Publicação
High-throughput FTIR-based bioprocess analysis of recombinant cyprosin production
| Resumo: | To increase the knowledge of the recombinant cyprosin production process in Saccharomyces cerevisiae cultures, it is relevant to implement efficient bioprocess monitoring techniques. The present work focuses on the implementation of a mid-infrared (MIR) spectroscopy-based tool for monitoring the recombinant culture in a rapid, economic, and high-throughput (using a microplate system) mode. Multivariate data analysis on the MIR spectra of culture samples was conducted. Principal component analysis (PCA) enabled capturing the general metabolic status of the yeast cells, as replicated samples appear grouped together in the score plot and groups of culture samples according to the main growth phase can be clearly distinguished. The PCA-loading vectors also revealed spectral regions, and the corresponding chemical functional groups and biomolecules that mostly contributed for the cell biomolecular fingerprint associated with the culture growth phase. These data were corroborated by the analysis of the samples' second derivative spectra. Partial least square (PLS) regression models built based on the MIR spectra showed high predictive ability for estimating the bioprocess critical variables: biomass (R (2) = 0.99, RMSEP 2.8%); cyprosin activity (R (2) = 0.98, RMSEP 3.9%); glucose (R (2) = 0.93, RMSECV 7.2%); galactose (R (2) = 0.97, RMSEP 4.6%); ethanol (R (2) = 0.97, RMSEP 5.3%); and acetate (R (2) = 0.95, RMSEP 7.0%). In conclusion, high-throughput MIR spectroscopy and multivariate data analysis were effective in identifying the main growth phases and specific cyprosin production phases along the yeast culture as well as in quantifying the critical variables of the process. This knowledge will promote future process optimization and control the recombinant cyprosin bioprocess according to Quality by Design framework. |
|---|---|
| Autores principais: | Sampaio, Pedro |
| Outros Autores: | Sales, Kevin C.; Rosa, Filipa O.; B. Lopes, Marta; Calado, Cecília |
| Assunto: | Cultivation High-throughput analysis Mid-infrared spectroscopy Partial least square regression Principal components analysis Recombinant cyprosin |
| Ano: | 2017 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Instituto Politécnico de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Científico do Instituto Politécnico de Lisboa |
| _version_ | 1866887643264974848 |
|---|---|
| author | Sampaio, Pedro |
| author2 | Sales, Kevin C. Rosa, Filipa O. B. Lopes, Marta Calado, Cecília |
| author2_role | author author author author |
| author_facet | Sampaio, Pedro Sales, Kevin C. Rosa, Filipa O. B. Lopes, Marta Calado, Cecília |
| author_role | author |
| contributor_name_str_mv | RCIPL |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Sampaio, Pedro\",\"Person.identifier.orcid\":\"0000-0003-2917-4904\"},{\"Person.name\":\"Sales, Kevin C.\"},{\"Person.name\":\"Rosa, Filipa O.\"},{\"Person.name\":\"B. Lopes, Marta\",\"Person.identifier.orcid\":\"0000-0002-4135-1857\"},{\"Person.name\":\"Calado, Cecília\",\"Person.identifier.orcid\":\"0000-0002-5264-9755\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | RCIPL |
| datacite.creators.creator.creatorName.fl_str_mv | Sampaio, Pedro Sales, Kevin C. Rosa, Filipa O. B. Lopes, Marta Calado, Cecília |
| datacite.date.Accepted.fl_str_mv | 2017-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2017-03-14T09:01:46Z |
| datacite.date.embargoed.fl_str_mv | 2017-03-14T09:01:46Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| datacite.subjects.subject.fl_str_mv | Cultivation High-throughput analysis Mid-infrared spectroscopy Partial least square regression Principal components analysis Recombinant cyprosin |
| datacite.titles.title.fl_str_mv | High-throughput FTIR-based bioprocess analysis of recombinant cyprosin production |
| dc.contributor.none.fl_str_mv | RCIPL |
| dc.creator.none.fl_str_mv | Sampaio, Pedro Sales, Kevin C. Rosa, Filipa O. B. Lopes, Marta Calado, Cecília |
| dc.date.Accepted.fl_str_mv | 2017-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2017-03-14T09:01:46Z |
| dc.date.embargoed.fl_str_mv | 2017-03-14T09:01:46Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10400.21/6851 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Springer Heidelberg |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| dc.subject.none.fl_str_mv | Cultivation High-throughput analysis Mid-infrared spectroscopy Partial least square regression Principal components analysis Recombinant cyprosin |
