• Something wrong with this record ?

Intracellular CHO Cell Metabolite Profiling Reveals Steady-State Dependent Metabolic Fingerprints in Perfusion Culture

DJ. Karst, RF. Steinhoff, MRG. Kopp, E. Serra, M. Soos, R. Zenobi, M. Morbidelli,

. 2017 ; 33 (4) : 879-890. [pub] 20170303

Language English Country United States

Document type Journal Article

Persistent link   https://www.medvik.cz/link/bmc18025218

Perfusion cell culture processes allow the steady-state culture of mammalian cells at high viable cell density, which is beneficial for overall product yields and homogeneity of product quality in the manufacturing of therapeutic proteins. In this study, the extent of metabolic steady state and the change of the metabolite profile between different steady states of an industrial Chinese hamster ovary (CHO) cell line producing a monoclonal antibody (mAb) was investigated in stirred tank perfusion bioreactors. Matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) of daily cell extracts revealed more than a hundred peaks, among which 76 metabolites were identified by tandem MS (MS/MS) and high resolution Fourier transform ion cyclotron resonance (FT-ICR) MS. Nucleotide ratios (Uridine (U)-ratio, nucleotide triphosphate (NTP)-ratio and energy charge (EC)) and multivariate analysis of all features indicated a consistent metabolite profile for a stable culture performed at 40 × 106 cells/mL over 26 days of culture. Conversely, the reactor was operated continuously so as to reach three distinct steady states one after the other at 20, 60, and 40 × 106 cells/mL. In each case, a stable metabolite profile was achieved after an initial transient phase of approximately three days at constant cell density when varying between these set points. Clear clustering according to cell density was observed by principal component analysis, indicating steady-state dependent metabolite profiles. In particular, varying levels of nucleotides, nucleotide sugar, and lipid precursors explained most of the variance between the different cell density set points. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 33:879-890, 2017.

References provided by Crossref.org

000      
00000naa a2200000 a 4500
001      
bmc18025218
003      
CZ-PrNML
005      
20180713102219.0
007      
ta
008      
180709s2017 xxu f 000 0|eng||
009      
AR
024    7_
$a 10.1002/btpr.2421 $2 doi
035    __
$a (PubMed)27997765
040    __
$a ABA008 $b cze $d ABA008 $e AACR2
041    0_
$a eng
044    __
$a xxu
100    1_
$a Karst, Daniel J $u Dept. of Chemistry and Applied Biosciences, Inst. for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland.
245    10
$a Intracellular CHO Cell Metabolite Profiling Reveals Steady-State Dependent Metabolic Fingerprints in Perfusion Culture / $c DJ. Karst, RF. Steinhoff, MRG. Kopp, E. Serra, M. Soos, R. Zenobi, M. Morbidelli,
520    9_
$a Perfusion cell culture processes allow the steady-state culture of mammalian cells at high viable cell density, which is beneficial for overall product yields and homogeneity of product quality in the manufacturing of therapeutic proteins. In this study, the extent of metabolic steady state and the change of the metabolite profile between different steady states of an industrial Chinese hamster ovary (CHO) cell line producing a monoclonal antibody (mAb) was investigated in stirred tank perfusion bioreactors. Matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) of daily cell extracts revealed more than a hundred peaks, among which 76 metabolites were identified by tandem MS (MS/MS) and high resolution Fourier transform ion cyclotron resonance (FT-ICR) MS. Nucleotide ratios (Uridine (U)-ratio, nucleotide triphosphate (NTP)-ratio and energy charge (EC)) and multivariate analysis of all features indicated a consistent metabolite profile for a stable culture performed at 40 × 106 cells/mL over 26 days of culture. Conversely, the reactor was operated continuously so as to reach three distinct steady states one after the other at 20, 60, and 40 × 106 cells/mL. In each case, a stable metabolite profile was achieved after an initial transient phase of approximately three days at constant cell density when varying between these set points. Clear clustering according to cell density was observed by principal component analysis, indicating steady-state dependent metabolite profiles. In particular, varying levels of nucleotides, nucleotide sugar, and lipid precursors explained most of the variance between the different cell density set points. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 33:879-890, 2017.
650    _2
$a zvířata $7 D000818
650    _2
$a monoklonální protilátky $x biosyntéza $7 D000911
650    12
$a bioreaktory $7 D019149
650    _2
$a CHO buňky $7 D016466
650    12
$a buněčné kultury $7 D018929
650    _2
$a kultivované buňky $7 D002478
650    _2
$a Cricetulus $7 D003412
650    12
$a metabolom $7 D055442
650    _2
$a multivariační analýza $7 D015999
650    12
$a perfuze $7 D010477
650    _2
$a spektrometrie hmotnostní - ionizace laserem za účasti matrice $7 D019032
650    _2
$a spektroskopie infračervená s Fourierovou transformací $7 D017550
650    _2
$a tandemová hmotnostní spektrometrie $7 D053719
655    _2
$a časopisecké články $7 D016428
700    1_
$a Steinhoff, Robert F $u Dept. of Chemistry and Applied Biosciences, Laboratory of Organic Chemistry, ETH Zurich, Zurich, Switzerland.
700    1_
$a Kopp, Marie R G $u Dept. of Chemistry and Applied Biosciences, Inst. for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland.
700    1_
$a Serra, Elisa $u Dept. of Chemistry and Applied Biosciences, Inst. for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland.
700    1_
$a Soos, Miroslav $u Dept. of Chemistry and Applied Biosciences, Inst. for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland. Dept. of Chemical Engineering, University of Chemistry and Technology, Prague, 166 28, Czech Republic.
700    1_
$a Zenobi, Renato $u Dept. of Chemistry and Applied Biosciences, Laboratory of Organic Chemistry, ETH Zurich, Zurich, Switzerland.
700    1_
$a Morbidelli, Massimo $u Dept. of Chemistry and Applied Biosciences, Inst. for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland.
773    0_
$w MED00000800 $t Biotechnology progress $x 1520-6033 $g Roč. 33, č. 4 (2017), s. 879-890
856    41
$u https://pubmed.ncbi.nlm.nih.gov/27997765 $y Pubmed
910    __
$a ABA008 $b sig $c sign $y a $z 0
990    __
$a 20180709 $b ABA008
991    __
$a 20180713102513 $b ABA008
999    __
$a ok $b bmc $g 1317349 $s 1022139
BAS    __
$a 3
BAS    __
$a PreBMC
BMC    __
$a 2017 $b 33 $c 4 $d 879-890 $e 20170303 $i 1520-6033 $m Biotechnology progress $n Biotechnol Prog $x MED00000800
LZP    __
$a Pubmed-20180709

Find record

Citation metrics

Archiving options