere to surfaces. Many suspension-adapted cells do not attach to a surface firmly even when provided with conditions favoring attachment (such as plating on extracellular matrix coated surface), while others readily revert to adherent growth when provided with a compatible surface. Thus, there appear to be different paths of physiological change during the adaptation to lead to a similar phenotype of suspension growth.

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To date, most cell transfection processes and subsequent cell clone selection are carried out in serum-containing medium. The inclusion of animal serum, typically from bovines, in growth medium poses many processing issues. In addition to the cost, the quality of serum may fluctuate. Furthermore, serum introduces a large array of molecules or even adventitious agents into the culture, which must be removed in product purification. A lot of effort has been devoted to developing serum-free media for various industrially important cell lines and processes. Many of these media formulations still contain animal protein lysate or tissue extracts. With some effort, most industrial cell lines can be adapted to grow in these media. In recent years, the possibility of prion contamination of animal-derived medium supplement became a real concern after the incidents of bovine spongy form encephalopathy. To eliminate the risk of transmission of such molecules into humans through the product, it is highly desirable to remove all components of animal origin from the manufacturing medium. It is even more desirable to have a completely defined medium for which every component is well characterized and is attainable reproducibly from reliable sources. Although, this is still not a prevailing practice at an industrial scale, major strides have been made in that direction, especially for hybridoma cells. Examples of the transition of cultivation conditions from adherent growth in a highly enriched serum-containing medium to suspension growth in a defined medium are found in literature (Berg et al., 1993; Sinacore et al., 2000). This transition surely incurs drastic changes in cellular physiology. In spite of the obvious advantages of process simplicity of suspension growth and benefits of eliminating animal source components from a public health point of view, our understanding of such physiological changes is still scarce.

2.3. Enhancing producti6ity by process engineering The cells can attain many physiological changes without extensive adaptation or genetic manipulation by controlling process variables. The manipu-

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lation of cellular physiology via the control of process variables in a bioreactor is the key to high productivity and high product quality. Calcium concentration was used to manipulate recombinant human cells (293 cells) to grow as aggregates or suspension cells (Peshwa et al., 1993). Since aggregated cells are easier to retain in the reactor (by using a settling device) in a continuous flow system, manipulating calcium concentration may be an easy and effective way of facilitating cell recycle and enhancing productivity. Fed-batch culture has been used extensively to prolong the culture period and to enhance the productivity. There is evidence that the metabolic state of cells is different in batch and fed-batch culture as indicated by nutrient consumption (Nadeau et al., 2000; Portner et al., 1996; Sanfeliu et al., 1996). In other fed-batch cultures, the concentration of glucose or glucose and glutamine was controlled at low levels resulting in a metabolic shift from a high lactic acid producing state to a low lactic acid producing state (Gambhir et al., 1999; Siegwart et al., 1999; Zhou et al., 1997). In the metabolically shifted state, the specific glucose consumption rate is reduced and the stoichiometric ratio of lactate produced to glucose consumed is also reduced. Culture conditions also influence the distribution of glycoforms of recombinant proteins. A lot of effort has been devoted to investigate the effect of culture conditions on the glycosylation pattern (Andersen and Goochee, 1994; Gawlitzek et al., 1995; Goochee et al., 1991; Goochee and Monica, 1990; Jenkins et al., 1996; Nyberg et al., 1999), especially on the sialylation of oligosaccharide moieties on glycoproteins as the glycans play an important role in protein function (Cumming, 1992). The results of these studies on the effect of culture condition on glycosylation appear to be specific for a cell line or for a particular product. Deriving general conclusions from such studies awaits more systematic studies. However, it does appear that introduction of stress conditions like culturing at an elevated osmolality (Oh et al., 1995; Oyaas et al., 1994; Ozturk and Palsson, 1991) or the presence of a moderate concentration of sodium butyrate (Oh et al., 1993; Santell et al., 1999) does enhance the specific productivity and

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Table 2 Physiological changes in cell line and process development Cause of physiological change

Gene expression profile change

Reversible phenotype

Genotype change

Process control Adaptation Cell line/clone selection

Yes/No Yes Yes

Yes Yes/No No

No Yes/No Yes

positively affect sialic acid content. One can postulate that various stress induced chaperon proteins exert positive effects on protein processing resulting in higher productivity and sialic acid content.