| dc.title.fl_str_mv | High-throughput FTIR-based bioprocess analysis of recombinant cyprosin production |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | To increase the knowledge of the recombinant cyprosin production process in Saccharomyces cerevisiae cultures, it is relevant to implement efficient bioprocess monitoring techniques. The present work focuses on the implementation of a mid-infrared (MIR) spectroscopy-based tool for monitoring the recombinant culture in a rapid, economic, and high-throughput (using a microplate system) mode. Multivariate data analysis on the MIR spectra of culture samples was conducted. Principal component analysis (PCA) enabled capturing the general metabolic status of the yeast cells, as replicated samples appear grouped together in the score plot and groups of culture samples according to the main growth phase can be clearly distinguished. The PCA-loading vectors also revealed spectral regions, and the corresponding chemical functional groups and biomolecules that mostly contributed for the cell biomolecular fingerprint associated with the culture growth phase. These data were corroborated by the analysis of the samples' second derivative spectra. Partial least square (PLS) regression models built based on the MIR spectra showed high predictive ability for estimating the bioprocess critical variables: biomass (R (2) = 0.99, RMSEP 2.8%); cyprosin activity (R (2) = 0.98, RMSEP 3.9%); glucose (R (2) = 0.93, RMSECV 7.2%); galactose (R (2) = 0.97, RMSEP 4.6%); ethanol (R (2) = 0.97, RMSEP 5.3%); and acetate (R (2) = 0.95, RMSEP 7.0%). In conclusion, high-throughput MIR spectroscopy and multivariate data analysis were effective in identifying the main growth phases and specific cyprosin production phases along the yeast culture as well as in quantifying the critical variables of the process. This knowledge will promote future process optimization and control the recombinant cyprosin bioprocess according to Quality by Design framework. |
| dirty | 0 |
| eu_rights_str_mv | restrictedAccess |
| format | article |
| fulltext.url.fl_str_mv | https://repositorio.ipl.pt/bitstreams/c8bdebd6-a181-4b97-a4f0-9996d91b248e/download |
| id | ripl_945fed1376c85a31ef6225d2ef029077 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10400.21/6851 |
| instacron_str | ipl |
| institution | Instituto Politécnico de Lisboa |
| instname_str | Instituto Politécnico de Lisboa |
| language | eng |
| network_acronym_str | ripl |
| network_name_str | Repositório Científico do Instituto Politécnico de Lisboa |
| oai_identifier_str | oai:repositorio.ipl.pt:10400.21/6851 |
| organization_str_mv | urn:organizationAcronym:ipl |
| person_str_mv | Sampaio, Pedro Sampaio, Pedro https://www.ciencia-id.pt/AF12-6ABA-43D8 AF12-6ABA-43D8 http://orcid.org/0000-0003-2917-4904 0000-0003-2917-4904 Sales, Kevin C. Rosa, Filipa O. B. Lopes, Marta B. Lopes, Marta https://www.ciencia-id.pt/FD16-A07F-7B12 FD16-A07F-7B12 http://orcid.org/0000-0002-4135-1857 0000-0002-4135-1857 Calado, Cecília Calado, Cecília https://www.ciencia-id.pt/9418-E320-3177 9418-E320-3177 http://orcid.org/0000-0002-5264-9755 0000-0002-5264-9755 |
| publishDate | 2017 |
| publisher.none.fl_str_mv | Springer Heidelberg |
| reponame_str | Repositório Científico do Instituto Politécnico de Lisboa |
| repository_id_str | urn:repositoryAcronym:ripl |
| service_str_mv | urn:repositoryAcronym:ripl |
| spelling | engSpringer Heidelbergpt_PTTo increase the knowledge of the recombinant cyprosin production process in Saccharomyces cerevisiae cultures, it is relevant to implement efficient bioprocess monitoring techniques. The present work focuses on the implementation of a mid-infrared (MIR) spectroscopy-based tool for monitoring the recombinant culture in a rapid, economic, and high-throughput (using a microplate system) mode. Multivariate data analysis on the MIR spectra of culture samples was conducted. Principal component analysis (PCA) enabled capturing the general metabolic status of the yeast cells, as replicated samples appear grouped together in the score plot and groups of culture samples according to the main growth phase can be clearly distinguished. The PCA-loading vectors also revealed spectral regions, and the corresponding chemical functional groups and biomolecules that mostly contributed for the cell