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    • 专利摘要:
    Methods of identifying culture conditions for a cell or organism, the method comprising: (a) obtaining a genome-wide stoichiometric metabolic model of the cell or organism, and (b) performing a constraint-based optimization of the metabolic model of the cell or organism using at least one yield-based constraint.
    公开号:BE1022340B1
    申请号:E2014/5118
    申请日:2014-12-15
    公开日:2016-03-25
    发明作者:Philippe Marc Helene Dehottay；Philippe Goffin；Bas Teusink；Dos Santos Filipe Branco
    申请人:Glaxosmithkline Biologicals S.A.；
    IPC主号:
    专利说明:

    PRODUCTION PROCESS
    Context
    The present invention relates to production methods for cells and materials obtained therefrom.
    Metabolic models for various organisms are known and can be used to develop commercial production processes. The present invention addresses the problem of improvements to existing metabolic models. Summary of the invention
    One aspect of the present invention relates to a method of identifying culture conditions for a cell or organism, the method comprising: (a) obtaining a stoichiometric metabolic model at the genome level of the cell or the organism, and (b) performing constraint-based optimization of the metabolic model of the cell or organism using at least one yield-based constraint, where yield-based constraint favors a condition identified to promote a kinetic parameter and / or to suppress a condition that has been identified as inhibiting a kinetic parameter so as to improve the culture conditions for both yield and kinetic parameter.
    Another aspect of the present invention is a method of developing a cell or an organism, the method comprising developing the cell or organism in said culture conditions identified by a method of identifying the conditions of culture as described herein, optionally the measurement of kinetic parameters, and the application of said kinetic parameters thus obtained to further optimize the culture conditions.
    Another aspect of the present invention is a system comprising a processor and a memory arranged to store computer executable instructions, which when executed causes the processor to perform a constraint-based optimization of a metabolic model of a cell or organism using at least one yield-based constraint, wherein the yield-based constraint promotes a condition identified as promoting a kinetic parameter and / or suppressing a condition that has been identified as inhibiting a kinetic parameter, in order to improve the culture conditions for both the yield and the kinetic parameter.
    Brief description of the drawings
    Figure 1 schematically illustrates the components of an exemplary computer-based device that can be used in the practice of the present invention.
    detailed description
    In a first aspect of the present invention, constraint-based optimization (specifically flow-based optimization) was performed on a metabolic model of a cell using at least one yield-based constraint, where the stress-based Efficiency enhances a condition identified as promoting a kinetic parameter and / or suppressing a condition that has been identified as inhibiting this kinetic parameter so as to improve culture conditions for both yield and kinetic parameter.
    An example of a kinetic parameter is the growth rate. Accordingly, the invention also relates to a method of identifying culture conditions for a cell or organism, the method comprising: obtaining a stoichiometric metabolic model at the genome level of the cell or organism; and performing constrained optimization of the metabolic model of the cell or organism using at least one yield-based constraint, where the yield-based constraint promotes a condition identified as promoting the rate of growing and / or suppressing a condition that has been identified as inhibiting the growth rate so as to improve the culture conditions for both yield and growth rate.
    Flux-based optimization is generally applied to optimize yield without considering kinetic parameters such as growth rate, because there is no kinetic information in the stoichiometric metabolic model.
    Specific compounds or parameters have been identified that either promote or inhibit the growth rate of certain bacterial species that can be used to refine the solutions (or set of solutions) for optimizing yield in a set of constraint data. Thus, compared with known performance-based modeling methods, the present invention allows for further reduction of a range of possible solutions for flux distributions with optimized kinetic parameters.
    In one aspect, the kinetic parameter is a rate, for example, a growth rate, a rate of production or consumption of metabolites, a level of gene expression, a rate of production of recombinant proteins or a level of secretion of proteins. recombinant. To avoid any ambiguity, the term "kinetic parameter" is used here in its broadest sense as any property that is non-stoichiometric.
    The growth rate can be determined, for example, by measuring the optical density. Protein production can be determined, for example, by an ELISA technique or any well-known technique. Gene expression can be controlled, for example, by RNA expression. High throughput methods such as those described in Example 3 can be used. All rate measurements are measured as a function of time.
    In Example 4, the data are presented in relation to the improvement in both yield and growth rate according to the present invention.
    In Example 5 which relates to PT production, the solution space is constrained so that the negative regulatory effects of the transcription are minimized, and therefore the flow balance analysis (FBA) can only identify solutions that result in the desired biomass yield (this is constrained to the desired value) while minimizing the negative regulatory effects on genes for PT expression. This example illustrates a general method for optimizing properties that are not explicitly considered in a model, converting these properties into constraints so that the solution space is restricted to solutions that satisfy these constraints, and therefore also satisfy these additional non-explicit "goals" (eg, rate, protein, toxin production, etc.). The invention uses a yield-based constraint identified as favoring a kinetic parameter. Such constraints can be identified by high-throughput or low-throughput culture tests, reading articles, performing a (comparative) transcriptomic / proteomic / metabolomic analysis, retrieving information from databases (public).
    More generally, the present invention relates to a method for optimizing kinetic properties with purely stoichiometric models, by incorporating knowledge about such properties in the form of constraints.
    The yield to which reference is made in the present invention may be the yield of a cell or the yield of a cellular component such as a particular protein or peptide or carbohydrate or nucleic acid.
    Any suitable method can be used to measure the yield. For example, for biomass yield, suitable methods include optical density, wet cell weight, dry cell weight, colony forming units, or particle counts (with FACS technique or microscopy, with different dyes , etc.). For the performance of a particular protein, suitable methods may include ELISA, SDS-PAGE densitometry (e.g., with different stains), methods based on MS, HPLC or CLUP (with various methods of detection).
    In one aspect, the claimed invention for identifying culture conditions for a cell or organism can be implemented on a computer. As such, the invention relates to a system comprising:
    A processor; and
    A memory adapted to store computer executable instructions, which when executed causes the processor to perform constraint-based optimization of a metabolic model of a cell or organism using at least one constraint based on the yield, where the yield-based constraint favors a condition identified as promoting a kinetic parameter and / or suppressing a condition that has been identified as inhibiting a kinetic parameter, so as to improve the culture conditions for both performance and the kinetic parameter.
    Figure 1 illustrates various components of an exemplary computer-based device that can be implemented in any form of computer and / or electronic device, and wherein embodiments of the invention can be implemented. artwork.
    The computer-based device includes one or more processors that may be microprocessors, controllers, or any other suitable type of processors for processing computer executable instructions to control the operation of the device to perform constraint-based optimization. of a metabolic model of a cell or organism using at least one yield-based constraint, where the yield-based constraint favors a condition identified as promoting a kinetic parameter and / or suppressing a condition that has has been identified as inhibiting a kinetic parameter, so as to improve the culture conditions for both the yield and the kinetic parameter.
    In some examples, for example, when a system or chip architecture is used, the processors may include one or more fixed function blocks (also referred to as accelerators) that implement a part of the method of making the chip. optimization based on constraints in hardware (rather than software or firmware). The platform software comprising an operating system or other suitable platform software can be provided to the computer-based device to enable the application software to be run on the device.