3. Cell physiology from a global perspective As discussed in the previous section, in the course of product development, cells undergo major physiological changes during clone selection and adaptation to new culture environment. In optimizing medium and reactor culture conditions, their physiological state is also being directed toward conditions favoring higher productivity. The phenotypic representation at various physiological states may be readily reversible in a short time scale, or it may be ‘permanent’ and cannot be reverted easily. At the molecular level, the physiological change may be the manifestation of genotypic modification, or it may incur only variations in gene expression without any genotypic changes (Table 2). It is possible that a physiological change invokes no change at the genome, transcription, and translation levels, but merely involves changes in the rate of biochemical reactions caused by alterations in the intracellular and extracellular concentrations of substrates/metabolites. After all, a change in reaction rate does not necessitate change in enzyme concentrations. The physiological changes resulting from adaptation and selection are likely to incur structural change at the DNA level, such as deletion, translocation, amplification or other forms of mutation. These molecular changes are not expected to be abolished by merely reverting culture conditions. In some cases, the phenotypic behavioral

changes seen in cell culture processing may be readily reversible while in others, once the phenotype is altered, it is ‘permanent’. For example, once adapted to suspension growth, some cells still retain the ability to adhere and multiply if suitable surfaces are provided; they can be reverted to grow in suspension when the surface is removed. Whereas for other clones, the ability to adhere to and spread on surfaces is completely lost. The physiological changes resulting from the control of process variables in bioreactors are generally reversible and incur no gross genotypic changes. Many continuous lines used in cell culture processing may not have a constant genotype in the traditional sense since cytological aberrations occur frequently. Hence, the rate at which the physiological change or its reversal occurs may also vary. Some cells change their oxygen uptake rate in response to changes in glucose concentration almost instantaneously (Frame and Hu, 1985). On the other hand, a physiological change (for example, the change from a high to low lactate producing state in a fed-batch culture) occurs only relatively slowly over a few doubling times (Europa et al., 2000). Such physiological changes induced by culture process variables may or may not incur changes in a gene expression pattern. It is plausible that small and rapid changes in metabolic state may not involve a change in cellular machinery or gene expression pattern; whereas slow but reversible physiological changes may involve gene expression pattern alteration. These gene expression changes may occur at transcription, transcript processing, translation, or post-translational level. In the case that a physiological change invokes gene expression pattern alteration, it is the resulting protein profile that gives the manifestation of ‘physiol-

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ogy’. There are ample examples that changes in a single gene product alter physiological events drastically (Davies, 1993; Lee et al., 1989a), especially through gene knockout studies (Mayford et al., 1995; Sapirstein and Bonventre, 2000). However, it is important to keep in mind that the physiological state is the manifestation of the expression level of numerous genes at various levels of regulatory hierarchy. Therefore, any physiological change is likely to be effected by alteration of a battery of genes, rather than by a single gene. It is also very likely that the genes whose expression profiles are altered in a physiological change may include not only the ones immediately related to the physiological phenomenon, but also those which are only peripherally related or seemingly unrelated. Let us take the case of cells adapted to growth in suspension. It is most likely that the expression of genes involved in cell adhesion, extracellular matrix and cytoskeleton organization have been changed. In effecting these changes, the expression of regulatory elements controlling their expression may have been altered. Many cellular events like cell growth cycle and distribution of organelles are closely related to cytoskeleton structure, cell adhesion and even cell shape. It is possible that their expression is also affected when cells are adapted to suspension growth. If the expression of regulatory genes is altered, the affected downstream genes may not be restricted to those directly or indirectly related to growth in suspension, but also encompass many ‘collateral’ ones that have no direct relation to growth in suspension. Similarly, even if a genetic change involves only a single gene, it may have an indirect effect on the expression of other genes. For example, a mutation causing a faster reaction rate may result in an increased intracellular concentration of an intermediate that in turn may influence the expression of other genes directly through feedback regulation or may indirectly modulate the expression of other genes to cope with the changed intracellular environment. It suffices to say that many detectable physiological changes

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are preceded and accompanied by change in expression pattern of an array of genes. A global survey or comparison of gene expression profiles in cells undergoing the physiological change can provide great insight into the regulatory structure, i.e. the genes that are directly or indirectly involved. It may also pave the way for optimizing genetic intervention by delivering genes conferring desired phenotypes without other neutral or undesirable co-lateral effects. Such tailored gene expression modulation, either at host cell or post-clonal selection level, will certainly facilitate new product development. With the advances in genomic and proteomic tools, genome-wide surveillance of gene expression is becoming increasingly feasible. These new tools have the potential of profoundly changing cell culture processing in the coming decade.

