Abstract
Yeasts are one of the mostly used microorganisms as models in several studies. A wide range of applications in different processes can be attributed to their intrinsic characteristics. They are eukaryotes and therefore valuable expression hosts that require elaborate post-translational modifications. Their arsenal of proteins has become a valuable biochemical tool for the catalysis of several reactions of great value to the food (beverages), pharmaceutical and energy industries. Currently, the main challenge in systemic yeast biology is the understanding of the expression, function and regulation of the protein pool encoded by such microorganisms. In this review, we will provide an overview of the proteomic methodologies used in the analysis of yeasts. This research focuses on the advantages and improvements in their most recent applications with an understanding of the functionality of the proteins of these microorganisms, as well as an update of the advances of methodologies employed in mass spectrometry.
Keywords: Functional proteomics, quantitative proteomics, protein profiling, metabolism, mass spectrometry, yeast.
Graphical Abstract
[1]
Papagianni, M. Fungal morphology and metabolite production in submerged mycelial processes. Biotechnol. Adv., 2004, 22(3), 189-259.
[2]
Pretorius, I.S. Tailoring wine yeast for the new millennium: Novel approaches to the ancient art of winemaking. Yeast, 2000, 16(8), 675-729.
[4]
Yofe, I.; Schuldiner, M. Primers-4-Yeast: a comprehensive web tool for planning primers for Saccharomyces cerevisiae. Yeast, 2014, 31(2), 77-80.
[5]
Romanos, M.A.; Scorer, C.A.; Clare, J.J. Foreign gene expression in yeast: A review. Yeast, 1992, 8(6), 423-488.
[6]
Hebert, A.S.; Richards, A.L.; Bailey, D.J.; Ulbrich, A.; Coughlin, E.E.; Westphall, M.S.; Coon, J.J. The one hour yeast proteome. Mol. Cell. Proteomics, 2014, 13(1), 339-347.
[7]
Chong, Y.T.; Koh, J.L.; Friesen, H.; Duffy, S.K.; Cox, M.J.; Moses, A.; Moffat, J.; Boone, C.; Andrews, B.J. Yeast proteome dynamics from single cell imaging and automated analysis. Cell, 2015, 161(6), 1413-1424.
[8]
Liu, Y.; Beyer, A.; Aebersold, R. On the dependency of cellular protein levels on mRNA abundance. Cell, 2016, 165(3), 535-550.
[10]
Anderson, N.L.; Anderson, N.G. Proteome and proteomics: New technologies, new concepts, and new words. Electrophoresis, 1998, 19(11), 1853-1861.
[11]
Berezovsky, I.N.; Guarnera, E.; Zheng, Z.; Eisenhaber, B.; Eisenhaber, F. Protein function machinery: from basic structural units to modulation of activity. Curr. Opin. Struct. Biol., 2017, 42, 67-74.
[12]
Aslam, B.; Basit, M.; Nisar, M.A.; Khurshid, M.; Rasool, M.H. Proteomics: Technologies and their applications. J. Chromatogr. Sci., 2017, 55(2), 182-196.
[13]
Westman, J.O.; Taherzadeh, M.J.; Franzen, C.J. Proteomic analysis of the increased stress tolerance of Saccharomyces cerevisiae encapsulated in liquid core alginate-chitosan capsules. PLoS One, 2012, 7(11), e49335.
[14]
Van Oudenhove, L.; Devreese, B. A review on recent developments in mass spectrometry instrumentation and quantitative tools advancing bacterial proteomics. Appl. Microbiol. Biotechnol., 2013, 97(11), 4749-4762.
[15]
Chalupova, J.; Raus, M.; Sedlarova, M.; Sebela, M. Identification of fungal microorganisms by MALDI-TOF mass spectrometry. Biotechnol. Adv., 2014, 32(1), 230-241.
[16]
Szopinska, A.; Christ, E.; Planchon, S.; Konig, H.; Evers, D.; Renaut, J. Stuck at work? Quantitative proteomics of environmental wine yeast strains reveals the natural mechanism of overcoming stuck fermentation. Proteomics, 2016, 16(4), 593-608.
[17]
Tokpohozin, S.E.; Lauterbach, A.; Fischer, S.; Behr, J.; Sacher, B.; Becker, T. Phenotypical and molecular characterization of yeast content in the starter of “Tchoukoutou,” a Beninese African sorghum beer. Eur. Food Res. Technol., 2016, 242(12), 2147-2160.
[18]
Kerr, E.D.; Schulz, B.L. Vegemite Beer: Yeast extract spreads as nutrient supplements to promote fermentation. PeerJ, 2016, 4, e2271.
[19]
Santos, R.M.; Nogueira, F.C.; Brasil, A.A.; Carvalho, P.C.; Leprevost, F.V.; Domont, G.B.; Eleutherio, E.C. Quantitative proteomic analysis of the Saccharomyces cerevisiae industrial strains CAT-1 and PE-2. J. Proteomics, 2017, 151, 114-121.
