Assessment of Biotechnologically Important Filamentous Fungal Biomass by Fourier Transform Raman Spectroscopy
Jazyk angličtina Země Švýcarsko Médium electronic
Typ dokumentu časopisecké články
Grantová podpora
FMETEKN Grant, project number 257622
Norges Forskningsråd
BIONÆR Grant, projects number 268305
Norges Forskningsråd
BIONÆR Grant, projects number 305215
Norges Forskningsråd
HAVBRUK2 Grant, project number 302543
Norges Forskningsråd
MATFONDAVTALE Grant, project number 301834
Norges Forskningsråd
Nordforsk Grant, project number 103507
Norges Forskningsråd
IS-DAAD Grant, project number 309220
Norges Forskningsråd
PubMed
34201486
PubMed Central
PMC8269384
DOI
10.3390/ijms22136710
PII: ijms22136710
Knihovny.cz E-zdroje
- Klíčová slova
- biodiesel, biopolymers, carotenoids, chitin, chitosan, fatty acids, fermentation, fungi, oleaginous microorganisms, pigments,
- MeSH
- analýza hlavních komponent MeSH
- biologické pigmenty analýza MeSH
- biomasa MeSH
- biotechnologie MeSH
- chromatografie plynová MeSH
- fosfor analýza metabolismus MeSH
- Fourierova analýza MeSH
- houby chemie růst a vývoj MeSH
- karotenoidy analýza MeSH
- lipidy analýza MeSH
- magnetická rezonanční spektroskopie MeSH
- Ramanova spektroskopie metody MeSH
- spektrofotometrie ultrafialová MeSH
- spektroskopie infračervená s Fourierovou transformací MeSH
- vápník metabolismus MeSH
- vysokoúčinná kapalinová chromatografie MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- biologické pigmenty MeSH
- fosfor MeSH
- karotenoidy MeSH
- lipidy MeSH
- vápník MeSH
Oleaginous filamentous fungi can accumulate large amount of cellular lipids and biopolymers and pigments and potentially serve as a major source of biochemicals for food, feed, chemical, pharmaceutical, and transport industries. We assessed suitability of Fourier transform (FT) Raman spectroscopy for screening and process monitoring of filamentous fungi in biotechnology. Six Mucoromycota strains were cultivated in microbioreactors under six growth conditions (three phosphate concentrations in the presence and absence of calcium). FT-Raman and FT-infrared (FTIR) spectroscopic data was assessed in respect to reference analyses of lipids, phosphorus, and carotenoids by using principal component analysis (PCA), multiblock or consensus PCA, partial least square regression (PLSR), and analysis of spectral variation due to different design factors by an ANOVA model. All main chemical biomass constituents were detected by FT-Raman spectroscopy, including lipids, proteins, cell wall carbohydrates, and polyphosphates, and carotenoids. FT-Raman spectra clearly show the effect of growth conditions on fungal biomass. PLSR models with high coefficients of determination (0.83-0.94) and low error (approximately 8%) for quantitative determination of total lipids, phosphates, and carotenoids were established. FT-Raman spectroscopy showed great potential for chemical analysis of biomass of oleaginous filamentous fungi. The study demonstrates that FT-Raman and FTIR spectroscopies provide complementary information on main fungal biomass constituents.
Faculty of Chemistry Brno University of Technology Purkyňova 464 118 61200 Brno Czech Republic
Faculty of Science and Technology Norwegian University of Life Sciences P O Box 5003 1432 Ås Norway
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Meyer V., Basenko E.Y., Benz J.P., Braus G.H., Caddick M.X., Csukai M., de Vries R.P., Endy D., Frisvad J.C., Gunde-Cimerman N., et al. Growing a circular economy with fungal biotechnology: A white paper. Fungal Biol. Biotechnol. 2020;7:5. doi: 10.1186/s40694-020-00095-z. PubMed DOI PMC
Meyer V., Andersen M.R., Brakhage A.A., Braus G.H., Caddick M.X., Cairns T.C., de Vries R.P., Haarmann T., Hansen K., Hertz-Fowler C., et al. Current challenges of research on filamentous fungi in relation to human welfare and a sustainable bio-economy: A white paper. Fungal Biol. Biotechnol. 2016;3:6. doi: 10.1186/s40694-016-0024-8. PubMed DOI PMC
Gupta V.K., Treichel H., Shapaval V., Oliveira L.A.d., Tuohy M.G. Microbial Functional Foods and Nutraceuticals. John Wiley & Sons; Hoboken, NJ, USA: 2017. pp. 1–309.