4. Differential gene regulation It is estimated that between 35 000 and 50 000 genes are encoded in a mammalian genome (International Human Genome Sequencing Consortium, 2001; Venter et al., 2001). At any given time, only a fraction is expressed in a particular cell to give rise to thousands of different proteins and their modified variants. The pattern of gene expression varies as cells differentiate into different types, organized into different tissues and organs (Crollius et al., 2000; Ewing and Green, 2000; Fields et al., 1994; Liang et al., 2000). By and large, such a differentiation and development coupled gene expression change is a ‘committed’ path for mammalian cells. Unlike bacterial or fungal differentiation, in which the differentiated state (such as a spore bearing state) can revert to a vegetative state, the terminally differentiated cells do not revert to an entirely undifferentiated state readily. Upon transformation or in vitro immortalization to become permanently transformed or a continuous cell line, they may retain many of their differential characteristics (Petzinger et al., 1994; Ueno et al., 1993), but in general, they tend to lose many differentiated properties. For exam-

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ple, immortalized liver cells and hepatoma derived cell lines retain their ability to synthesize albumin, although they do not retain a high level of biotransformational activities. A given gene executes its function via transcription into transcriptome, which is spliced into mRNA, and translated into protein. There has not been a large amount of data available on the overall relationship between mRNA and protein abundance. Notable examples include the comparison of selected mRNA and protein levels in human liver (Anderson and Seilhamer, 1997) and in yeast (Gygi et al., 1999). Different correlations were found based on different subsets of proteins, suggesting that post-transcriptional regulation of gene expression is a frequent phenomenon rendering the relative abundance of mRNAs and their corresponding proteins far from linear. A gene expression profiling exploration will thus benefit from examination at both transcript and protein levels. In differential gene expression studies, transcriptional analysis holds major advantages over the proteomic based approach in that hybridization of complementary sequences allows one to normalize mRNA species with differing degrees of abundance and to subtract the same species expressed under different growth conditions; furthermore, by polymerase chain reaction (PCR) the nucleotide signals can be amplified by orders of magnitude. However, mRNA analysis will not reveal regulation at translational or post-translational levels. The relative stability of mRNA and proteins may be different in the cell due to differences in rates of degradation. A single transcript may give rise to different proteins by alternative splicing under different conditions. Thus, the two approaches complement each other (HumpherySmith et al., 1997) and an integrated view of transcript and protein expression profiles can give insight into differential gene regulation and help elucidate the gene regulatory elements (Bucher, 1999) or networks (Hatzimanikatis and Lee, 1999). In the following sections, we will review briefly common methods of exploring mRNA and protein expression profiles. Readers are referred to many recent review articles for further details on those methods (Crollius et al., 2000; Ewing and Green, 2000; Fields et al., 1994; Liang et al., 2000).

4.1. Genomics Recent advances in genomics are opening up a new era of research of biological systems. The completion of the first draft of the human genome is monumental (International Human Genome Sequencing Consortium, 2001; Venter et al., 2001). In addition to the human genome, the list of organisms with a completely sequenced genome currently includes yeast, many viruses, bacteria, a small number of species in the animal kingdom (e.g. C. elegans), and is expanding rapidly. Genomic sequencing projects on mice, rats, and pigs are underway (Kyrpides, 1999).

4.1.1. Genetic analysis of the producing cells A number of cells used in the production of biologics are normal diploid cell strains of human (such as MRC-5 (Jacobs et al., 1970), WI-38 (Hayflick and Moorhead, 1961)) and swine (such as testicular epithelial cells ST) origin, or are primary cells (such as chicken embryo fibroblasts). However, majority of the cells used for rDNA protein production are continuous cell lines. Some cells are from organisms whose genomes are being sequenced, including 293 (human) (Graham et al., 1977) and 3T3 (mouse) (Todaro and Green, 1963) cells. Others, such as Vero (monkey) (Earley and Johnson, 1987), BHK (Macpherson and Stoker, 1962) and CHO (Urlaub and Chasin, 1980) (both from hamster), are from organisms for which either no major sequencing effort is underway or the completion of the sequence is still in the distant future. A number of genome sequencing programs are underway for industrially or medically important microorganisms (Nelson et al., 2000). However, similar efforts are unlikely to be launched for the cell lines used in bioprocessing despite the economic value of the products. The effects of cell-aging parameters like DNA methylation and chromatin structure on transcription have been studied extensively in cancer cells. It has been shown that direct physical and functional links exist between these processes (Leonhardt and Cardoso, 2000) and the importance of epigenetically mediated differences in gene expression is becoming more apparent (Rountree et al.,