[20]
Chen, L.; Lee, J.J.L.; Zhang, J.; Chen, W.N. Comparative proteomic analysis of engineered Saccharomyces cerevisiae with enhanced free fatty acid accumulation. Appl. Microbiol. Biotechnol., 2016, 100(3), 1407-1420.
[21]
Qu, M.; An, B.; Shen, S.; Zhang, M.; Shen, X.; Duan, X.; Balthasar, J.P.; Qu, J. Qualitative and quantitative characterization of protein biotherapeutics with liquid chromatography mass spectrometry. Mass Spectrom. Rev., 2017, 36(6), 734-754.
[22]
Miah, S.; Banks, C.A.; Adams, M.K.; Florens, L.; Lukong, K.E.; Washburn, M.P. Advancement of mass spectrometry-based proteomics technologies to explore triple negative breast cancer. Mol. Biosyst., 2016, 13(1), 42-55.
[23]
Bond, U.; Blomberg, A. Principles and applications of genomics and proteomics in the analysis of industrial yeast strains.In: Yeasts in Food and Beverages; Querol, A.; Fleet, G., Eds.; Springer Berlin Heidelberg: Berlin, Heidelberg, 2006, pp. 175-213.
[24]
Wright, E.P.; Partridge, M.A.; Padula, M.P.; Gauci, V.J.; Malladi, C.S.; Coorssen, J.R. Top-down proteomics: enhancing 2D gel electrophoresis from tissue processing to high-sensitivity protein detection. Proteomics, 2014, 14(7-8), 872-889.
[25]
Kohl, F.J.; Sanchez-Hernandez, L.; Neususs, C. Capillary electrophoresis in two-dimensional separation systems: Techniques and applications. Electrophoresis, 2015, 36(1), 144-158.
[26]
Magdeldin, S.; Enany, S.; Yoshida, Y.; Xu, B.; Zhang, Y.; Zureena, Z.; Lokamani, I.; Yaoita, E.; Yamamoto, T. Basics and recent advances of two dimensional- polyacrylamide gel electrophoresis. Clin. Proteomics, 2014, 11(1), 16.
[27]
Feret, R.; Lilley, K.S. Protein profiling using two-dimensional
difference gel electrophoresis (2-D DIGE). Curr. Protoc. Protein
Sci., 2014, 75, Unit 22 2.
[28]
Arentz, G.; Weiland, F.; Oehler, M.K.; Hoffmann, P. State of the art of 2D DIGE. Proteomics Clin. Appl., 2015, 9(3-4), 277-288.
[29]
Oliveira, B.M.; Coorssen, J.R.; Martins-de-Souza, D. 2DE: The phoenix of proteomics. J. Proteomics, 2014, 104, 140-150.
[30]
Pomastowski, P.; Buszewski, B. Two-dimensional gel electrophoresis in the light of new developments. TrAC. Trends Analyt. Chem., 2014, 53, 167-177.
[31]
Washburn, M.P.; Yates, J.R. New methods of proteome analysis: Multidimensional chromatography and mass spectrometry. Trends Biotechnol., 2000, 18, 27-30.
[32]
Washburn, M.P. Utilisation of proteomics datasets generated via multidimensional protein identification technology (MudPIT). Brief. Funct. Genomics Proteomics, 2004, 3(3), 280-286.
[33]
The, M.; Tasnim, A.; Kall, L. How to talk about protein-level false discovery rates in shotgun proteomics. Proteomics, 2016, 16(18), 2461-2469.
[34]
Lereim, R.R.; Oveland, E.; Berven, F.S.; Vaudel, M.; Barsnes, H. Visualization, inspection and interpretation of shotgun proteomics identification results. Adv. Exp. Med. Biol., 2016, 919, 227-235.
[35]
Hamzeiy, H.; Cox, J. What computational non-targeted mass spectrometry-based metabolomics can gain from shotgun proteomics. Curr. Opin. Biotechnol., 2017, 43, 141-146.
[36]
Nagaraj, N.; Kulak, N.A.; Cox, J.; Neuhauser, N.; Mayr, K.; Hoerning, O.; Vorm, O.; Mann, M. System-wide perturbation analysis with nearly complete coverage of the yeast proteome by single-shot ultra HPLC runs on a bench top Orbitrap. Mol. Cell
Proteomics, 2012, 11(3), M111 013722.
[37]
Picotti, P.; Clement-Ziza, M.; Lam, H.; Campbell, D.S.; Schmidt, A.; Deutsch, E.W.; Rost, H.; Sun, Z.; Rinner, O.; Reiter, L.; Shen, Q.; Michaelson, J.J.; Frei, A.; Alberti, S.; Kusebauch, U.; Wollscheid, B.; Moritz, R.L.; Beyer, A.; Aebersold, R. A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis. Nature, 2013, 494(7436), 266-270.
[38]
Webb, K.J.; Xu, T.; Park, S.K.; Yates, J.R. 3rd. Modified MuDPIT separation identified 4488 proteins in a system-wide analysis of quiescence in yeast. J. Proteome Res., 2013, 12(5), 2177-2184.