Papanikolaou S., Galiotou-Panayotou M., Fakas S., Komaitis M., Aggelis G. Lipid production by oleaginous Mucorales cultivated on renewable carbon sources. Eur. J. Lipid Sci. Technol. 2007;109:1060–1070. doi: 10.1002/ejlt.200700169. DOI
Qiao W.C., Tao J.Q., Luo Y., Tang T.H., Miao J.H., Yang Q.W. Microbial oil production from solid-state fermentation by a newly isolated oleaginous fungus, Mucor circinelloides Q531 from mulberry branches. R. Soc. Open Sci. 2018;5 doi: 10.1098/rsos.180551. PubMed DOI PMC
Meng X., Yang J.M., Xu X., Zhang L., Nie Q.J., Xian M. Biodiesel production from oleaginous microorganisms. Renew. Energy. 2009;34:1–5. doi: 10.1016/j.renene.2008.04.014. DOI
Formenti L.R., Norregaard A., Bolic A., Hernandez D.Q., Hagemann T., Heins A.L., Larsson H., Mears L., Mauricio-Iglesias M., Kruhne U., et al. Challenges in industrial fermentation technology research. Biotechnol. J. 2014;9:727–738. doi: 10.1002/biot.201300236. PubMed DOI
Kuhar N., Sil S., Verma T., Umapathy S. Challenges in application of Raman spectroscopy to biology and materials. RSC Adv. 2018;8:25888–25908. doi: 10.1039/C8RA04491K. PubMed DOI PMC
Baker M.J., Trevisan J., Bassan P., Bhargava R., Butler H.J., Dorling K.M., Fielden P.R., Fogarty S.W., Fullwood N.J., Heys K.A., et al. Using Fourier transform IR spectroscopy to analyze biological materials. Nat. Protoc. 2014;9:1771–1791. doi: 10.1038/nprot.2014.110. PubMed DOI PMC
Potocki L., Depciuch J., Kuna E., Worek M., Lewinska A., Wnuk M. FTIR and Raman Spectroscopy-Based Biochemical Profiling Reflects Genomic Diversity of Clinical Candida Isolates That May Be Useful for Diagnosis and Targeted Therapy of Candidiasis. Int. J. Mol. Sci. 2019;20:988. doi: 10.3390/ijms20040988. PubMed DOI PMC
Schalk R., Braun F., Frank R., Rädle M., Gretz N., Methner F.-J., Beuermann T. Non-contact Raman spectroscopy for in-line monitoring of glucose and ethanol during yeast fermentations. Bioprocess. Biosyst. Eng. 2017;40:1519–1527. doi: 10.1007/s00449-017-1808-9. PubMed DOI
Papaioannou E.H., Liakopoulou-Kyriakides M., Christofilos D., Arvanitidis I., Kourouklis G. Raman Spectroscopy for Intracellular Monitoring of Carotenoid in Blakeslea trispora. Appl. Biochem. Biotechnol. 2009;159:478–487. doi: 10.1007/s12010-008-8472-0. PubMed DOI
Rebrošová K., Šiler M., Samek O., Růžička F., Bernatová S., Holá V., Ježek J., Zemánek P., Sokolová J., Petráš P. Rapid identification of staphylococci by Raman spectroscopy. Sci. Rep. UK. 2017;7:14846. doi: 10.1038/s41598-017-13940-w. PubMed DOI PMC
Kizovský M., Pilát Z., Mylenko M., Hrouzek P., Kuta J., Skoupý R., Krzyžánek V., Hrubanová K., Adamczyk O., Ježek J., et al. Raman Microspectroscopic Analysis of Selenium Bioaccumulation by Green Alga Chlorella vulgaris. Biosensors. 2021;11:115. doi: 10.3390/bios11040115. PubMed DOI PMC