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2001). How important these cell-aging parameters are in continuous cell lines is unclear, since there seems to be no common denominator regarding how methylation patterns impact the transcription and production efficiency of continuous cell lines. Hence, these studies may or may not yield useful information regarding productivity of the cells. The production level of the specific mRNA can be analyzed using Northern blot. Since the production efficiency of cells in culture depends on the integration site and copy number of the gene, gene copy number and chromosomal integration site can be determined by using fluorescent in situ hybridization (FISH). Karyotype analysis of hybridoma cells using FISH has shown that major rearrangements of chromosome fragments and chromosome numbers occur early in hybridoma culture (Wollweber et al., 2000). These cell lines are known to be aneuploid, with a large variation in the genetic content among different clones and among individual cells of the same clone (Cram et al., 1983). Since the genome size of these mammalian cells is almost the same as that of the host organism, the cost of sequencing their genome will be horrendous and the benefits are unclear. The best way of taking advantage of genomic information is by gene expression pattern profiling to probe key genes effecting important physiological changes.

4.1.2. Differential expression profiling of transcripts At any given time, 10 000– 15 000 distinct mRNA species are present in a typical mammalian cell. Among these, there are 10– 15 supervalent mRNAs representing 10– 20% of total mRNA mass. About 1000 – 2000 mRNAs contribute 40–45% while 15 000– 20 000 complex or rare mRNAs represent the remaining 40– 45% mRNA mass (Bonaldo et al., 1996). This variation in number is an important factor to be kept in mind while selecting a method to identify differentially expressed mRNA as it may be difficult to identify rare differentially expressed transcripts due to the masking effect created by the abundant transcripts. A number of techniques have been developed to probe transcript expression changes. A closed sys-

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tem, such as a hybridization array, allows mRNA expression to be probed against a large number of sequences, with a relatively small amount of cell sample. However, this method only probes the expression of genes for which homologous genes are immobilized on the array. Another method, such as Differential Display (DD), on the other hand, is an open system and is not limited to detect expression patterns of a certain set of genes. It is a discovery technique in which genes, which are differentially expressed, have a higher probability of being isolated. The shortcomings of such techniques are their labor intensiveness and probabilistic nature. It is extremely difficult to cast a very wide net to isolate all the genes that are differentially expressed. These different techniques and their application for high throughput analysis of gene expression in various biological systems have been reviewed in literature (Carulli et al., 1998; Colantuoni et al., 2000; Gray and Collins, 2000). In the following section, a number of major techniques will be briefly described.

4.1.2.1. Hybridization microarrays. Hybridization microarrays have been a revolutionary tool as they provided a systematic way to survey DNA and RNA variation. This innovative technology, its salient features and applications in many diverse fields have been reviewed extensively over the past few years (Braxton and Bedilion, 1998; Epstein and Butow, 2000; Khan et al., 1999a; Nuwaysir et al., 1999; van Hal et al., 2000; Young, 2000). Individual DNA samples are uniquely located using robotics on nylon, glass or silicon substratum to make a microarray or DNA chip. The DNA samples are oligonucleotides (Chee et al., 1996), cDNA sequences (Schena et al., 1995) or Expressed Sequence Tags (ESTs). The oligonucleotides can be synthesized on the substratum by photolithographic patterning using masks, not unlike those used in semiconductor industry to manufacture integrated circuits. High-density oligonucleotide patterns are made on surfaces using in situ combinatorial synthesis by photodeprotection-type synthetic reactions. This technique is best suited towards generating large number of identical arrays because of the high expense in-

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volved in making masks. Oligonucleotides can also be presynthesized and then deposited onto the substratum. cDNAs are generally generated by PCR and deposited by pen or by adaptations of the ink-jet printing technology using piezoelectric and other forms of propulsion to deliver reagents from miniature nozzles to individual spots on the array. Another method is mechanical micro spotting using a delivery mechanism containing an array of tweezers, pins or capillaries where printing is done by direct surface contact. The different methods of making microarrays have been discussed extensively in literature (Blanchard, 1998; Kricka, 1998; Ramsay, 1998; Schena et al., 1998). Instead of attaching DNA directly to the glass surface, miniature gel pads have been created on glass slides (Proudnikov et al., 1998). DNA is attached at the array site by cross linking the molecule into the gel material, which helps in having more DNA available than surface sites making the array sites more like miniature test tubes. The slide or chip thus has uniquely located individual DNA sequences. The hybridization array method takes advantage of the highly selective nature of DNA double helix hybridization. To profile transcript expression, the slides are probed with labeled single-stranded species, which are generated by reverse transcription from mRNA isolated from cells or tissues in different states that one wishes to compare. In cDNA microarrays, a different fluorescent dye is used to label the cDNAs for each physiological state. The identical cDNA sequences from two different physiological states and labeled with two different dyes are thus competing with each other in hybridizing to the complementary DNA sequence immobilized on the surface. This allows direct comparison between two states on a single chip. The detection scheme is measuring relative hybridization intensity using two-color fluorescence analysis. Data from the hybridization reactions are collected by a reader or a scanner, which is basically a computer-controlled inverted laser scanning confocal fluorescent microscope with maximum sensitivity necessary to measure low abundance mRNAs. Image analysis is performed to extract fluorescence intensities or their ratios at