[39]
Bianco, L.; Perrotta, G. Methodologies and perspectives of proteomics applied to filamentous fungi: From sample preparation to secretome analysis. Int. J. Mol. Sci., 2015, 16(3), 5803-5829.
[40]
Gygi, S.P.; Rochon, Y.; Franza, B.R.; Aebersold, R. Correlation between protein and mRNA abundance in yeast. Mol. Cell. Biol., 1999, 19(3), 1720-1730.
[41]
Garcia-Santamarina, S.; Boronat, S.; Domenech, A.; Ayte, J.; Molina, H.; Hidalgo, E. Monitoring in vivo reversible cysteine oxidation in proteins using ICAT and mass spectrometry. Nat. Protoc., 2014, 9(5), 1131-1145.
[42]
Koppel, I.; Fainzilber, M. Omics approaches for subcellular translation studies. Mol. Omics, 2018, 14(6), 380-388.
[43]
Zhao, X.; Hui, D.S.; Lee, R.; Edwards, J.L. Ratiometric quantitation of thiol metabolites using non-isotopic mass tags. Anal. Chim. Acta, 2018, 1037, 274-280.
[44]
Jia, S.; Wang, R.; Wu, K.; Jiang, H.; Du, Z. Elucidation of the mechanism of action for metal based anticancer drugs by mass spectrometry-based quantitative proteomics. Molecules, 2019, 24(3), 581.
[45]
Aguilar-Pontes, M.V.; de Vries, R.P.; Zhou, M. (Post-)genomics approaches in fungal research. Brief. Funct. Genomics, 2014, 13(6), 424-439.
[46]
Casey, T.M.; Khan, J.M.; Bringans, S.D.; Koudelka, T.; Takle, P.S.; Downs, R.A.; Livk, A.; Syme, R.A.; Tan, K.C.; Lipscombe, R.J. Analysis of reproducibility of proteome coverage and quantitation using isobaric mass tags (iTRAQ and TMT). J. Proteome Res., 2017, 16(2), 384-392.
[47]
Culibrk, L.; Croft, C.A.; Tebbutt, S.J. Systems biology approaches for host-fungal interactions: An expanding multi-omics frontier. OMICS, 2016, 20(3), 127-138.
[48]
Spanos, C.; Moore, J.B. Sample preparation approaches for iTRAQ labeling and quantitative proteomic analyses in systems biology. Methods Mol. Biol., 2016, 1394, 15-24.
[49]
Searle, B.C.; Yergey, A.L. An efficient solution for resolving iTRAQ and TMT channel crosstalk. J. Mass Spectrometry., 2019 In press
[50]
Mirzaei, M.; Pascovici, D.; Wu, J.X.; Chick, J.; Wu, Y.; Cooke, B.; Haynes, P.; Molloy, M.P. TMT one-stop shop: From reliable sample preparation to computational analysis platform.In: Proteome Bioinformatics; Springer, 2017, pp. 45-66.
[51]
Gonneaud, A.; Asselin, C.; Boudreau, F.; Boisvert, F.M. Phenotypic analysis of organoids by proteomics. Proteomics, 2017, 17(20), 1700023.
[52]
Chen, B.; Zhang, D.; Wang, X.; Ma, W.; Deng, S.; Zhang, P.; Zhu, H.; Xu, N.; Liang, S. Proteomics progresses in microbial physiology and clinical antimicrobial therapy. Eur. J. Clin. Microbiol. Infect. Dis., 2017, 36(3), 403-413.
[53]
Hsu, J.L.; Chen, S.H. Stable isotope dimethyl labelling for
quantitative proteomics and beyond. Philos. Trans. A Math. Phys.
Eng. Sci., 2016, 374(2079), pii: 20150364
[54]
Hsu, J.L.; Huang, S.Y.; Chow, N.H.; Chen, S.H. Stable-isotope dimethyl labeling for quantitative proteomics. Anal. Chem., 2003, 75(24), 6843-6852.
[55]
Frost, D.C.; Rust, C.J.; Robinson, R.A.S.; Li, L.; Increased, N. N-dimethyl leucine isobaric tag multiplexing by a combined precursor isotopic labeling and isobaric tagging approach. Anal. Chem., 2018, 90(18), 10664-10669.
[56]
Renvoise, M.; Bonhomme, L.; Davanture, M.; Zivy, M.; Lemaire, C. Phosphoproteomic analysis of isolated mitochondria in yeast. Methods Mol. Biol., 2017, 1636, 283-299.
[57]
Renvoise, M.; Bonhomme, L.; Davanture, M.; Valot, B.; Zivy, M.; Lemaire, C. Quantitative variations of the mitochondrial proteome and phosphoproteome during fermentative and respiratory growth in Saccharomyces cerevisiae. J. Proteomics, 2014, 106, 140-150.
[58]
Ahmad, Y.; Lamond, A.I. A perspective on proteomics in cell biology. Trends Cell Biol., 2014, 24(4), 257-264.