Moudříková Š., Sadowsky A., Metzger S., Nedbal L., Mettler-Altmann T., Mojzeš P. Quantification of Polyphosphate in Microalgae by Raman Microscopy and by a Reference Enzymatic Assay. Anal. Chem. 2017;89:12006–12013. doi: 10.1021/acs.analchem.7b02393. PubMed DOI
Grace C.E.E., Lakshmi P.K., Meenakshi S., Vaidyanathan S., Srisudha S., Mary M.B. Biomolecular transitions and lipid accumulation in green microalgae monitored by FTIR and Raman analysis. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2020;224:117382. doi: 10.1016/j.saa.2019.117382. PubMed DOI
Pilát Z., Bernatová S., Ježek J., Kirchhoff J., Tannert A., Neugebauer U., Samek O., Zemánek P. Microfluidic Cultivation and Laser Tweezers Raman Spectroscopy of E. coli under Antibiotic Stress. Sensors. 2018;18:1623. doi: 10.3390/s18051623. PubMed DOI PMC
Tafintseva V., Shapaval V., Smirnova M., Kohler A. Extended multiplicative signal correction for FTIR spectral quality test and pre-processing of infrared imaging data. J. Biophotonics. 2020;13:e201960112. doi: 10.1002/jbio.201960112. PubMed DOI
Tahir H.E., Zou X.B., Xiao J.B., Mahunu G.K., Shi J.Y., Xu J.L., Sun D.W. Recent Progress in Rapid Analyses of Vitamins, Phenolic, and Volatile Compounds in Foods Using Vibrational Spectroscopy Combined with Chemometrics: A Review. Food Anal. Method. 2019;12:2361–2382. doi: 10.1007/s12161-019-01573-w. DOI
Biancolillo A., Marini F. Chemometric Methods for Spectroscopy-Based Pharmaceutical Analysis. Front. Chem. 2018;6:576. doi: 10.3389/fchem.2018.00576. PubMed DOI PMC
Salzer R., Siesler H.W. Infrared and Raman Spectroscopic Imaging. Wiley-VCH; Weinheim, Germany: 2009. pp. 65–112.
Tafintseva V., Vigneau E., Shapaval V., Cariou V., Qannari E., Kohler A. Hierarchical classification of microorganisms based on high-dimensional phenotypic data. J. Biophotonics. 2018;11:e201700047. doi: 10.1002/jbio.201700047. PubMed DOI
Zhang X.L., Lin T., Xu J.F., Luo X., Ying Y.B. DeepSpectra: An end-to-end deep learning approach for quantitative spectral analysis. Anal. Chim. Acta. 2019;1058:48–57. doi: 10.1016/j.aca.2019.01.002. PubMed DOI
Liland K.H., Kohler A., Shapaval V. Hot PLS-a framework for hierarchically ordered taxonomic classification by partial least squares. Chemometr. Intell. Lab. 2014;138:41–47. doi: 10.1016/j.chemolab.2014.07.010. DOI
Cannizzaro C., Rhiel M., Marison I., von Stockar U. On-line monitoring of Phaffia rhodozyma fed-batch process with in situ dispersive Raman spectroscopy. Biotechnol. Bioeng. 2003;83:668–680. doi: 10.1002/bit.10698. PubMed DOI
Horiue H., Sasaki M., Yoshikawa Y., Toyofuku M., Shigeto S. Raman spectroscopic signatures of carotenoids and polyenes enable label-free visualization of microbial distributions within pink biofilms. Sci. Rep. UK. 2020;10 doi: 10.1038/s41598-020-64737-3. PubMed DOI PMC
Tauber J.P., Matthaus C., Lenz C., Hoffmeister D., Popp J. Analysis of basidiomycete pigments in situ by Raman spectroscopy. J. Biophotonics. 2018;11 doi: 10.1002/jbio.201700369. PubMed DOI