    The computer executable instructions may be provided using any computer readable medium that is accessible by the computer-based device. Computer readable media may include, for example, computer storage media such as memory and communications media. Computer storage media, such as memory, include volatile and nonvolatile, extractable, and non-extractable media implemented in any method or technology for storing information such as computer readable instructions, data structures, data structures, program modules or other data.
    Computer storage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other memory technology, CD-ROM, DVD (Digital Versatile Disks), or other optical storage , magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that may be used to store information for access by a computing device. In contrast, the communication media may comprise computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave, or other transport mechanism. As defined herein, a computer storage medium does not include the communication media. Although the computer storage medium is presented within the computer-based device, it will be understood that the storage may be distributed or located remotely and accessed over a network or other communication link. .
    The computer-based device also suitably includes an I / O controller arranged to display the output information on a display device that can be separated from or integrated with the computer-based device. The information display can provide a graphical user interface. The I / O controller is also designed to receive and process inputs from one or more devices, such as a user input device (for example, a mouse or keyboard). This user input can be used to enter information for performing stress-based optimization of a metabolic model of a cell or organism using at least one performance-based constraint.
    In one embodiment, the display device may also act as a user input device if it is a touch display device. The I / O controller may also output data to devices other than the display device, for example, a locally connected print device (not shown in FIG. 1).
    The term "computer" is used herein to refer to any device having processing capability so that it can execute instructions. Those skilled in the art will understand that such processing capabilities are incorporated into many different devices and therefore, the term "computer" includes PCs, servers, mobile phones, PDAs (Personal Digital Assistants) and many others. devices.
    The method of the invention uses an optimization based on constraints of a metabolic model.
    Constraint-based modeling procedures do not seek to find a single solution but rather to find a collection of all the permissible solutions for the main equations that can be defined. Solutions that violate any of the imposed constraints are excluded from the collection, which from a mathematical point of view is called a solution space. Subsequent application of additional constraints further reduces the space of the solutions and, therefore, reduces the number of permissible solutions that a cell can utilize. Constraints that have been used in the first generation of stress-based models include stoichiometric constraints (mass balance), thermodynamic constraints (concerning the reversibility of a reaction) and enzymatic capacity constraints.
    Metabolic models are described in "Orth JD, Fleming RMT, Palsson B0 2010." Reconstruction and Use of Microbial Metabolic Networks: The Core Escherichia coli Metabolic Model as an Educational Guide In Curtiss R, III, Kaper JB, Squires CL, Karp PD, Neidhardt FC, Slauch JM (ed.), EcoSal Such models are also described in Santos et al., "Methods in Enzymology, Volume 500 # 2011 Elsevier Inc ISSN 0076-6879, DOI: 10.1016 / B978-0- 12-385118-5.00024-4 "Constraint-based optimization can be flow-based analysis (FBA) Flow-based analysis is typically designed for yield optimization in a stoichiometric network without no information on kinetics, rate prediction is impossible using flow-based analysis After applying constraints (which reduce the solution space), an optimization method (such as FBA) is applied to search for a single solution (FBA) or a set of solutions (typically, using flow variability analysis (FVA)) within the solution space, which maximizes or minimizes an objective function (which may be a single flow, for example, the production of biomass), or a combination of several streams). Other constraint-based optimization methods that may be considered in the present invention are discussed in Lewis et al. Nat. Microbiol. Rev. 10: 291 (2012), incorporated herein by reference (see, for example, Figure 2 of Lewis et al.).
    In the flow-based analysis, the constraints are applied to the different reactions of the model, ie the minimum and maximum flux possible through each reaction. The reactions in the model can be divided into two main categories and therefore the type of constraints that will be applied will differ, as described below.
    Internal reactions, ie all the reactions that are inside the system (note that this also includes the transport reactions from the outside to the inside of the cell (and in the other sense), and extracellular reactions). These reactions are the (bio) chemical reactions that connect the metabolites together.
    In one aspect, no constraint is attached to these internal reactions. In one aspect, some reactions can be defined as irreversible (mainly from a thermodynamic point of view), which is reflected by a lower limit of 0 and an upper limit of infinity (for example, practically this will be +99999). In one aspect, for example for reversible reactions, the lower limit will be set infinitely low, for example -99999 and the infinitely high upper limit, for example at +99999.
    In one aspect, if the objective is to stimulate the effects of the absence of a reaction (knockout gene for example), the lower and upper limits are both set to 0.
    Exchange reactions, that is, a relatively small number of reactions (about 202 in the pertussis model) that determine which compounds are allowed to enter and exit the system. In other words, which compounds can be produced and consumed.
    Preferably, the stress-based modeling is applied to such exchange and / or internal reactions.
    By convention, absorption (consumption) is defined as a negative flow, while excretion (production) is defined as a positive flow.
    In an aspect where a specific compound is to be consumed, i.e., it is not desired in the composition of the medium but can be produced, the lower limit is set to 0 and the upper limit is +99999 . The upper limit can also be set to any specific number between 0 and +99999, reflecting the maximum allowable production flow for that compound. In other words, where it does not matter if a compound is produced, the upper limits can be set to be infinitely high.
    In one aspect, a specific compound is consumed and not produced, the limits both lower and higher can be set to 0.
    In an aspect where a specific compound is to be consumed (i.e. considered to be part of the composition of the medium) but not to be produced, the lower limit is set at the maximum concentration permitted in the composition of the medium (eg if there is a desired maximum of 125 mM of L-glutamate in the medium, the lower limit will be set at -125), and the upper limit is set to 0 (no production allowed ). Such a constraint will not force the model to identify solutions when the compound is actually consumed. To achieve this, the upper limit can be as well set at -125 (for the example of L-glutamate).
    If a specific compound is to be consumed, and also produced, the lower limit may be set to -99999 and the upper limit to +99999 (or any specific number, if a maximum rate of consumption / production is to be tolerated).
    In general, in summary, to force the model to identify solutions where a specific compound is consumed (for example, because it has a positive effect on the growth rate), the upper limit for the corresponding flux can be fixed at any number less than 0 and greater than or equal to the lower limit.
    On the contrary, to exclude a specific compound from the composition of the medium (for example, because it has a negative effect on the growth rate), the lower limit can be set to any number greater than or equal to 0.
    As an example: for B. pertussis, and compared to a minimally identified medium, L-arginine has a positive effect on the growth rate. It is desired that the model identify solutions where at least 2mM of L-Arg is consumed, and only a maximum of 100mM of L-Arg in the medium can be provided. In this situation, the lower limit is set to -100, the upper limit is -2. The application of constraint-based optimization in a given set of constraints results in a solution (if feasible) where the L-Arg absorption flux is between 2 and 100 mM.
    As another example: for B. pertussis and compared to a minimally identified medium, L-isoleucine has a negative effect on the growth rate. It is desired that no L-Ile is consumed, and that it is not produced. The lower limit is set to 0, the upper limit is set to 0. The model will then propose (if feasible) solutions where L-Ile is neither absorbed nor produced.