each cDNA location, and then correlate them to level of gene expression. Pseudo color is used to indicate intensity of the signal, which is proportional to amount of mRNA. Microarray image analysis involves array target segmentation, background intensity extraction, target detection, target intensity extraction, ratio analysis, multiple image analysis, software for database development and design. Analysis of expression information is done by pattern recognition and cluster analysis (Eisen et al., 1998; Sherlock, 2000). The focus of most current array-based studies is scanning a large array of genes to screen for mutations (Yershov et al., 1996), disease genes (DeRisi et al., 1996; Heller et al., 1997; Khan et al., 1999b) and polymorphism (Chee et al., 1996); to monitor and analyze RNA expression levels in bacteria (Oh and Liao, 2000), in plants (Schena, 1996; Schena et al., 1995), in yeast (DeRisi et al., 1997), in humans (Schena et al., 1996) and in response to biochemical stimulus (Lockhart et al., 1996), toxins (Afshari et al., 1999) and serum (Iyer et al., 1999); for physical mapping, for mapping function of genes by selective knockout and gene tagging (Shoemaker et al., 1996) and DNA resequencing (Schena et al., 1998). If detection methods are sensitive enough, quantitation of transcript levels for every gene in an organism is possible if the genome has been sequenced and the identity of the genes is known. The advantages of this method are speed of measurement, ability to quantify in parallel, miniaturization, multiplexing and ease of automating analysis, which ensures quality, reproducibility, availability and affordability. The disadvantages are that they are closed systems where absence of a gene on the array makes it impossible to assay for its differential expression and problems associated with any hybridization method.

4.1.2.2. Other gene expression analysis methods. Some of the disadvantages of hybridization arrays can be overcome by using these methods as they are sensitive and flexible methods where the identity or even the existence of a gene need not be known to detect a change in their transcript expression levels. The different methods are Serial Analysis of Gene Expression (SAGE), DD, Rep-

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resentational Difference Analysis (RDA) and Suppression Subtractive Hybridization (SSH). SAGE uses a molecular indexing scheme, in which individual transcripts are identified by short, well-defined subsequences. Indexes are counted by classical sequencing methods and are used as a measure of relative populations of RNA transcripts in the sample (Velculescu et al., 1997). It can be used when the main goal of the study is to catalog all mRNA expressed (Zhang et al., 1997; Zhou et al., 1998). DD relies on RT-PCR amplification of expressed mRNA exploiting many combinations of primers (Liang and Pardee, 1992). It can compare several samples simultaneously and detect loss as well as gain of cDNA populations on the same gel. RDA (Lawson and Berliner, 1998; Michiels et al., 1998) compares two populations of DNA by combining subtractive hybridization and a subsequent PCR-based enrichment of DNAs, followed by visual display, which represents differences between the two compared samples. Using one cDNA population in excess, it can be used with cDNA (cDNA RDA) to amplify the molecules that are different between the two samples. It cannot compare more than two samples. SSH, a new and highly effective method combines normalization and subtraction in one procedure (Diatchenko et al., 1996). During normalization, the abundance of different cDNAs is equalized, i.e. abundant transcripts are reduced and rare transcripts are increased in frequency. This is the difference between RDA and SSH. During subtraction, common sequences between target and driver population are excluded. This method is superior to DD, which can express a relatively high level of false positives (Sompayrac et al., 1995) and is biased for high copy number RNAs (Bertioli et al., 1995). The singular advantage of this strategy is that only the rare transcripts, which remained single stranded during the first round, form hybrids in the second round which allows the preferential amplification of rare and differentially expressed transcripts (Kuang et al., 1998). SSH has been used extensively to identify differential gene expression (Fang et al., 2000; Glienke et al., 2000; Hufton et al., 1999; Murphy et al., 1999; Stier et al., 2000; Ye and Connor, 2000).

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Microarrays can survey the expression profile of only the genes that have been spotted on the surface, while these other methods can be used to discover both known and novel genes. But they are more labor intensive than hybridization microarrays. The two methods have been applied together taking advantage of their respective merits to get a complete picture of differential gene expression (Yang et al., 1999).