[59]
Mann, M.; Andersen, J.; Ishihama, Y.; Rappsilber, J.; Ong, S.; Foster, L.; Blagoev, B.; Kratchmarova, I.; Lasonder, E. Mass spectrometry based proteomics in cell biology and signaling research. Proceedings of the Australian Society for Biochemistry and Molecular Biology, 2002. Plenary-13
[60]
de Godoy, L.M. SILAC yeast: from labeling to comprehensive proteome quantification. Methods Mol. Biol., 2014, 1156, 81-109.
[61]
Kaneva, I.N.; Longworth, J.; Sudbery, P.E.; Dickman, M.J. Quantitative proteomic analysis in Candida albicans using SILAC‐based mass spectrometry. Proteomics, 2018, 18(5-6), 1700278.
[62]
Gruhler, A.; Olsen, J.V.; Mohammed, S.; Mortensen, P.; Faergeman, N.J.; Mann, M.; Jensen, O.N. Quantitative phosphoproteomics applied to the yeast pheromone signaling pathway. Mol. Cell. Proteomics, 2005, 4(3), 310-327.
[63]
Jang, W.E.; Kim, M.S. SILAC expands its territory to the pathogenic yeast, Candida albicans. Proteomics, 2018, 18(5-6), 1700458.
[64]
Noberini, R.; Bonaldi, T. A super-SILAC strategy for the accurate and multiplexed profiling of histone posttranslational modifications.In: Methods in enzymology; Elsevier, 2017, Vol. 586, pp. 311-332.
[65]
Anand, S.; Samuel, M.; Ang, C-S.; Keerthikumar, S.; Mathivanan, S. Label-based and label-free strategies for protein quantitation.In: Proteome Bioinformatics; Springer, 2017, pp. 31-43.
[66]
Greening, D.W.; Xu, R.; Gopal, S.K.; Rai, A.; Simpson, R.J. Proteomic insights into extracellular vesicle biology–defining exosomes and shed microvesicles. Expert Rev. Proteomics, 2017, 14(1), 69-95.
[67]
Aoki, W.; Ueda, T.; Tatsukami, Y.; Kitahara, N.; Morisaka, H.; Kuroda, K.; Ueda, M. Time-course proteomic profile of Candida albicans during adaptation to a fetal serum. Pathog. Dis., 2013, 67(1), 67-75.
[68]
Laskay, U.A.; Srzentic, K.; Fornelli, L.; Upir, O.; Kozhinov, A.N.; Monod, M.; Tsybin, Y.O. Practical considerations for improving the productivity of mass spectrometry-based proteomics. Chimia (Aarau), 2013, 67(4), 244-249.
[69]
Beck, S.; Michalski, A.; Raether, O.; Lubeck, M.; Kaspar, S.; Goedecke, N.; Baessmann, C.; Hornburg, D.; Meier, F.; Paron, I. The Impact II, a very high-resolution quadrupole time-of-flight instrument (QTOF) for deep shotgun proteomics. Mol. Cell. Proteomics, 2015, 14(7), 2014-2029.
[70]
Ghezzi, P.; Chan, P. Redox proteomics applied to the thiol secretome. Antioxid. Redox Signal., 2017, 26(7), 299-312.
[71]
Goeminne, L.J.; Gevaert, K.; Clement, L. Experimental design and data-analysis in label-free quantitative LC/MS proteomics: A tutorial with MSqRob. J. Proteomics, 2018, 171, 23-36.
[72]
Lyutvinskiy, Y.; Yang, H.; Rutishauser, D.; Zubarev, R.A. In silico instrumental response correction improves precision of label-free proteomics and accuracy of proteomics-based predictive models. Mol. Cell. Proteomics, 2013, 12(8), 2324-2331.
[73]
Fredens, J.; Engholm-Keller, K.; Moller-Jensen, J.; Larsen, M.R.; Faergeman, N.J. Identification of novel protein functions and signaling mechanisms by genetics and quantitative phosphoproteomics in Caenorhabditis elegans. Methods Mol. Biol., 2014, 1188, 107-124.
[74]
Cox, J.; Hein, M.Y.; Luber, C.A.; Paron, I.; Nagaraj, N.; Mann, M. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol. Cell. Proteomics, 2014, 13(9), 2513-2526.
[75]
Bubis, J.A.; Levitsky, L.I.; Ivanov, M.V.; Tarasova, I.A.; Gorshkov, M.V. Comparative evaluation of label-free quantification methods for shotgun proteomics. Rapid Commun. Mass Spectrom., 2017, 31(7), 606-612.
[76]
Bhosale, S.D.; Moulder, R.; Kouvonen, P.; Lahesmaa, R.; Goodlett, D.R. Mass Spectrometry-Based Serum Proteomics for Biomarker Discovery and Validation.In: Serum/Plasma Proteomics; Springer, 2017, pp. 451-466.