    A condition identified as promoting a kinetic parameter (e.g., growth rate) or a condition that inhibits a kinetic parameter (e.g., growth rate) may be the presence or absence of a compound or a set of compounds.
    The compounds may be, for example, amino acids, vitamins, minerals, metals or any conventional growth medium component.
    The compounds can be any compound or combination of compounds as described herein, such as (but not limited to) adenine, biotin, pantothenate, calcium, choline, citrate, cobalt, folates , glycine, glycerol, guanine, borate, hemin, inositol, alanine, arginine, asparagine, aspartate, cysteine, glutamine, histidine, isoleucine , leucine, lysine, methionine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine, valine, magnesium, manganese, acetate, beta-hydroxybutyrate, formate, lactate, pyruvate, 2-ketoglutarate, fumarate, phosphate, NAD, bicarbonate, ammonia, para-aminobenzoic acid, potassium iodide, pyridoxine, riboflavin, succinic acid, sucrose, thiamine, uracil, zinc, chloride, sulphate, sodium. In one aspect, such compounds are for use in Pertussis models and growth media.
    In another aspect, a condition promoting a kinetic parameter or inhibiting a kinetic parameter may be a gas supply, such as an oxygen or carbon dioxide supply, or a proton / acid availability.
    In one aspect, the condition is the inclusion of a known compound or set of compounds to promote a kinetic parameter, possibly having a higher maximum concentration.
    In one aspect, the growth condition promotes the kinetic parameter without any adverse effect on yield. In one aspect, the yield is not affected by more than 5% or 10% compared to growth or prediction without the growth condition.
    In one aspect, the growth condition promotes both the kinetic parameter and the yield.
    In one aspect, the reference to a condition that promotes a kinetic parameter is a condition measured against a kinetic parameter in the same medium but without the condition. For example, the addition of a specific compound such as glycine to a medium can demonstrate faster growth than the same medium without glycine.
    In one aspect, the kinetic parameter is considered relative to a chemically defined medium that may be a minimal medium. A minimal medium may be any medium that contains only a minimal set of components necessary for cell growth, but it may also refer to a medium in which the addition of an additional component can enhance growth and which, therefore, is not an optimal growth medium. A minimal medium can also be considered as an environment that supports suboptimal growth.
    In one aspect, a condition that promotes a kinetic parameter or that suppresses a condition that has been identified as inhibiting a kinetic parameter such as growth rate, has been identified by a comparison of the kinetic parameter in the medium as a minimal medium with and without the condition.
    In one aspect, the cell or organism is a single-cell organism such as yeast or bacteria, and in one aspect it is a bacterium, such as B. pertussis. In one embodiment, the Bordetella species is selected from the group consisting of Bordetella pertussis, Bordetella parapertussis, or Bordetella bronchisepta. In one embodiment, the Bordetella species is Bordetella pertussis. In one embodiment, the Bordetella species expresses at least one virulence factor selected from the group consisting of pertussis toxin (PT), filamentous haemagglutinin (FHA), and pertactin (PRN). In one embodiment, the Bordetella species expresses the PT, in one embodiment, the Bordetella species expresses the FHA, in one embodiment, the Bordetella species expresses the PRN, in one embodiment, the species Bordetella expresses PT and FHA, in one embodiment, the Bordetella species expresses PT and PRN, in one embodiment, the Bordetella species expresses PRN and FHA, in one embodiment, Bordetella species expresses PT, PRN and FHA. PT, FHA and PRN are well known in the art. The pertussis toxin may refer to a genetically modified toxin or pertussis toxoid. Other suitable cells or organisms include prokaryotic or eukaryotic cells such as Escherichia coli, Staphylococcus aureus, Streptococcus pneumoniae, Haemophilus influenzae, Clostridium difficile and Neisseria meningitidis, Saccharomyces cerevisiae, Pichia pastoris, Hansenula polymorpha, Pseudomonas fluorescens, Bacillus subtilis, and lines eukaryotic cells such as CHO, VERO, MRC5, HEK293, EB66, or insect cells.
    In one aspect, for B. pertussis, the growth condition is the presence of a compound below, at a concentration of 0.01 to 100 mM, such as 0.01 to 90 mM, 0.01 to 80 mM, 0.01 to 70 mM, 0.01 to 60 mM, 0.01 to 50 mM, 0.01 to 40 mM, 0.01 to 30 mM, 0.01 to 20 mM, 0.01 to 10 mM, as 9, 8, 7, 6, 5, 4, 3, 2 or 1 mM. Suitable compounds include CaCl2.2H2O (optionally at 0.3 mM), glycine (optionally at 2 mM), hemin (optionally at 50 mg / l), L-alanine (optionally at 2 mM), L-histidine (optionally at 2 mM), L-proline (optionally at 2 mM), sodium L-lactate (optionally at 5 mM), NaHCCg (optionally at 2 mM), para-aminobenzoic acid ( optionally at 0.2 mg / l), and riboflavin (optionally at 0.01 to 100 mg / l, such as 90, 80, 70, 60, 50, 40, 30, 20, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1 or 0.5 or 0.4 mg / l as 0.3 mg / l). Such conditions may be appropriate to promote growth rate without affecting yield.
    In one aspect, for B. pertussis, the growth condition can promote both growth rate and yield. Optionally, the growth condition may be the presence of a compound chosen from biotin (optionally at 0.02 mg / l), glycine (optionally at 20 mM), L-alanine (optionally at 20 mM), L-arginine (optionally at 2 and / or 20 mM), L-cysteine (optionally at 2 and / or 20 mM), L-methionine (optionally at 2 and / or 20 mM), L-proline (optionally at 20 mM), MgCl 2 · 6H 2 O (optionally at 10 mM), NaHCCy (optionally at 20 mM), and thiamine (optionally at 0.01 to 100 mg / l, such as 90, 80, 70, 60, 50). , 40, 30, 20 mg / l as 10 mg / l).
    The present invention also includes both a method of identifying culture conditions for the growth of a cell or organism, and a method using these conditions including growth of the cell or organism under conditions culture identified by the method of identification of culture conditions, and optionally further recovery of cells of the body or a product derived therefrom.
    The growth may be an industrial scale fermentation process using a medium as described herein, wherein the method comprises providing an inoculum of a cell or organism and incubating the inoculum a chemically defined medium, where the fermentation is allowed to proceed for a time sufficient for the bacteria to reproduce.
    The present invention also includes a method as described herein further comprising growing the organism in said identified culture conditions and measuring the growth parameters, and using the measured growth parameters to allow optimization of the growth parameters. a model used to predict crop conditions.