Li F.W., Xue F., Yu X.H. GC-MS, FTIR and Raman Analysis of Antioxidant Components of Red Pigments from Stemphylium lycopersici. Curr. Microbiol. 2017;74:532–539. doi: 10.1007/s00284-017-1220-3. PubMed DOI
Li K., Cheng J., Ye Q., He Y., Zhou J.H., Cen K.F. In vivo kinetics of lipids and astaxanthin evolution in Haematococcus pluvialis mutant under 15% CO2 using Raman microspectroscopy. Bioresour. Technol. 2017;244:1439–1444. doi: 10.1016/j.biortech.2017.04.116. PubMed DOI
de Oliveira L.F.C., Le Hyaric M., Berg M.M., de Almeida M.V., Edwards H.G.M. Raman spectroscopic characterization of cinnabarin produced by the fungus Pycnoporus sanguineus (Fr.) Murr. J. Raman Spectrosc. 2007;38:1628–1632. doi: 10.1002/jrs.1881. DOI
Culka A., Jehlicka J., Ascaso C., Artieda O., Casero C.M., Wierzchos J. Raman microspectrometric study of pigments in melanized fungi from the hyperarid Atacama desert gypsum crust. J. Raman Spectrosc. 2017;48:1487–1493. doi: 10.1002/jrs.5137. DOI
Arcangeli C., Cannistraro S. In situ Raman microspectroscopic identification and localization of carotenoids: Approach to monitoring of UV-B irradiation stress on antarctic fungus. Biopolymers. 2000;57:179–186. doi: 10.1002/(SICI)1097-0282(2000)57:3<179::AID-BIP6>3.0.CO;2-4. PubMed DOI
Munchberg U., Wagner L., Spielberg E.T., Voigt K., Rosch P., Popp J. Spatially resolved investigation of the oil composition in single intact hyphae of Mortierella spp. with micro-Raman spectroscopy. BBA-Mol. Cell Biol. L. 2013;1831:341–349. doi: 10.1016/j.bbalip.2012.09.015. PubMed DOI
Chiu Y.F., Huang C.K., Shigeto S. In Vivo Probing of the Temperature Responses of Intracellular Biomolecules in Yeast Cells by Label-Free Raman Microspectroscopy. ChemBioChem. 2013;14:1001–1005. doi: 10.1002/cbic.201300096. PubMed DOI
Munchberg U., Wagner L., Rohrer C., Voigt K., Rosch P., Jahreis G., Popp J. Quantitative assessment of the degree of lipid unsaturation in intact Mortierella by Raman microspectroscopy. Anal. Bioanal. Chem. 2015;407:3303–3311. doi: 10.1007/s00216-015-8544-2. PubMed DOI
Kochan K., Peng H.D., Gwee E.S.H., Izgorodina E., Haritos V., Wood B.R. Raman spectroscopy as a tool for tracking cyclopropane fatty acids in genetically engineered Saccharomyces cerevisiae. Analyst. 2019;144:901–912. doi: 10.1039/C8AN01477A. PubMed DOI
Gherman A.M.R., Dina N.E., Chis V., Wieser A., Haisch C. Yeast cell wall–Silver nanoparticles interaction: A synergistic approach between surface-enhanced Raman scattering and computational spectroscopy tools. Spectrochim. Acta A. 2019;222 doi: 10.1016/j.saa.2019.117223. PubMed DOI
Noothalapati H., Sasaki T., Kaino T., Kawamukai M., Ando M., Hamaguchi H., Yamamoto T. Label-free Chemical Imaging of Fungal Spore Walls by Raman Microscopy and Multivariate Curve Resolution Analysis. Sci. Rep. UK. 2016;6:1–10. doi: 10.1038/srep27789. PubMed DOI PMC