[77]
Neilson, K.A.; Ali, N.A.; Muralidharan, S.; Mirzaei, M.; Mariani, M.; Assadourian, G.; Lee, A.; van Sluyter, S.C.; Haynes, P.A. Less label, more free: approaches in label-free quantitative mass spectrometry. Proteomics, 2011, 11(4), 535-553.
[78]
Kolkman, A.; Olsthoorn, M.M.; Heeremans, C.E.; Heck, A.J.; Slijper, M. Comparative proteome analysis of Saccharomyces cerevisiae grown in chemostat cultures limited for glucose or ethanol. Mol. Cell. Proteomics, 2005, 4(1), 1-11.
[79]
Brilhante, R.S.N.; Oliveira, J.S.; Evangelista, A.J.J.; Serpa, R.; Silva, A.L.D.; Aguiar, F.R.M.; Pereira, V.S.; Castelo-Branco, D.; Pereira-Neto, W.A.; Cordeiro, R.A.; Sidrim, J.J.C.; Rocha, M.F.G. Candida tropicalis from veterinary and human sources shows similar in vitro hemolytic activity, antifungal biofilm susceptibility and pathogenesis against Caenorhabditis elegans. Vet. Microbiol., 2016, 192, 213-219.
[80]
Bader, G.D.; Heilbut, A.; Andrews, B.; Tyers, M.; Hughes, T.; Boone, C. Functional genomics and proteomics: charting a multidimensional map of the yeast cell. Trends Cell Biol., 2003, 13(7), 344-356.
[81]
Gavin, A.C.; Aloy, P.; Grandi, P.; Krause, R.; Boesche, M.; Marzioch, M.; Rau, C.; Jensen, L.J.; Bastuck, S.; Dumpelfeld, B.; Edelmann, A.; Heurtier, M.A.; Hoffman, V.; Hoefert, C.; Klein, K.; Hudak, M.; Michon, A.M.; Schelder, M.; Schirle, M.; Remor, M.; Rudi, T.; Hooper, S.; Bauer, A.; Bouwmeester, T.; Casari, G.; Drewes, G.; Neubauer, G.; Rick, J.M.; Kuster, B.; Bork, P.; Russell, R.B.; Superti-Furga, G. Proteome survey reveals modularity of the yeast cell machinery. Nature, 2006, 440(7084), 631-636.
[82]
Olshina, M.A.; Sharon, M. Mass spectrometry: A technique of many faces. Q. Rev. Biophys., 2016, 49, e18.
[83]
Graumann, J.; Dunipace, L.A.; Seol, J.H.; McDonald, W.H.; Yates, J.R. 3rd; Wold, B.J.; Deshaies, R.J. Applicability of tandem affinity purification MudPIT to pathway proteomics in yeast. Mol. Cell. Proteomics, 2004, 3(3), 226-237.
[84]
Selevsek, N.; Chang, C.Y.; Gillet, L.C.; Navarro, P.; Bernhardt, O.M.; Reiter, L.; Cheng, L.Y.; Vitek, O.; Aebersold, R. Reproducible and consistent quantification of the Saccharomyces cerevisiae proteome by SWATH-mass spectrometry. Mol. Cell. Proteomics, 2015, 14(3), 739-749.
[85]
Seidel, G.; Meierhofer, D.; Sen, N.E.; Guenther, A.; Krobitsch, S.; Auburger, G. Quantitative global proteomics of yeast PBP1 deletion mutants and their stress responses identifies glucose metabolism, mitochondrial, and stress granule changes. J. Proteome Res., 2017, 16(2), 504-515.
[86]
Addis, M.F.; Tanca, A.; Landolfo, S.; Abbondio, M.; Cutzu, R.; Biosa, G.; Pagnozzi, D.; Uzzau, S.; Mannazzu, I. Proteomic analysis of Rhodotorula mucilaginosa: Dealing with the issues of a non-conventional yeast. Yeast, 2016, 33(8), 433-449.
[87]
Group, B.D.W. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin. Pharmacol. Ther., 2001, 69(3), 89-95.
[89]
Smeekens, J.M.; Xiao, H.; Wu, R. Global analysis of secreted proteins and glycoproteins in Saccharomyces cerevisiae. J. Proteome Res., 2017, 16(2), 1039-1049.
[90]
Gil-Bona, A.; Monteoliva, L.; Gil, C. Global proteomic profiling of the secretome of Candida albicans ecm33 cell wall mutant reveals the involvement of Ecm33 in Sap2 secretion. J. Proteome Res., 2015, 14(10), 4270-4281.
[91]
Leger, T.; Garcia, C.; Ounissi, M.; Lelandais, G.; Camadro, J.M. The metacaspase (Mca1p) has a dual role in farnesol-induced apoptosis in Candida albicans. Mol. Cell. Proteomics, 2015, 14(1), 93-108.
[92]
Bozhkov, P.V.; Suarez, M.F.; Filonova, L.H.; Daniel, G.; Zamyatnin, A.A. Jr.; Rodriguez-Nieto, S.; Zhivotovsky, B.; Smertenko, A. Cysteine protease mcII-Pa executes programmed cell death during plant embryogenesis. Proc. Natl. Acad. Sci. USA, 2005, 102(40), 14463-14468.