    The present invention also relates to the use of one or more of the following compounds in the preparation of a chemically defined medium for the growth of B. pertussis, optionally in the complementation of a minimal medium for B. pertussis, optionally the middle of example 1:
    CaCl2.2H2O (optionally at 0.3 mM), glycine (optionally at 2 mM), hemin (optionally at 50 mg / l), L-alanine (optionally at 2 mM), L-histidine ( optionally at 2 mM), L-proline (optionally at 2 mM), sodium L-lactate (optionally at 5 mM), NaHCCg (optionally at 2 mM), para-aminobenzoic acid (optionally at 0, 2 mg / l), and riboflavin (optionally at 0.01 to 100 mg / l, such as 90, 80, 70, 60, 50, 40, 30, 20, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1 or 0.5, 0.4 mg / l as 0.3 mg / l); biotin (optionally at 0.02 mg / l), glycine (optionally at 20 mM), L-alanine (optionally at 20 mM), L-arginine (optionally at 2 and / or 20 mM), L cysteine (optionally at 2 and / or 20 mM), L-methionine (optionally at 2 and / or 20 mM), L-proline (optionally at 20 mM), MgCl 2 · 6H 2 O (optionally at 10 mM), NaHCO 3 (optionally 20 mM), and thiamine (optionally to optionally 0.01 to 100 mg / l, such as 90, 80, 70, 60, 50, 40, 30, 20 mg / l as 10 mg / l. ).
    Optionally, the method of the invention is designed to identify the culture conditions for a cell or an organism (in one embodiment, it is B. pertussis) in which a condition favoring a kinetic parameter such as growing and / or suppressing a condition that has been identified as inhibiting a kinetic parameter as the growth rate is established compared to a chemically defined medium (CDM).
    In one embodiment, the method further comprises purifying a virulence factor, for example from B. pertussis to produce a purified virulence factor. The purified virulence factor may be purified pertussis toxin (PT), purified filamentous haemagglutinin (FHA), purified pertactin (PRN), purified agglutinogen 2, or purified agglutinogen 3. The purified virulence factor can be modified after purification, for example the pertussis toxin can be chemically detoxified after purification. See also EP 427462 and WO 91/12020 for the preparation of pertussis antigens. In one embodiment, the purification involves the purification of the cells using chromatography. In one embodiment, the chromatographic technique is affinity chromatography, gel filtration, high pressure liquid chromatography (HPLC) or ion exchange chromatography. Optionally, the affinity chromatography uses a purification column with an affinity tag, an antibody purification column, a lectin affinity column, a prostaglandin purification column or a column with streptavidin. Optionally, HPLC uses an ion exchange column, a reverse phase column or a size exclusion column. Optionally, the ion exchange column is an anion exchange column or a cation exchange column.
    In one embodiment, the method further comprises a step of formulating an immunogenic composition comprising a component produced using the method of the invention (such as a purified virulence factor).
    In one embodiment, the method further comprises another step of adding at least one antigen to the immunogenic composition. In one embodiment, said antigen is selected from the group consisting of pertussis toxin, filamentous haemagglutinin, pertactin, fimbrial agglutinogen, diphtheria toxoid, tetanus toxoid, at least one N. meningitidis saccharide antigen. , hepatitis B surface antigen, inactivated poliovirus (IPV) and a Haemophilus influenzae b saccharide antigen (possibly conjugated to tetanus toxoid). Said meningitidis saccharide antigen may be MenC, MenY, MenA and MenW (eg A + C, A + Y, A + W, C + Y, C + W, Y + W, A + C + Y, A + C + W, A + Y + W, C + Y + W, A + C + Y + W); possibly MenC and / or MenY is included, eventually all four are included.
    Alternatively or in addition to the aforementioned meningococcal antigens, the immunogenic composition may comprise one or more oligosaccharide or pneumococcal capsular polysaccharide-carrier protein conjugates (see above for carrier proteins comprising helper T epitopes, such as CRM197, toxoid diphtheria, tetanus toxoid or protein D). Generally, the pneumococcal oligosaccharides or capsular polysaccharides represented in the compositions of the invention include antigens derived from at least four serotypes of pneumococci, such as serotypes 6B, 14, 19F and 23F. Alternatively, at least 7 serotypes are included in the composition, for example those derived from serotypes 4, 6B, 9V, 14, 18C, 19F, and 23F. As a variant, at least 11 serotypes are included in the composition (11-valents), for example those derived from serotypes 1, 3, 4, 5, 6B, 7F, 9V, 14, 18C, 19F and 23F. In one embodiment of the invention, at least 13 of these conjugated pneumococcal antigens are included, although other antigens, for example 23-valents (such as serotypes 1, 2, 3, 4, 5, 6B, 7F , 8, 9N, 9V, 10A, 11A, 12F, 14, 15B, 17F, 18C, 19A, 19F, 20, 22F, 23F and 33F) are also contemplated by the invention.
    In one embodiment, the immunogenic composition comprises a pharmaceutically acceptable excipient.
    In one embodiment, the immunogenic composition comprises an adjuvant such as aluminum phosphate or aluminum hydroxide. Methods for adsorbing DTPa and DTPw antigens on aluminum adjuvants are known in the art. See, for example, WO 93/24148 and WO 97/00697. Usually, the adsorbed components on the adjuvant are left for a period of at least 10 minutes at room temperature at a suitable pH to adsorb most or all of the antigen prior to mixing the antigens together in the combination of the compositions. immunogens of the present invention. Other components may be non-adsorbed (such as IPV) or adsorbed specifically to other adjuvants, for example hepatitis B surface antigen (HepBsa) may be adsorbed onto aluminum phosphate (as it may be). is described in WO 93/24148) before mixing with the other components.
    In another embodiment, there is provided a virulence factor obtainable by the method. In another embodiment, a virulence factor obtained by the method is provided.
    In another embodiment, there is provided an immunogenic composition comprising the virulence factor and a pharmaceutically acceptable excipient. In one embodiment, the immunogenic composition comprises at least one other additional antigen. In one embodiment, said antigen is selected from the group consisting of pertussis toxoid, filamentous haemagglutinin, pertactin, fimbrial agglutinogen, diphtheria toxoid, tetanus toxoid, at least one N. meningitidis saccharide antigen. , hepatitis B surface antigen, inactivated poliovirus (IPV) and a Haemophilus influenzae b saccharide antigen (possibly conjugated to tetanus toxoid). Said meningitidis saccharide antigen may be MenC, MenY, MenA and MenW (eg A + C, A + Y, A + W, C + Y, C + W, Y + W, A + C + Y, A + C + W, A + Y + W, C + Y + W, A + C + Y + W); possibly MenC and / or MenY is included, eventually all four are included. In one embodiment, the vaccine comprises diphtheria toxoid, tetanus toxoid, and at least one of PT, FHA and PRN (a DTPa vaccine).
    In one embodiment, the immunogenic composition comprises aluminum phosphate or aluminum hydroxide. Methods of absorbing DTPa antigens on aluminum adjuvants are known in the art. See, for example, WO 93/24148 and WO 97/00697. Usually, the adsorbed components on the adjuvant are left for a period of at least 10 minutes at room temperature at a suitable pH to adsorb most or all of the antigen prior to mixing the antigens together in the combination of the compositions. immunogens of the present invention. Other components may be non-adsorbed (such as IPV) or adsorbed specifically to other adjuvants. For example, hepatitis B surface antigen (HepBsa) may be adsorbed onto aluminum phosphate (as described in WO 93/24148) prior to mixing with the other components.