biomolecular fingerprint associated with the culture growth phase. These data were corroborated by the analysis of the samples' second derivative spectra. Partial least square (PLS) regression models built based on the MIR spectra showed high predictive ability for estimating the bioprocess critical variables: biomass (R (2) = 0.99, RMSEP 2.8%); cyprosin activity (R (2) = 0.98, RMSEP 3.9%); glucose (R (2) = 0.93, RMSECV 7.2%); galactose (R (2) = 0.97, RMSEP 4.6%); ethanol (R (2) = 0.97, RMSEP 5.3%); and acetate (R (2) = 0.95, RMSEP 7.0%). In conclusion, high-throughput MIR spectroscopy and multivariate data analysis were effective in identifying the main growth phases and specific cyprosin production phases along the yeast culture as well as in quantifying the critical variables of the process. This knowledge will promote future process optimization and control the recombinant cyprosin bioprocess according to Quality by Design framework.application/pdfpt_PTHigh-throughput FTIR-based bioprocess analysis of recombinant cyprosin productionPersonalSampaio, PedroDSpacehttp://dspace.org/items/303f3e22-ec1a-4243-9ded-98c647776e6aDSpacehttp://dspace.org/items/303f3e22-ec1a-4243-9ded-98c647776e6aSampaioPedroCiência IDhttps://www.ciencia-id.ptAF12-6ABA-43D8ORCIDhttp://orcid.org0000-0003-2917-4904Scopus Author IDhttps://www.scopus.com24178064100Sales, Kevin C.Rosa, Filipa O.PersonalB. Lopes, MartaDSpacehttp://dspace.org/items/183b936b-4a1d-4c80-9405-17491abb3d64DSpacehttp://dspace.org/items/183b936b-4a1d-4c80-9405-17491abb3d64B. LopesMartaCiência IDhttps://www.ciencia-id.ptFD16-A07F-7B12ORCIDhttp://orcid.org0000-0002-4135-1857Researcher IDhttps://www.researcherid.comF-5378-2011Scopus Author IDhttps://www.scopus.com55489480400Scopus Author IDhttps://www.scopus.com7202369144Scopus Author IDhttps://www.scopus.com55489480400Scopus Author IDhttps://www.scopus.com57968048700PersonalCalado, CecíliaDSpacehttp://dspace.org/items/e8577257-c64c-4481-9b2b-940fedb360ccDSpacehttp://dspace.org/items/e8577257-c64c-4481-9b2b-940fedb360ccCaladoCecíliaCiência IDhttps://www.ciencia-id.pt9418-E320-3177ORCIDhttp://orcid.org0000-0002-5264-9755Researcher IDhttps://www.researcherid.comE-2102-2014Researcher IDhttps://www.researcherid.comE-2102-2014Scopus Author IDhttps://www.scopus.com6603163260HostingInstitutionOrganizationalRCIPLe-mailmailto:rcaap@sp.ipl.ptrcaap@sp.ipl.ptISSNIsPartOf1367-5435ISSNIsPartOf1476-5535DOIIsPartOf10.1007/s10295-016-1865-02017-03-14T09:01:46Z2017-012017-01-01T00:00:00ZHandlehttp://hdl.handle.net/10400.21/6851http://purl.org/coar/access_right/c_16ecrestricted accessCultivationHigh-throughput analysisMid-infrared spectroscopyPartial least square regressionPrincipal components analysisRecombinant cyprosin2108389 bytesliteraturehttp://purl.org/coar/resource_type/c_6501journal articlehttp://purl.org/coar/access_right/c_16ecapplication/pdffulltexthttps://repositorio.ipl.pt/bitstreams/c8bdebd6-a181-4b97-a4f0-9996d91b248e/downloadJournal of Industrial Microbiology and Biotechnology4414961 |
| spellingShingle | High-throughput FTIR-based bioprocess analysis of recombinant cyprosin production Sampaio, Pedro Cultivation High-throughput analysis Mid-infrared spectroscopy Partial least square regression Principal components analysis Recombinant cyprosin |
| status | SINGLETON |
| subject.fl_str_mv | Cultivation High-throughput analysis Mid-infrared spectroscopy Partial least square regression Principal components analysis Recombinant cyprosin |
| title | High-throughput FTIR-based bioprocess analysis of recombinant cyprosin production |
| title_full | High-throughput FTIR-based bioprocess analysis of recombinant cyprosin production |
| title_fullStr | High-throughput FTIR-based bioprocess analysis of recombinant cyprosin production |
| title_full_unstemmed | High-throughput FTIR-based bioprocess analysis of recombinant cyprosin production |
| title_short | High-throughput FTIR-based bioprocess analysis of recombinant cyprosin production |
| title_sort | High-throughput FTIR-based bioprocess analysis of recombinant cyprosin production |
| topic | Cultivation High-throughput analysis Mid-infrared spectroscopy Partial least square regression Principal components analysis Recombinant cyprosin |
| topic_facet | Cultivation High-throughput analysis Mid-infrared spectroscopy Partial least square regression Principal components analysis Recombinant cyprosin |
| url | http://hdl.handle.net/10400.21/6851 |
| visible | 1 |