[93]
Caetano-Anolles, G.; Kim, H.S.; Mittenthal, J.E. The origin of modern metabolic networks inferred from phylogenomic analysis of protein architecture. Proc. Natl. Acad. Sci. USA, 2007, 104(22), 9358-9363.
[94]
Lee, P.Y.; Gam, L.H.; Yong, V.C.; Rosli, R.; Ng, K.P.; Chong, P.P. Identification of immunogenic proteins of Candida parapsilosis by serological proteome analysis. J. Appl. Microbiol., 2014, 116(4), 999-1009.
[95]
Geddes, J.M.; Caza, M.; Croll, D.; Stoynov, N.; Foster, L.J.; Kronstad, J.W. Analysis of the protein kinase a-regulated proteome of cryptococcus neoformans identifies a role for the ubiquitin-proteasome pathway in capsule formation. MBio, 2016, 7(1), e01862-e15.
[96]
Coelho, C.; Bocca, A.L.; Casadevall, A. The tools for virulence of Cryptococcus neoformans. Adv. Appl. Microbiol., 2014, 87, 1-41.
[97]
Alspaugh, J.A. Virulence mechanisms and Cryptococcus neoformans pathogenesis. Fungal Genet. Biol., 2015, 78, 55-58.
[98]
Park, Y.D.; Shin, S.; Panepinto, J.; Ramos, J.; Qiu, J.; Frases, S.; Albuquerque, P.; Cordero, R.J.; Zhang, N.; Himmelreich, U.; Beenhouwer, D.; Bennett, J.E.; Casadevall, A.; Williamson, P.R. A role for LHC1 in higher order structure and complement binding of the Cryptococcus neoformans capsule. PLoS Pathog., 2014, 10(5), e1004037.
[99]
Geddes, J.M.; Croll, D.; Caza, M.; Stoynov, N.; Foster, L.J.; Kronstad, J.W. Secretome profiling of Cryptococcus neoformans reveals regulation of a subset of virulence-associated proteins and potential biomarkers by protein kinase A. BMC Microbiol., 2015, 15, 206.
[100]
Geddes-McAlister, J.; Shapiro, R.S. New pathogens, new tricks: Emerging, drug-resistant fungal pathogens and future prospects for antifungal therapeutics. Ann. N. Y. Acad. Sci., 2019, 1435(1), 57-78.
[101]
Vu, K.; Eigenheer, R.A.; Phinney, B.S.; Gelli, A. Cryptococcus neoformans promotes its transmigration into the central nervous system by inducing molecular and cellular changes in brain endothelial cells. Infect. Immun., 2013, 81(9), 3139-3147.
[102]
Marinach-Patrice, C.; Fekkar, A.; Atanasova, R.; Gomes, J.; Djamdjian, L.; Brossas, J.Y.; Meyer, I.; Buffet, P.; Snounou, G.; Datry, A.; Hennequin, C.; Golmard, J.L.; Mazier, D. Rapid species diagnosis for invasive candidiasis using mass spectrometry. PLoS One, 2010, 5(1), e8862.
[103]
Marinach, C.; Alanio, A.; Palous, M.; Kwasek, S.; Fekkar, A.; Brossas, J.Y.; Brun, S.; Snounou, G.; Hennequin, C.; Sanglard, D.; Datry, A.; Golmard, J.L.; Mazier, D. MALDI-TOF MS-based drug susceptibility testing of pathogens: The example of Candida albicans and fluconazole. Proteomics, 2009, 9(20), 4627-4631.
[104]
Posteraro, B.; De Carolis, E.; Vella, A.; Sanguinetti, M. MALDI-TOF mass spectrometry in the clinical mycology laboratory: Identification of fungi and beyond. Expert Rev. Proteomics, 2013, 10(2), 151-164.
[105]
Stefaniuk, E.; Baraniak, A.; Fortuna, M.; Hryniewicz, W. Usefulness of CHROMagar Candida medium, biochemical methods--API ID32C and VITEK 2 compact and two MALDI-TOF MS systems for Candida spp. identification. Pol. J. Microbiol., 2016, 65(1), 111-114.
[106]
Bader, O. MALDI-TOF-MS-based species identification and typing approaches in medical mycology. Proteomics, 2013, 13(5), 788-799.
[107]
Chandra, J.; Mukherjee, P.K. Candida biofilms: Development, architecture, and resistance. Microbiol. Spectr., 2015, 3(4)
[http://dx.doi.org/10.1128/microbiolspec.MB-0020-2015]
[http://dx.doi.org/10.1128/microbiolspec.MB-0020-2015]
[108]
Santi, L.; Beys-da-Silva, W.O.; Berger, M.; Calzolari, D.; Guimaraes, J.A.; Moresco, J.J.; Yates, J.R. 3rd Proteomic profile of Cryptococcus neoformans biofilm reveals changes in metabolic processes. J. Proteome Res., 2014, 13(3), 1545-1559.