    In one embodiment, there is provided a vaccine comprising the immunogenic composition.
    The vaccine preparation is generally described in Vaccine Design - The Subunit and Adjuvant Approach Ed Powell and Newman; Pellum Press. Advantageously, the combination vaccine according to the invention is a pediatric vaccine.
    The protein antigen content in the vaccine will generally be in the range of 1 to 100 μρ or 5 to 50 μg, most typically in the range of 5 to 25 μρ.
    A sufficient amount of antigen for a particular vaccine can be determined by conventional studies involving observation of antibody titers and other responses in subjects. After initial vaccination, subjects may receive one or two booster shots at intervals of approximately 4 weeks or longer.
    The vaccine preparations of the present invention may be used to protect or treat mammalian subjects (including humans) susceptible to infection by administering said vaccine systemically or mucosally. Such administrations may include intramuscular, intraperitoneal, intradermal or subcutaneous injection; or via a mucosal administration to the oral / food, respiratory, urogenital systems.
    In another aspect, the immunogenic composition or vaccine is provided for use in the prevention or treatment of a disease.
    In another aspect, the immunogenic composition or vaccine is provided for use in the prevention or treatment of Bordetella pertussis disease.
    In another aspect, there is provided a use of the immunogenic composition or vaccine in the prevention or treatment of a disease.
    In another aspect, there is provided a use of the immunogenic composition or vaccine in the preparation of a medicament for the treatment or prevention of bacterial disease.
    In another aspect, there is provided a method of preventing or treating a disease comprising administering the immunogenic composition or vaccine as described herein to a subject.
    In one embodiment, the disease is a disease caused by Bordetella pertussis.
    In one embodiment, the disease is a disease caused by Bordetella pertussis, and the subject is a human being.
    Unless otherwise indicated, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. Definitions of common terms in molecular biology can be found in Benjamin Lewin, Gen. V, published by Oxford University Press, 1994 (ISBN 0-19-854287-9); Kendrew et al. (eds.), The Encyclopedia of Molecular Biology, published by Blackwell Science Ltd., 1994 (ISBN 0-632-02182-9); and Robert A. Meyers (ed.), Molecular Biology and Biotechnology: A Comprehensive Desk Reference, published by VCH Publishers, Inc., 1995 (ISBN 1-56081-569-8).
    The conventional three-letter abbreviations are used herein in the amino acid references, for example Glu for glutamic acid, Cys for cysteine, Ser for serine, Met for methionine, etc. Amino acids are the optical isomer in the L form, unless otherwise stated that they are in the D form. Conventional abbreviations are used to refer to chemical compounds, for example, Na + for sodium ion, H2PO4 "for dihydrogen phosphate ion; Ca2 + for the calcium ion, Fe2 + for the iron ion, K + for the potassium ion, and Mg2 + for the magnesium ion. Such ions may be provided by inorganic salts.
    The terms singular "one", "one", "the" and "the" include plural articles unless the context clearly indicates otherwise. Similarly, the term "or" is meant to include "and" unless the context clearly indicates otherwise. The term "plurality" refers to two or more. It is to be further understood that all base sizes or amino acid sizes, and all molecular weight or molecular weight values, given for the nucleic acids or polypeptides are approximate, and are provided for the description. In addition, the numerical limitations given with respect to the concentrations or levels of a substance, such as an antigen, are supposed to be approximate. Thus, when a concentration is indicated as being at least 200 μg, for example, the concentration is expected to be at least approximately 200 μg.
    Although methods and materials similar or equivalent to those described herein may be used in the practice or analysis of this disclosure, suitable methods and materials are described below. The term "includes" means "includes". Thus, unless the context requires otherwise, the term "includes" and variations such as "include" and "including" will be understood to imply the inclusion of a specified compound or composition (eg, an acid). nucleic, a polypeptide, an antigen) or a step, or a group of compounds or steps, but not the exclusion of any other compound, composition, step or their groups. The abbreviation "e.g." is derived from the Latin exempli gratia, and is used here to indicate a non-limiting example. Thus, the "e.g." appreciation is synonymous with the term "for example".
    Examples
    Example 1 - Construction of a Genome-wide Stoichiometric Metabolic Model for Bordetella pertussis Tohama I
    The metabolic network of B. pertussis Tohama I has been reconstructed based on its genomic sequence (Parkhill et al., Nat Genet 35: 32-40 (2003)) using standard methods that rely on genome scale available (Thiele and Palsson Nat Protocols 5: 93-121 (2010) and Santos et al., Methods Enzymol 500: 509-532 (2011)). Because of its phylogenetic proximity, model Escherichia coli ÎAF1260 MG1655 was used as a template (Feist et al Mol., System Biol 3: 121 (2007)). This initial automated reconstruction draft was then manually manipulated intensively, based on the newly obtained literature and experimental data.
    We started with extensive characterization of biomass composition and fluxes of exometabolites at different stages of growth in a reference controlled lot experiment. These data were used to calibrate and refine the model through multiple iterative cycles of in silico simulations and experimentation, eventually leading to ÎBP1870. IBP1870 contains exchange reactions for 202 compounds, defining the potential for compound uptake and excretion, and 1473 internal reactions, of which 1017 are associated with genes, representing 762 genes (22% of the genome). Important features include a detailed biomass equation specific to B. pertussis, newly defined reactions in amino acid metabolism pathways, iron acquisition, sulfur metabolism, and the biosynthesis of lipooligosaccharides and storage compounds.
    Importantly, the ÎBP1870 model does not contain explicit reactions for the production of toxins (such as PT) or adhesins (such as FHA or PRN). As a purely stoichiometric model, it does not contain information on reaction rates and the regulation of gene expression. Model IBP1870 has been validated against previously published datasets (Thalen et al., J. Biotechnol 75: 147-159 (1999) and Thalen et al., Biologicals 34: 289-297 (2006)): the differences in growth performance observed in media containing various ratios of carbon and nitrogen sources were reproduced accurately in silico. The validated model can then be used to design a de novo chemically defined growth medium based on the structure of the metabolic network and the different optimization criteria.
    EXAMPLE 2 Fermentation on a 20-L Scale of Bordetella pertussis in Minimally Defined Chemically De novo Medium
    Given a validated genome-wide reconstruction of B. pertussis (see Example 1), the minimal growth requirements of B. pertussis (i.e. minimal sets of substrates allow B. pertussis to produce biomass). These minimum growth requirements have been defined as the minimum number of active input streams (i.e., the exchange fluxes used for substrate uptake). Among the different possible minimum sets of identified substrates, a unique solution was chosen arbitrarily, and used to formulate de novo chemically defined medium (see composition in Table 1).