4.2. Proteomics Proteomics encompasses analysis of gene expression at the protein level and is a powerful complementation to the nucleic-acid based technology. Protein-based gene expression analysis is done by analyzing the ‘proteome’, the entire protein population expressed from a genome in any cell or tissue at a given time. It is an entity that can change under different conditions but is the direct product of a genome. The importance of proteomics, the practical challenges and its contribution towards fundamental understanding in biology has been reviewed extensively (Anderson and Anderson, 1998; Corthals et al., 2000; Harry et al., 2000; Kellner, 2000; Lopez, 2000; Pandey and Mann, 2000). The analysis of complex patterns of protein expression has been improved since the development of two-dimensional (2-D) gel electrophoresis. There are a lot of shortcomings of the technique and different alternative methods and modifications have been suggested. However the main argument that has been put forth in favor of 2-D gel electrophoresis is that at present there is no other method powerful enough to handle the protein complement of the entire genome.

4.2.1. Two-dimensional gel electrophoresis The separation of proteins using 2-D gel electrophoresis and the various ways to detect and identify the proteins have been described in great detail (Jungblut and Thiede, 1997; Klose and Kobalz, 1995; Quadroni and James, 1999; VanBogelen and Olson, 1995). There are some inherent problems associated with using 2-D gel electrophoresis for proteomics. Hydrophobic proteins and proteins present at low copy num-

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bers may not be detected with this method (Wilkins et al., 1998). These problems can be overcome by separating the proteins into sub-fractions that can be run on separate gels under appropriate conditions to get maximum resolution and the gel images can be combined to make a composite image representing the entire proteome (Cordwell et al., 2000). Proteins are first separated in one dimension according to their charge (pI) by isoelectric focusing (IEF) and subsequently by their molecular weight using sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) in the other dimension. A prerequisite for separation and comparison of complex protein mixtures by 2-D gel electrophoresis is high resolution and reproducibility of gels. The development of immobilized pH gradients (IPGs) on strips for IEF has largely solved the issue of 2-D gel reproducibility, allowing intra- and inter-laboratory comparison of separated protein profiles (Celis et al., 1998). The separated proteins can be detected directly by methods like staining with dyes or metals. Silver staining is the most commonly used technique to detect proteins on gels. Silver staining is 100-fold more sensitive than Coomassie blue staining. Radiolabeling of proteins is the most sensitive method where the proteins are visualized by autoradiography or fluorography after drying the gel (Jungblut and Thiede, 1997). A recent review discusses the advantages of fluorescence detection techniques over conventional techniques (Patton, 2000). Protein samples can be identified by a number of techniques like N-terminal protein microsequencing by Edman degradation, peptide mapping and amino acid composition analysis, 2-D Western blotting and peptide mass fingerprinting via MALDI-TOF mass spectrometry (Dunn, 1997; Gevaert and Vandekerckhove, 2000). Mass spectrometry is the most popular technique due to its capacity for high throughput analysis of proteins (Lahm and Langen, 2000; Patterson, 2000). MALDI-TOF mass spectrometry has been used for large-scale identification of yeast proteins from 2-D gels, as the entire yeast genome sequence was available and hence database searches with peptide mass fingerprinting and sequence

tags made the process very efficient (Shevchenko et al., 1996). 2-D gel databases have been created that are available today (Celis et al., 1998; Fountoulakis et al., 1999; Geisow, 1998) and another way of analyzing the gel is to match new gels against protein patterns provided by databases (Toda et al., 1998). The complexity and quantity of data available from 2-D gel patterns can be handled by image analysis techniques using automated computer analysis systems, which can extract both qualitative and quantitative information from individual gels and provide pattern matching between gels. The presence of a range of image-acquisition and digitization devices, together with the present generation of powerful microcomputer workstations, has resulted in the development of many commercial 2-D gel analysis software that are readily accessible to the scientific community. Some examples are BioImage (Ann Arbor, MI) 2D analysis software (Arnott et al., 1998; O’Connell and Stults, 1997), PDQuest software (Protein and DNA Imageware Inc.) (Heinke et al., 1998; Toda et al., 1998). This technique has been used for comparing protein expression profiles of patients with cancer, heart and infectious diseases to unaffected people to identify disease-associated proteins (Jungblut et al., 1999). It has been used to monitor effects of xenobiotics on changes in the expression of liver proteins (Newsholme et al., 2000). In spite of the shortcomings associated with the technique, 2-D gel electrophoresis is used extensively for characterizing the protein complement of the genome, proteome, and to some degree for differential expression profiling of proteins. The advances made towards improving the robustness, reproducibility and high throughput capacity of this technique make it a powerful tool for monitoring changes in expression of proteins.