[109]
Truong, T.; Zeng, G.; Qingsong, L.; Kwang, L.T.; Tong, C.; Chan, F.Y.; Wang, Y.; Seneviratne, C.J. Comparative ploidy proteomics of Candida albicans biofilms unraveled the role of the AHP1 gene in the biofilm persistence against amphotericin B. Mol. Cell. Proteomics, 2016, 15(11), 3488-3500.
[110]
Vitali, A.; Vavala, E.; Marzano, V.; Leone, C.; Castagnola, M.; Iavarone, F.; Angiolella, L. Cell wall composition and biofilm formation of azoles-susceptible and -resistant Candida glabrata strains. J. Chemother., 2017, 29(3), 164-172.
[111]
Winter, M.B.; Salcedo, E.C.; Lohse, M.B.; Hartooni, N.; Gulati, M.; Sanchez, H.; Takagi, J.; Hube, B.; Andes, D.R.; Johnson, A.D.; Craik, C.S.; Nobile, C.J. Global identification of biofilm-specific proteolysis in Candida albicans. MBio, 2016, 7(5), e01514-16.
[112]
Dack, R.E.; Black, G.W.; Koutsidis, G.; Usher, S.J. The effect of Maillard reaction products and yeast strain on the synthesis of key higher alcohols and esters in beer fermentations. Food Chem., 2017, 232, 595-601.
[113]
Ciani, M.; Comitini, F. Yeast interactions in multi-starter wine fermentation. Curr. Opin. Food Sci., 2015, 1, 1-6.
[114]
Legras, J.L.; Moreno-Garcia, J.; Zara, S.; Zara, G.; Garcia-Martinez, T.; Mauricio, J.C.; Mannazzu, I.; Coi, A.L.; Bou Zeidan, M.; Dequin, S.; Moreno, J.; Budroni, M. Flor yeast: New perspectives beyond wine aging. Front. Microbiol., 2016, 7, 503.
[115]
Matallana, E.; Aranda, A. Biotechnological impact of stress response on wine yeast. Lett. Appl. Microbiol., 2017, 64(2), 103-110.
[116]
Munoz-Bernal, E.; Deery, M.J.; Rodriguez, M.E.; Cantoral, J.M.; Howard, J.; Feret, R.; Natera, R.; Lilley, K.S.; Fernandez-Acero, F.J. Analysis of temperature-mediated changes in the wine yeast Saccharomyces bayanus var uvarum. An oenological study of how the protein content influences wine quality. Proteomics, 2016, 16(4), 576-592.
[117]
Moreno-García, J.; Mauricio, J.C.; Moreno, J.; García-Martínez, T. Stress responsive proteins of a flor yeast strain during the early stages of biofilm formation. Process Biochem., 2016, 51(5), 578-588.
[118]
Rice, C.J.; Pawlowsky, K.; Smart, C. Evaluating haze formation in flavoured lager beers using a range of forcing methods. J. Instit. Brewing., 2017, 123(3), 388-395.
[119]
Mostert, T.T.; Divol, B. Investigating the proteins released by yeasts in synthetic wine fermentations. Int. J. Food Microbiol., 2014, 171, 108-118.
[120]
Van Sluyter, S.C.; McRae, J.M.; Falconer, R.J.; Smith, P.A.; Bacic, A.; Waters, E.J.; Marangon, M. Wine protein haze: mechanisms of formation and advances in prevention. J. Agric. Food Chem., 2015, 63(16), 4020-4030.
[121]
Salvado, Z.; Chiva, R.; Rozes, N.; Cordero-Otero, R.; Guillamon, J.M. Functional analysis to identify genes in wine yeast adaptation to low-temperature fermentation. J. Appl. Microbiol., 2012, 113(1), 76-88.
[122]
Rodicio, R.; Heinisch, J.J. Carbohydrate Metabolism in Wine Yeasts.In: Biology of Microorganisms on Grapes, in Must and in Wine; König, H.; Unden, G.; Fröhlich, J., Eds.; Springer International Publishing: Cham, 2017, pp. 189-213.
[123]
García, J.M. Proteomic and metabolomic study of wine yeasts in free and immobilized formats, subjected to different stress conditions. Analytical thesis Universidad de Córdoba, Córdoba,, 2017.
[124]
Salvado, Z.; Chiva, R.; Rodriguez-Vargas, S.; Randez-Gil, F.; Mas, A.; Guillamon, J.M. Proteomic evolution of a wine yeast during the first hours of fermentation. FEMS Yeast Res., 2008, 8(7), 1137-1146.
[125]
Fasoli, E.; Righetti, P.G. Proteomics of fruits and beverages. Curr. Opin. Food Sci., 2015, 4, 76-85.
[126]
Picariello, G.; Mamone, G.; Cutignano, A.; Fontana, A.; Zurlo, L.; Addeo, F.; Ferranti, P. Proteomics, peptidomics, and immunogenic potential of wheat beer (Weissbier). J. Agric. Food Chem., 2015, 63(13), 3579-3586.