Edwards H.G.M., Russell N.C., Weinstein R., Wynnwilliams D.D. Fourier-Transform Raman-Spectroscopic Study of Fungi. J. Raman Spectrosc. 1995;26:911–916. doi: 10.1002/jrs.1250260843. DOI
Esmonde-White K.A., Cuellar M., Uerpmann C., Lenain B., Lewis I.R. Raman spectroscopy as a process analytical technology for pharmaceutical manufacturing and bioprocessing. Anal. Bioanal. Chem. 2017;409:637–649. doi: 10.1007/s00216-016-9824-1. PubMed DOI PMC
De Gussem K., Vandenabeele P., Verbeken A., Moens L. Raman spectroscopic study of Lactarius spores (Russulales, Fungi) Spectrochim. Acta A. 2005;61:2896–2908. doi: 10.1016/j.saa.2004.10.038. PubMed DOI
McGovern A.C., Broadhurst D., Taylor J., Kaderbhai N., Winson M.K., Small D.A., Rowland J.J., Kell D.B., Goodacre R. Monitoring of complex industrial bioprocesses for metabolite concentrations using modern spectroscopies and machine learning: Application to gibberellic acid production. Biotechnol. Bioeng. 2002;78:527–538. doi: 10.1002/bit.10226. PubMed DOI
De Gussem K., Vandenabeele P., Verbeken A., Moens L. Chemotaxonomical identification of spores of macrofungi: Possibilities of Raman spectroscopy. Anal. Bioanal. Chem. 2007;387:2823–2832. doi: 10.1007/s00216-007-1150-1. PubMed DOI
Meenu M., Xu B.J. Application of vibrational spectroscopy for classification, authentication and quality analysis of mushroom: A concise review. Food Chem. 2019;289:545–557. doi: 10.1016/j.foodchem.2019.03.091. PubMed DOI
Witkowska E., Jagielski T., Kaminska A. Genus- and species-level identification of dermatophyte fungi by surface-enhanced Raman spectroscopy. Spectrochim. Acta A. 2018;192:285–290. doi: 10.1016/j.saa.2017.11.008. PubMed DOI
Dina N.E., Gherman A.M.R., Chis V., Sarbu C., Wieser A., Bauer D., Haisch C. Characterization of Clinically Relevant Fungi via SERS Fingerprinting Assisted by Novel Chemometric Models. Anal. Chem. 2018;90:2484–2492. doi: 10.1021/acs.analchem.7b03124. PubMed DOI
Lee C.M., Cho E.M., Ochir E.G., Dembereldorj U., Yang S.I. Chemotaxonomic Raman Spectroscopy Investigation of Ascomycetes and Zygomycetes. Bull. Korean Chem. Soc. 2013;34:1240–1242. doi: 10.5012/bkcs.2013.34.4.1240. DOI
Baranska M., Roman M., Dobrowolski J.C., Schulz H., Baranski R. Recent Advances in Raman Analysis of Plants: Alkaloids, Carotenoids, and Polyacetylenes. Curr. Anal. Chem. 2013;9:108–127. doi: 10.2174/157341113804486455. DOI
Bowie B.T., Chase D.B., Griffiths P.R. Factors affecting the performance of bench-top Raman spectrometers. Part II: Effect of sample. Appl. Spectrosc. 2000;54:200a–207a. doi: 10.1366/0003702001950175. DOI
Bowie B.T., Chase D.B., Griffiths P.R. Factors affecting the performance of bench-top Raman spectrometers. Part I: Instrumental effects. Appl. Spectrosc. 2000;54:164a–173a. doi: 10.1366/0003702001949924. DOI