5. Impact of genomic and proteomics on cell culture processing The advances made in cell culture technology in the last two decades were greatly facilitated by genetic and physiological manipulation of the

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producing cells. These manipulations seldom affect a single metabolic pathway. Typically, an array of changes in the different levels of cellular machinery gives the desired characteristics to those cells. Although traditional analytical methods allow in depth study of a group of metabolic reactions or regulatory events, their scope is relatively ‘local’. The genomic and proteomic techniques offer a global survey of cellular changes that lead to, or correlate to, the physiological phenomenon of interest. These techniques can be used complementarily. Many methods for transcript analysis, such as SSH and RDA, are powerful tools capable of discovering differentially expressed rare genes. However, due to the probabilistic nature of gene discovery, they may not identify a large number of gene species. cDNA microarray, on the contrary, allows thousands of genes to be scanned efficiently. But, it probes only the genes which have been previously isolated. The proteomic methods extend the gene expression studies from mRNA to protein level, although they are rather labor intensive. Each technique provides a different perspective of the intricacy of the cellular machinery. The genomic techniques reviewed in this article have been applied for gene expression monitoring, investigating biological phenomena and to identify novel genes, which may play an important role in the process. A similar investigation of cells in culture can give insight into the molecular mechanism of macroscopic physiological changes. The change in gene expression of fibroblasts in culture in response to changes in serum concentration has been studied using cDNA microarrays (Iyer et al., 1999). Changes in gene expression profiles when cells undergo adaptation or other process condition or environmental perturbations can also be probed by microarray technology. cDNA and oligonucleotide arrays are available for a number of species commercially including human, mouse and rat. For cell lines of human, mouse origin or from other species for which the whole genome sequence is available or the sequencing effort is near completion, cDNA microarrays can also be constructed by individual laboratories with only a subset of genes that are of interest to each laboratory. However, many cell

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lines used for producing biologics are derived from other organisms like hamster, monkey, pig etc. for which little genomic information is available and alternative approaches are necessary. One can use the gene expression analysis methods like SSH where one does not need any prior sequence information. The differentially expressed genes can be sequenced and at least some of the isolated sequences can be identified by their similarity to annotated gene sequences of other species. One can also use microarrays with sequences of other organisms that are phylogenetically close e.g. human arrays for monkey cells (Gagneux and Varki, 2001), rodent arrays for hamster cells. Proteomic tools have been used extensively for product characterization in the cell culture industry. SDS-PAGE is used to assess purity and integrity of the product isolated from culture supernatant (Ackermann et al., 1995). HPLC, MALDI mass spectrometry and other methods have been used to ascertain the identity of the protein product, to study its structure and its glycosylation pattern (Gawlitzek et al., 1995; Hooker et al., 1995; Price et al., 1995). Proteomics has also been used to assess the consequences of cell culture conditions on protein expression. These studies are at a larger scale where 2-D gel electrophoresis is used to separate whole cell protein content to identify changes in protein expression. This is followed by similar techniques to identify the proteins. 2-D gel electrophoresis of the intracellular proteins of E2F-1 clones of CHO cells showed an increase in 236 proteins as compared with CHOK1 control cells (Lee et al., 1996b). CHO cells were stimulated to grow by fetal calf serum, insulin, or basic fibroblast growth factor and the resulting protein expression patterns were analyzed by 2-D gel electrophoresis (Lee et al., 1996a). Twenty four gene products were identified as being differentially expressed. These proteins may play an important role in growth factor signaling. The same technique was also used to examine the heat shock response of a mouse hybridoma cell line grown in a bioreactor (Passini and Goochee, 1989). As an illustration, we describe the use of genomic and proteomic techniques to better understand the molecular mechanism of metabolic shift.