    A first preculture in a stirring flask containing 7.5 ml of fresh medium (MSS, derived from Stainer and Schölte's medium, J. Gen. Microbiol 63: 211-220 (1971)) was inoculated with 109 CFU of B. pertussis and incubated at 35 ° C (+/- 1 ° C) and 150 rpm for 24 h (+/- 1 h). The first preculture was used to inoculate a second preculture in a stirring flask containing 100 ml of fresh medium (MSS). The second preculture was incubated at 35 ° C (+ / - 1 ° C) and 150 rpm for 24 h (+/- 1 h), and used to inoculate two shake flasks each containing 1 1 of fresh medium. (Minimum de novo CDM, see composition in Table 1). After growth at 35 ° C (+/- 1 ° C) and 150 rpm for 40 h (+/- 4 h), the two stirring flasks from the third preculture were grouped. Group preculture was used to inoculate a fermenter as soon as the third preculture was stopped. A 20 1 fermentor (Biolafitte) was used. 10 1 of medium were transferred aseptically into the fermenter. The following conditions were used to set the dissolved oxygen (DO) level at 100%: temperature (35 ° C) and head pressure (0.4 bar). Inoculation was obtained by the addition of 1.5 1 of the pooled preculture.
    During the fermentation, the temperature (35 ° C) and the head pressure (0.4 bar) were kept constant. Foam formation was controlled by automatic addition of a polydimethylsiloxane emulsion using a foam controller. The airflow was kept constant at 20 NL / min. the dissolved oxygen level was set at 25% and controlled by increasing agitation when the OD dropped below 25%. The minimum stirring speed was set at 50 rpm. The pH was controlled at 7.2 by the addition of 50% (w / v) acetic acid.
    The main fermentation data are summarized in Table 2.
    Table 1
    Composition of the minimum de novo CDM All values are in mg / 1, unless otherwise indicated
    Table 2
    Main fermentation parameters for B. pertussis grown in de novo minimal CDM
    Initial biomass concentration calculated based on the measured OD650nm of the preculture, i.e., 1.5 * OD / L / min. ** Yields were calculated as the difference between the DO650nm at the end of the fermentation and the OD650nm at the start of fermentation, divided by the concentration of C, N or S in the medium, respectively. *** Total fermentation time is defined as the time at which oxygen consumption decreases (as a consequence of glutamate depletion), resulting in a decrease in agitation speed. **** The average generation time is calculated as follows. First, the number of generations is calculated as the ratio between the DO650nm at the end of the fermentation and the DO650nm at the start of the fermentation, converted to log2. The average generation time is then calculated by dividing the total fermentation time by the number of generations.
    Example 3 High-throughput screening of compounds that modulate the growth behavior of Bordetella pertussis
    The de novo minimal CDM described in Example 2 was used as a basis for screening for growth-modulating compounds, i.e. compounds that will affect either the growth rate or the growth efficiency of B. pertussis, either both.
    A stirring flask containing 7.5 ml of fresh medium (MSS containing 0.604 g / l of niacin) was inoculated with 109 CFU of B. pertussis and incubated at 35 ° C. (+/- 1 ° C.) and 150 ° C. / min for 24 h (+/- 5 h). Cells were harvested by centrifugation, washed twice with 0.9% (w / v) NaCl, and resuspended in fresh medium (de novo minimal CDM, see composition in Table 1) at a DOsonm. theoretical of 0.5, calculated from the DOesonm of the crop before harvest. 20 μl of this cell suspension were used to inoculate each well of a 96-well microtiter plate filled with 180 μΐ of de novo minimal CDM. To each of the wells, 20 μl of a complement solution, which contained one or other of the compounds listed in Table 3, were added. Only the internal wells of the plate were used for the cultures, so that to minimize evaporation, and two controls were included, in which the complement solution was replaced with water.
    The plate was then incubated for 7 days at 35 ° C in a Biotek Synergy H1 reader with constant agitation, and growth was automatically monitored every 10 minutes at an OD650nm · The entire procedure was repeated 7 times, in order to A total of 56 compounds, each at two different concentrations, can be screened in 3 independent repeats.
    For each supplement, the effect on the growth rate (defined as the average growth rate during the first generation, that is, until the doubling of ODsonm) and the growth yield (defined as the maximum ODesonm) was calculated relative to the controls on the same plate. The relative growth yield and growth rate for each complement was then averaged over the three independent repeats, and the standard deviation was calculated. The results are shown in Table 3.
    Table 3 Results of High Throughput Screening of Growth Modulating Compounds
    Based on this high throughput assay, the compounds tested can be classified as follows: (I) Compounds with a positive effect (greater than 105, 00%) on both growth yield and growth rate. This category includes biotin (0.02 mg / l), glycine (20 mM), L-alanine (20 mM), L-arginine (2 and 20 mM), L-cysteine (2 and 20 mM ), L-methionine (2 and 20 mM), L-proline (20 mM), MgCl 2 · 6H 2 O (10 mM), NaHCO 3 (20 mM), and thiamine (10 mg / l). These compounds should be included in the composition of the medium if yield and growth rate are to be improved. (II) Compounds with a positive effect (greater than 105.00%) on the growth yield and no effect (between 95.00% and 105.00%) on the growth rate. This category includes biotin (0.2 mg / l), calcium pantothenate (1 mg / l), folate (0.12 mg / l), L-aspartate (0.225 mM), L-tryptophan ( 0.518 mM), sodium formate (2 mM), sodium L-lactate (50 mM), disodium 2-ketoglutarate (2 and 20 mM), disodium fumarate (2 mM), Na2HPO4 (1 and 10 mM), sucrose (6 g / l), ZnCl2 (10 mg / l), and ZnSO4.7H2O (18.9 mg / l). These compounds should be included in the composition of the medium if the growth yield is to be improved without affecting the rate of growth. (III) Compounds with a positive effect (greater than 105.00%) on the growth yield and a negative effect (less than 95.00%) on the growth rate. This category includes CaCl2.2H20 (3 mM), CoC12.6H20 (4.2 mg / l), glycerol (20 mM), L-glutamine (20 mM), L-leucine (13.3 mM) ), L-lysine (2 mM), MnSO 4 .H 2 O (1.89 mg / l), sodium acetate (50 mM), sodium DL-beta-hydroxybutyrate (20 mM), sodium (20 mM), sodium pyruvate (20 mM), disodium fumarate (20 mM), Na2MoO4.2H2O (9.4 mg / l), NH4Cl (10 mM), sucrose (60 g / l) ), ZnCl2 (100 mg / l), and ZnSO4.7H2O (1.89 mg / l). These compounds should be included in the composition of the medium if the growth yield is to be improved and a reduction in growth rate is required or can be tolerated. (IV) Compounds with a negative effect (less than 95.00%) on both growth yield and growth rate. This category includes citric acid monohydrate (2 and 20 mM), L-isoleucine (2 and 20 mM), l-lysine (20 mM), L-phenylalanine (1.33 and 13.3 mM), L-serine (20 mM), L-threonine (20 mM), L-valine (20 mM), MnSO 4 .H 2 O (18.9 mg / l), sodium pyruvate (2 mM), and succinic acid (2 and 20 mM). These compounds should be included in the composition of the medium if a reduction in both growth yield and growth rate is required or can be tolerated. (V) Compounds with a negative effect (less than 95.00%) on the growth yield and no effect (between 95.00% and 105.00%) on the growth rate. This category includes H3BO3 (23.6 mg / l), L-asparagine (13.3 mM), and L-serine (2 mM). These compounds should be included