Moester M.J.B., Zada L., Fokker B., Ariese F., de Boer J.F. Stimulated Raman scattering microscopy with long wavelengths for improved imaging depth. J. Raman Spectrosc. 2019;50:1321–1328. doi: 10.1002/jrs.5494. DOI
Boyaci I.H., Temiz H.T., Genis H.E., Soykut E.A., Yazgan N.N., Guven B., Uysal R.S., Bozkurt A.G., Ilaslan K., Torun O., et al. Dispersive and FT-Raman spectroscopic methods in food analysis. RSC Adv. 2015;5:56606–56624. doi: 10.1039/C4RA12463D. DOI
He H.R., Sun D.W., Pu H.B., Chen L.J., Lin L. Applications of Raman spectroscopic techniques for quality and safety evaluation of milk: A review of recent developments. Crit. Rev. Food Sci. 2019;59:770–793. doi: 10.1080/10408398.2018.1528436. PubMed DOI
Agarwal U.P. 1064 nm FT-Raman spectroscopy for investigations of plant cell walls and other biomass materials. Front. Plant. Sci. 2014;5:490. doi: 10.3389/fpls.2014.00490. PubMed DOI PMC
Kendel A., Zimmermann B. Chemical Analysis of Pollen by FT-Raman and FTIR Spectroscopies. Front. Plant. Sci. 2020;11:352. doi: 10.3389/fpls.2020.00352. PubMed DOI PMC
Dzurendova S., Zimmermann B., Kohler A., Tafintseva V., Slany O., Certik M., Shapaval V. Microcultivation and FTIR spectroscopy-based screening revealed a nutrient-induced co-production of high-value metabolites in oleaginous Mucoromycota fungi. PLoS ONE. 2020;15:e0234870. doi: 10.1371/journal.pone.0234870. PubMed DOI PMC
Kosa G., Kohler A., Tafintseva V., Zimmermann B., Forfang K., Afseth N.K., Tzimorotas D., Vuoristo K.S., Horn S.J., Mounier J., et al. Microtiter plate cultivation of oleaginous fungi and monitoring of lipogenesis by high-throughput FTIR spectroscopy. Microb. Cell Fact. 2017;16 doi: 10.1186/s12934-017-0716-7. PubMed DOI PMC
Kosa G., Shapaval V., Kohler A., Zimmermann B. FTIR spectroscopy as a unified method for simultaneous analysis of intra- and extracellular metabolites in high-throughput screening of microbial bioprocesses. Microb. Cell Fact. 2017;16 doi: 10.1186/s12934-017-0817-3. PubMed DOI PMC
Kosa G., Zimmermann B., Kohler A., Ekeberg D., Afseth N.K., Mounier J., Shapaval V. High-throughput screening of Mucoromycota fungi for production of low- and high-value lipids. Biotechnol. Biofuels. 2018;11 doi: 10.1186/s13068-018-1070-7. PubMed DOI PMC
Dzurendova S., Zimmermann B., Kohler A., Reitzel K., Nielsen U.G., Dupuy-Galet B.X., Leivers S., Horn S.J., Shapaval V. Calcium Affects Polyphosphate and Lipid Accumulation in Mucoromycota Fungi. J. Fungi. 2021;7:300. doi: 10.3390/jof7040300. PubMed DOI PMC
Forfang K., Zimmermann B., Kosa G., Kohler A., Shapaval V. FTIR Spectroscopy for Evaluation and Monitoring of Lipid Extraction Efficiency for Oleaginous Fungi. PLoS ONE. 2017;12:e0170611. doi: 10.1371/journal.pone.0170611. PubMed DOI PMC
Beever R.E., Burns D.J.W. Phosphorus Uptake, Storage and Utilization by Fungi. In: Woolhouse H.W., editor. Advances in Botanical Research. Volume 8. Academic Press; San Diego, CA, USA: 1981. pp. 127–219.