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These techniques provided great insight that would have been extremely difficult to gain through conventional metabolic studies. Cultured mammalian cells typically convert more than half of the total carbon they consume into lactate, and almost one third of the nitrogen they uptake through amino acids into ammonia (Zeng et al., 1998a,b). It is possible to alter the metabolism to reduce nutrient consumption, and thereby decrease lactate and ammonia production by controlling residual nutrient concentrations (Zhou et al., 1997). Moreover, the shifted metabolic state (low DL/DG state) was sustained once the culture was switched to a continuous mode of operation. Cells that did not undergo a metabolic shift remained in the high DL/DG state and continued to produce lactate even after switching to a continuous mode (Europa et al., 2000). Various specific rates, including glucose, amino acids and oxygen consumption, and lactate, ammonia and IgG production, were obtained and the metabolic flux analysis was performed to compare cell metabolism at the two different metabolic states (Europa et al., 2000). When cells switched from a high lactate production state to a low lactate production state, the specific consumption rates of all nutrients were reduced, along with the specific production rates of lactate and ammonia, whereas the antibody productivity, on a per cell basis, was not affected. To better understand the changes in cellular machinery that play a key role in the metabolic shift, the activities of some selected enzymes were measured (unpublished data). The results show that hexokinase and glutaminase levels were unaffected when switched from a high to a low DL/DG state, whereas the levels of glucose 6-phosphate dehydrogenase, phosphofructokinase, and malic enzyme were significantly reduced at the low DL/DG state. Obviously only a limited number of enzymes could be assayed, as each has a distinct assay protocol which usually calls for a rather large amount of cellular material for the measurement. Genomic and proteomic tools, in contrast to enzymatic assays, allow for a more global survey. Cells from two different metabolic states were harvested, lysed, and used for the isolation of

mRNA and total cellular protein. mRNA was used for SSH and gene expression profiling using microarrays with DNA sequences from mouse as probes. SSH was performed using each sample as tester and driver, respectively. The resulting EST clones in the two libraries thus represent genes that are up-regulated or down-regulated when cells undergo metabolic shift from a high DL/DG state to a low DL/DG state. Among the genes identified are those involved in glycolysis like lactate dehydrogenase, phosphoglyceromutase and pyruvate kinase. All of them were expressed at a high level in the high DL/DG state. In addition to genes involved in central metabolism, some novel genes were also differentially expressed (unpublished data). Two types of microarrays were used; the first was a glass slide spotted with cDNA for annotated sequences representing mouse genes and the second was the Affymetrix mouse array with oligonucleotides as probes. The standard protocol for preparing fluorophore labeled single strands was applied to mRNA samples from low and high DL/DG states. Majority of genes involved in central energy metabolism were shown to be down-regulated like pyruvate kinase, phosphofructokinase, lactate dehydrogenase or to remain relatively constant for the low DL/DG state as compared with the high DL/DG state like glutamate dehydrogenase (unpublished data). Protein was subjected to 2-D gel electrophoresis to identify differentially expressed species (Seow et al., 2001). Eight differentially expressed spots were isolated from a gel with an expanded gradient of pH 4 –7. MALDI-TOF mass spectrometry of their tryptic digests was performed. The differentially expressed spots were identified on the basis of peptide mass fingerprints. Phosphoglyceromutase, a glycolytic enzyme, and cytoplasmic actin, are both up-regulated at the high DL/DG state, while a subunit of NADH–ubiquinone oxidoreductase involved in oxidative phosphorylation and ubiquitin carboxy-terminal hydroxylase L1 are up-regulated at the low DL/DG state. Some of the genes were identified by more than one technique like phosphoglyceromutase. Other genes like the central metabolism genes were expected to be involved in metabolic shift. The

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novel genes may be playing an important role in regulation and their function needs to be ascertained to identify their contribution to metabolic shift. However, genes like ubiquitin carboxy-terminal hydroxylase, which is involved in protein turnover, were not expected. Identification of such pathways that do not seem to be involved in the phenomena under investigation is the strength of genomic and proteomic techniques. A metabolic shift is the manifestation of changes in environmental factors (especially nutrient concentrations), activities of enzymes/ transporters, and translation and transcription of numerous genes involved. Amongst the cellular machinery changed in metabolic shift, there are enzymes directly involved in central metabolism, others may be function or structure related changes that must occur in order to accommodate the metabolic shift, and yet others may be truly ‘collateral’, which are affected by, but not essential to, metabolic shift. One cannot possibly exhaustively investigate every detail in the multi-layered regulatory network that may be involved in metabolic shift. In an experimental program, it is more likely that specific rate calculation, flux analysis, enzyme activity measurement, protein expression, differential gene expression profiling, gene expression probing by cDNA microarray, each gives only a partial glimpse of the operation of the global reaction network. But, combining all, a more comprehensive view eventually emerges. One can justifiably criticize that a vast majority of the genomic and proteomic investigation in cell culture processing would fall in the category of a survey or a discovery. One can be overwhelmed by the amount of data generated by these techniques. A prospecting characteristic will exist for a number of years to this type of work. However, the new insight in cellular physiology gained from these studies will definitely lead to a new understanding of the regulation of cellular events of bioprocess importance and unleash a new wave of innovation to create new host strains, new processes and new strategies of product development.

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