in the composition of the medium if a reduction in growth yield is required without affecting the rate of growth. (VI) Compounds with a negative effect (less than 95.00%) on the growth yield and a positive effect (greater than 105.00%) on the growth rate. This category includes hemin (5 mg / l) and L-histidine (20 mM). These compounds will need to be included in the composition of the medium if the growth rate needs to be improved, and a reduction in growth yield is required or can be tolerated. (VII) Compounds with no effect (between 95.00% and 105.00%) on growth yield and a negative effect (less than 95.00%) on growth rate. This category includes CoC12.6H20 (0.42 mg / l), H3BO3 (23.6 mg / l), l-aspartate (13.3 mM), L-threonine (2 mM), L- tryptophan (5.18 mM), L-tyrosine (0.221 mM), L-valine (2 mM), Na2MoO4.2H2O (0.94 mg / l), and potassium iodide (0.47 mg / 1). These compounds should be included in the composition of the medium if a reduction in growth rate is required without affecting the growth yield. (VIII) Compounds with no effect (between 95.00% and 105.00%) on both growth yield and growth rate. This category includes adenine (0.1 and 1 mg / l), calcium pantothenate (10 mg / l), choline chloride (0.6 and 6 mg / l), folate (1.2 mg 1), glycerol (2 mM), guanine (0.1 and 1 mg / l), inositol (2.8 and 28 mg / l), L-asparagine (1.33 mM), L-glutamine (2 mM), L-leucine (1.33 mM), L-tyrosine (0.0221 mM), MgCl 2 · 6H 2 O (1 mM), sodium acetate (5 mM), Sodium DL-beta-hydroxybutyrate (2 mM), NAD (10 and 100 mg / l), NH4Cl (1 mM), para-aminobenzoic acid (2 mg / l), potassium iodide (4) , 7 mg / l), pyridoxine (0.2 and 2 mg / l), riboflavin (3 mg / l), thiamine (1 mg / l), and uracil (0.1 mg / l) . (IX) Compounds with no effect (between 95.00% and 105.00%) on the growth yield and with a positive effect (greater than 105.00%) on the growth rate. This category includes CaCl2.2H20 (0.3 mM), glycine (2 mM), hemin (50 mg / l), L-alanine (2 mM), L-histidine (2 mM), 1-proline (2 mM), sodium L-lactate (5 mM), NaHCCg (2 mM), para-aminobenzoic acid (0.2 mg / l), and riboflavin (0.3 mg / ml). 1). These compounds should be added to the composition of the medium if the rate of growth is to be improved without affecting the growth yield.
    Example 4 - Stoichiometric model-based design of a fermentation medium to improve the growth rate From a practical point of view, the kinetic properties of biological systems are of paramount importance for designing fermentation processes. For example, growth kinetics, which is determined by the kinetics of individual reactions in the system, determines the processing time. Similarly, in fermentations intended to produce a protein (whether homologous or recombinant), the dynamic behavior of the expression of the protein in response to the evolution of the fermentation processes, directly determines the amount of the protein. target.
    Genome-wide metabolic models contain only stoichiometric information, and therefore can only be used to predict and / or optimize yields. This is achieved by methods known collectively as constraint-based modeling, where a set of constraints is applied to the model such that the range of possible solutions (i.e. possible network states or flow distributions) is reduced to solutions that satisfy all constraints. One such method is flow balance analysis (FBA), which further optimizes an objective (usually maximizing biomass yield) within the constrained solution space. For example, this method can be used to determine a media composition (a ratio between the substrates) that produces optimal biomass yield. However, in the absence of kinetic information, the reaction rates can not be calculated. Similarly, the regulation of gene expression, another kinetic property of biological systems, is not generally included in such models, and therefore may not be predicted or taken into account during optimization. performance based on the model. Indeed, the construction of kinetic models at the genome scale is currently limited by the availability and quality of information concerning the kinetic properties of the individual enzymatic reactions in the model (Chakrabarti et al., Biotechnol., J. 8: 1043- 1057 (2013)). As a result, growth rates or recombinant protein production rates, for example, can not be accounted for in optimization strategies.
    Purely stoichiometric genome-wide models are fairly well suited for yield optimization, but intrinsically lack the critical information required for rate optimization. Nevertheless, rate optimization can be taken into account in the form of constraints, but by further restricting the space of possible solutions to those that satisfy the desired rate criteria.
    Such a method has been applied to predict an improved medium formulation based on the de novo minimum CDM of Example 2, wherein the growth rate will be higher while maintaining a high growth yield. In practical terms, BAF was performed to maximize growth yield (biomass production) under a set of constraints reflecting (i) the composition of the minimal de novo CDM and (ii) the effect of individual substrates on the rate of growth, based on the high throughput test in Example 3 (i.e. for each substrate, the maximum absorption flux was defined as the maximum concentration showing a non-negative impact on the rate growth, thus favoring substrates with a positive impact on the growth rate compared to those with a negative impact). The composition of the resulting medium is shown in Table 4.
    Fermentation at a scale of 20 l was performed in the enhanced CDM, under the same conditions as those described in Example 2, except that phosphoric acid at 50% (w / v) rather than 50% (w / v) acetic acid was used for pH regulation. The main data are summarized in Table 5. The biomass yield (final biomass concentration) was not different between the two media; the growth rate, however, was significantly higher in the enhanced CDM, as reflected by a shorter average generation time.
    This example demonstrates that kinetic properties such as growth rate can be optimized with purely stoichiometric models, incorporating knowledge about such properties in the form of constraints.
    Table 4
    Composition of the de novo minimal CDM and enhanced CDM. All values are in mg / 1
    Table 5
    Main fermentation parameters for B. pertussis grown in de novo minimal CDM or enhanced CDM
    Initial biomass concentration calculated based on the measured OD 50 nm of preculture, ie, 1.5% pre-fuit / 11.5. ** The total fermentation time is defined as the time at which oxygen consumption decreases (as a result of glutamate depletion), resulting in a decrease in agitation speed. *** The average generation time is calculated as follows. First, the number of generations is calculated as the ratio between the DChsonm at the end of the fermentation and the DOesonm at the start of the fermentation, converted to log2- The average generation time is then calculated by dividing the total fermentation time by the number of generations.
    Example 5 - Stoichiometric Model Based Design of a Fermentation Medium for Improved PT Production
    Pertussis toxin (PT) production by Bordetella pertussis is regulated at the level of gene expression via the BvgAS two-component system. Inhibitors of PT production (called modulators of bvg) have been extensively studied. Among the modulators of bvg, sulfate is one of the most powerful. However, sulphate is part of most B. pertussis media, and in addition, it is the final product of catabolism of cysteine, which is also included in most B. pertussis media.