Ye Y.L., Gan J., Hu B. Screening of Phosphorus-Accumulating Fungi and Their Potential for Phosphorus Removal from Waste Streams. Appl. Biochem. Biotechnol. 2015;177:1127–1136. doi: 10.1007/s12010-015-1801-1. PubMed DOI
Dzurendova S., Zimmermann B., Tafintseva V., Kohler A., Horn S.J., Shapaval V. Metal and Phosphate Ions Show Remarkable Influence on the Biomass Production and Lipid Accumulation in Oleaginous Mucor circinelloides. J. Fungi. 2020;6:260. doi: 10.3390/jof6040260. PubMed DOI PMC
Ramos I.B., Miranda K., Ulrich P., Ingram P., LeFurgey A., Machado E.A., de Souza W., Docampo R. Calcium- and polyphosphate-containing acidocalcisomes in chicken egg yolk. Biol. Cell. 2010;102:421–434. doi: 10.1042/BC20100011. PubMed DOI
Fontaine T., Mouyna I., Hartland R.P., Paris S., Latge J.P. From the surface to the inner layer of the fungal cell wall. Biochem. Soc. T. 1997;25:194–199. doi: 10.1042/bst0250194. PubMed DOI
Cabib E., Bowers B., Sburlati A., Silverman S.J. Fungal Cell-Wall Synthesis—The Construction of a Biological Structure. Microbiol. Sci. 1988;5:370–375. PubMed
Bartnick S. Cell Wall Chemistry Morphogenesis and Taxonomy of Fungi. Annu. Rev. Microbiol. 1968;22:87–108. doi: 10.1146/annurev.mi.22.100168.000511. PubMed DOI
Dzurendova S., Zimmermann B., Tafintseva V., Kohler A., Ekeberg D., Shapaval V. The influence of phosphorus source and the nature of nitrogen substrate on the biomass production and lipid accumulation in oleaginous Mucoromycota fungi. Appl. Microbiol. Biotechnol. 2020;104:8065–8076. doi: 10.1007/s00253-020-10821-7. PubMed DOI PMC
Martinezcadena G., Ruizherrera J. Activation of Chitin Synthetase from Phycomyces-Blakesleeanus by Calcium and Calmodulin. Arch. Microbiol. 1987;148:280–285. doi: 10.1007/BF00456705. DOI
Papp T., Velayos A., Bartok T., Eslava A., Vagvolgyi C., Iturriaga E. Heterologous expression of astaxanthin biosynthesis genes in Mucor circinelloides. Appl. Microbiol. Biotechnol. 2006;69:526–531. doi: 10.1007/s00253-005-0026-6. PubMed DOI
Papp T., Nagy G., Csernetics Á., Szekeres A., Vágvölgyi C. Beta-carotene production by Mucoralean fungi. J. Eng. Anim. 2009;7:173–176.
Naz T., Nosheen S., Li S., Nazir Y., Mustafa K., Liu Q., Garre V., Song Y. Comparative Analysis of β-Carotene Production by Mucor circinelloides Strains CBS 277.49 and WJ11 under Light and Dark Conditions. Metabolites. 2020;10:38. doi: 10.3390/metabo10010038. PubMed DOI PMC
Zajac A., Hanuza J., Wandas M., Dyminska L. Determination of N-acetylation degree in chitosan using Raman spectroscopy. Spectrochim. Acta A. 2015;134:114–120. doi: 10.1016/j.saa.2014.06.071. PubMed DOI
Jehlicka J., Edwards H.G.M., Orenc A. Raman Spectroscopy of Microbial Pigments. Appl. Environ. Microb. 2014;80:3286–3295. doi: 10.1128/AEM.00699-14. PubMed DOI PMC
Avalos J., Limon M.C. Biological roles of fungal carotenoids. Curr. Genet. 2015;61:309–324. doi: 10.1007/s00294-014-0454-x. PubMed DOI
Hassani S., Martens H., Qannari E.M., Hanafi M., Borge G.I., Kohler A. Analysis of -omics data: Graphical interpretation- and validation tools in multi-block methods. Chemometr. Intell. Lab. 2010;104:140–153. doi: 10.1016/j.chemolab.2010.08.008. DOI
Diehn S., Zimmermann B., Tafintseva V., Seifert S., Bagcioglu M., Ohlson M., Weidner S., Fjellheim S., Kohler A., Kneipp J. Combining Chemical Information From Grass Pollen in Multimodal Characterization. Front. Plant. Sci. 2020;10:1788. doi: 10.3389/fpls.2019.01788. PubMed DOI PMC
Hassani S., Hanafi M., Qannari E., Kohler A. Deflation strategies for multi-block principal component analysis revisited. Chemometr. Intell. Lab. 2013;120:154–168. doi: 10.1016/j.chemolab.2012.08.011. DOI
Kavadia A., Komaitis M., Chevalot I., Blanchard F., Marc I., Aggelis G. Lipid and γ-linolenic acid accumulation in strains of Zygomycetes growing on glucose. J. Am. Oil Chem. Soc. 2001;78:341–346. doi: 10.1007/s11746-001-0266-3. DOI
Kosa G., Vuoristo K.S., Horn S.J., Zimmermann B., Afseth N.K., Kohler A., Shapaval V. Assessment of the scalability of a microtiter plate system for screening of oleaginous microorganisms. Appl. Microbiol. Biotechnol. 2018;102:4915–4925. doi: 10.1007/s00253-018-8920-x. PubMed DOI PMC
Demsar J., Curk T., Erjavec A., Gorup C., Hocevar T., Milutinovic M., Mozina M., Polajnar M., Toplak M., Staric A., et al. Orange: Data Mining Toolbox in Python. J. Mach. Learn. Res. 2013;14:2349–2353.
Toplak M., Birarda G., Read S., Sandt C., Rosendahl S.M., Vaccari L., Demšar J., Borondics F. Infrared Orange: Connecting Hyperspectral Data with Machine Learning. Synchrotron Radiat. News. 2017;30:40–45. doi: 10.1080/08940886.2017.1338424. DOI
Guo S.X., Kohler A., Zimmermann B., Heinke R., Stockel S., Rosch P., Popp J., Bocklitz T. Extended Multiplicative Signal Correction Based Model Transfer for Raman Spectroscopy in Biological Applications. Anal. Chem. 2018;90:9787–9795. doi: 10.1021/acs.analchem.8b01536. PubMed DOI
Zimmermann B., Kohler A. Optimizing Savitzky-Golay Parameters for Improving Spectral Resolution and Quantification in Infrared Spectroscopy. Appl. Spectrosc. 2013;67:892–902. doi: 10.1366/12-06723. PubMed DOI
Curtasu M.V., Tafintseva V., Bendiks Z.A., Marco M.L., Kohler A., Xu Y.T., Norskov N.P., Laerke H.N., Knudsen K.E.B., Hedemann M.S. Obesity-Related Metabolome and Gut Microbiota Profiles of Juvenile Gottingen Minipigs-Long-Term Intake of Fructose and Resistant Starch. Metabolites. 2020;10:456. doi: 10.3390/metabo10110456. PubMed DOI PMC
Westerhuis J.A., Kourti T., MacGregor J.F. Analysis of multiblock and hierarchical PCA and PLS models. J. Chemometr. 1998;12:301–321. doi: 10.1002/(SICI)1099-128X(199809/10)12:5<301::AID-CEM515>3.0.CO;2-S. DOI
Harrington P.D., Vieira N.E., Espinoza J., Nien J.K., Romero R., Yergey A.L. Analysis of variance-principal component analysis: A soft tool for proteomic discovery. Anal. Chim. Acta. 2005;544:118–127. doi: 10.1016/j.aca.2005.02.042. DOI
Smilde A.K., Jansen J.J., Hoefsloot H.C.J., Lamers R.J.A.N., van der Greef J., Timmerman M.E. ANOVA-simultaneous component analysis (ASCA): A new tool for analyzing designed metabolomics data. Bioinformatics. 2005;21:3043–3048. doi: 10.1093/bioinformatics/bti476. PubMed DOI