    A genome-wide stoichiometric metabolic model of B. pertussis was constructed, and used to optimize an existing media composition to improve PT production, using a strategy based on BAF. Since the reactions for PT production were not included in the model, biomass production was used as an objective function to be maximized by ABF. Constraints have been imposed only on the exchange flows. For the compounds present in the existing medium, the exchange reactions were constrained to allow the absorption of the substrate corresponding to twice the concentration that was actually used by the cells in reference cultures used to calibrate the model. No constraints were fixed on the production of these compounds. For compounds not present in the existing medium, no absorption was allowed, but no constraints were placed on maximum production, with the exception of sulphate, sulphite, and sulfur dioxide, whose production was not allowed. Finally, biomass production was constrained exactly to the value measured in the reference fermentations. FBA was performed to maximize biomass production under this set of constraints, while minimizing flux through all network reactions, to avoid unnecessary substrate consumption during unnecessary cycles. The deduced medium composition did not contain sulfate and contained approximately 10 times less cysteine compared to the existing medium. It supported similar biomass production with a 2.2 times higher PT yield.
    The strategy was repeated using a slightly different set of constraints, in which cysteine uptake was not allowed. The composition of the deduced medium contained no cysteine at all, which was replaced by thiosulfate. In this environment, similar biomass yields were obtained, and PT production was 2.4 times higher compared to the existing medium.
    This example demonstrates that the kinetic properties associated with the regulation of gene expression can be optimized with purely stoichiometric models, incorporating knowledge about such properties in the form of constraints.
    权利要求:
    Claims (21)
    [1]
    A method of identifying culture conditions for a cell or organism, the method comprising: (a) obtaining a stoichiometric metabolic model at the genome level of the cell or organism, and b) performing stress-based optimization of the metabolic model of the cell or organism using at least one yield-based constraint, where the yield-based constraint favors a condition identified as promoting a kinetic parameter and / or by removing a condition which has been identified as inhibiting a kinetic parameter so as to improve the culture conditions for both yield and kinetic parameter.
    [2]
    The method of claim 1, wherein the kinetic parameter is a rate, such as one of growth rate, protein production rate, antigen production rate, or gene expression rate.
    [3]
    3. The method of claim 1 or 2, wherein the condition promoting a kinetic parameter or inhibiting a kinetic parameter is selected from a compound or a set of compounds, a supply of oxygen or carbon dioxide, availability of protons, or any of their combinations.
    [4]
    A method according to any one of the preceding claims, wherein the condition promoting a kinetic parameter or inhibiting a kinetic parameter is defined as an absolute amount or a concentration of a compound or set of compounds, the contribution of oxygen or carbon dioxide, or the availability of protons, or any of their combinations.
    [5]
    The method according to any one of the preceding claims, wherein the condition promotes the kinetic parameter without any adverse effect on the yield.
    [6]
    The method of any one of the preceding claims, wherein the condition promotes the kinetic parameter and the yield.
    [7]
    The method of any one of the preceding claims, wherein the kinetic parameter is the growth rate.
    [8]
    The method of any one of the preceding claims, wherein the condition is applied to a chemically defined minimal medium.
    [9]
    The method according to any one of the preceding claims, wherein the kinetic parameter, such as the growth rate, and / or the yield is measured compared to a chemically defined minimal medium.
    [10]
    The method of any one of the preceding claims, wherein the organism is a single-cell organism such as a yeast or bacterium, or a cultured eukaryotic cell.
    [11]
    The method of claim 10, wherein the organism is B. pertussis.
    [12]
    The method of claim 11, wherein the organism is selected from the group consisting of B. pertussis, B. bronchiseptica and B. parapertussis.
    [13]
    The method of claim 11 or 12, wherein the yield-based constraint promotes a condition identified as promoting the growth rate without any adverse effect on the yield and wherein the condition is the presence of a compound selected from CaCl2.2H2O (optionally at 0.3 mM), glycine (optionally at 2 mM), hemin (optionally at 50 mg / l), L-alanine (optionally at 2 mM), L-histidine ( optionally at 2 mM), L-proline (optionally at 2 mM), sodium L-lactate (optionally at 5 mM), NaHCO 3 (optionally at 2 mM), para-aminobenzoic acid (optionally at 0, 2 mg / l), and riboflavin (optionally at 0.3 mg / l), optionally the totalities of said compounds.
    [14]
    The method of claim 11 or 12, wherein the yield-based constraint promotes a condition identified as promoting both growth rate and yield and wherein the condition is the presence of a compound selected from biotin. (optionally at 0.02 mg / l), glycine (optionally at 20 mM), L-alanine (optionally at 20 mM), L-arginine (optionally at 2 and / or 20 mM), L-cysteine (optionally at 2 and / or 20 mM), L-methionine (optionally at 2 and / or 20 mM), L-proline (optionally at 20 mM), MgCl 2 · 6H 2 O (optionally at 10 mM), NaHCO 3 (optionally at 20 mM), and thiamine (optionally at 10 mg / l).
    [15]
    The method according to any one of the preceding claims, further comprising growing the cell or organism in said identified culture conditions and optionally further recovering the cells of the organism or a product derived from it. these last.
    [16]
    The method according to any one of the preceding claims further comprising growing the cell or organism in said identified culture conditions and measuring the growth parameters, and applying said growth parameters thus obtained for further optimize the model.
    [17]
    A method for growth of a cell or an organism, the method comprising growing the cell or organism in said culture conditions identified by a method of identifying culture conditions as described herein, possibly the measurement of the growth parameters, and the application of said growth parameters thus obtained to further optimize the model.
    [18]
    The method of claims 15 to 17, further comprising the step of formulating any product obtained from the cell or organism with an adjuvant or excipient.
    [19]
    The method of claims 15 to 18, further comprising the step of formulating any product obtained from the cell or organism with at least one other antigen, optionally wherein said antigen comprises the pertussis toxin, Filamentous haemagglutinin, pertactin, fimbrial agglutinogen, diphtheria toxoid, tetanus toxoid, N. meningitidis saccharide antigen, hepatitis B surface antigen, inactivated poliovirus (IPV) or antigen saccharide of Haemophilus influenzae b.
    [20]
    20. System comprising: a processor; and a memory adapted to store computer-executable instructions which, when executed, causes the processor to perform constraint-based optimization of a metabolic model of a cell or organism using at least one constraint yield-based, where the yield-based constraint favors a condition identified as promoting a kinetic parameter and / or suppressing a condition that has been identified as inhibiting a kinetic parameter, so as to improve both the yield and the parameter kinetic.
    [21]
    The system of claim 20, wherein the kinetic parameter is one of growth rate, rate of production or consumption of a metabolite, level of gene expression, rate of recombinant protein production or secretion level of recombinant protein.
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    WO2015092650A1|2015-06-25|
    EP3084662A1|2016-10-26|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    US20080003661A1|2006-05-04|2008-01-03|Korea Advanced Institute Of Science And Technology|Method for developing culture medium using genome information and in silico analysis|
    US20130095566A1|2007-07-10|2013-04-18|University Of Pittsburgh - Of The Commonwealth System Of Higher Education|Flux Balance Analysis With Molecular Crowding|
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    GBGB1322303.7A|GB201322303D0|2013-12-17|2013-12-17|Production process|
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