[127]
Kobi, D.; Zugmeyer, S.; Potier, S.; Jaquet-Gutfreund, L. Two-dimensional protein map of an “ale”-brewing yeast strain: proteome dynamics during fermentation. FEMS Yeast Res., 2004, 5(3), 213-230.
[128]
Xu, W.; Wang, J.; Li, Q. Comparative proteome and transcriptome analysis of lager brewer’s yeast in the autolysis process. FEMS Yeast Res., 2014, 14(8), 1273-1285.
[129]
Schulte, F.; Flaschel, E.; Niehaus, K. Proteome-based analysis of colloidal instability enables the detection of haze-active proteins in beer. J. Agric. Food Chem., 2016, 64(35), 6752-6761.
[130]
Blasco, L.; Vinas, M.; Villa, T.G. Proteins influencing foam formation in wine and beer: The role of yeast. Int. Microbiol., 2011, 14(2), 61-71.
[131]
Macedo, N.; Brigham, C.J. From beverages to biofuels: the journeys of ethanol-producing microorganisms. Int. J. Biotechnol. Wellness Industries., 2014, 3(3), 79-87.
[132]
Schulz, B.L.; Phung, T.K.; Bruschi, M.; Janusz, A.; Stewart, J.; Meehan, J.; Healy, P.; Nouwens, A.S.; Fox, G.P.; Vickers, C.E. Process proteomics of beer reveals a dynamic proteome with extensive modifications. J. Proteome Res., 2018, 17(4), 1647-1653.
[133]
Knight, M.J.; Bull, I.D.; Curnow, P. The yeast enzyme Eht1 is an octanoyl-CoA: Ethanol acyltransferase that also functions as a thioesterase. Yeast, 2014, 31(12), 463-474.
[134]
Petruzzi, L.; Rosaria Corbo, M.; Sinigaglia, M.; Bevilacqua, A. Brewer’s yeast in controlled and uncontrolled fermentations, with a focus on novel, nonconventional, and superior strains. Food Rev. Int., 2016, 32(4), 341-363.
[135]
Turvey, M.E.; Weiland, F.; Meneses, J.; Sterenberg, N.; Hoffmann, P. Identification of beer spoilage microorganisms using the MALDI Biotyper platform. Appl. Microbiol. Biotechnol., 2016, 100(6), 2761-2773.
[136]
De la Torre Gonzalez, F.J.; Gutierrez Avendano, D.O.; Gschaedler Mathis, A.C.; Kirchmayr, M.R. Evaluation of MALDI-TOF mass spectrometry for differentiation of Pichia kluyveri strains isolated from traditional fermentation processes. Rapid Commun. Mass Spectrom., 2018. [Epub ahead of print].
[137]
Lauterbach, A.; Wilde, C.; Bertrand, D.; Behr, J.; Vogel, R.F. Rating of the industrial application potential of yeast strains by molecular characterization. Eur. Food Res. Technol., 2018, 244(10), 1759-1772.
[138]
Farrell, A.E.; Plevin, R.J.; Turner, B.T.; Jones, A.D.; O’Hare, M.; Kammen, D.M. Ethanol can contribute to energy and environmental goals. Science, 2006, 311(5760), 506-508.
[139]
Sheng, J.; Feng, X. Metabolic engineering of yeast to produce fatty acid-derived biofuels: Bottlenecks and solutions. Front. Microbiol., 2015, 6, 554.
[140]
Basso, L.C.; de Amorim, H.V.; de Oliveira, A.J.; Lopes, M.L. Yeast selection for fuel ethanol production in Brazil. FEMS Yeast Res., 2008, 8(7), 1155-1163.
[141]
Tang, X.; Feng, H.; Zhang, J.; Chen, W.N. Comparative proteomics analysis of engineered Saccharomyces cerevisiae with enhanced biofuel precursor production. PLoS One, 2013, 8(12), e84661.
[142]
Campbell, K.; Xia, J.; Nielsen, J. The impact of systems biology on bioprocessing. Trends Biotechnol., 2017, 35(12), 1156-1168.
[143]
Wang, T-Y. Engineering yeast for cellulosic ethanol production. Austin Chem. Eng., 2015, 2(2), 1018.
[144]
Latimer, L.N.; Lee, M.E.; Medina-Cleghorn, D.; Kohnz, R.A.; Nomura, D.K.; Dueber, J.E. Employing a combinatorial expression approach to characterize xylose utilization in Saccharomyces cerevisiae. Metab. Eng., 2014, 25, 20-29.
[145]
Landels, A.; Evans, C.; Noirel, J.; Wright, P.C. Advances in proteomics for production strain analysis. Curr. Opin. Biotechnol., 2015, 35, 111-117.
[146]
Sato, T.K.; Tremaine, M.; Parreiras, L.S.; Hebert, A.S.; Myers, K.S.; Higbee, A.J.; Sardi, M.; McIlwain, S.J.; Ong, I.M.; Breuer, R.J.; Narasimhan, R.A.; McGee, M.A.; Dickinson, Q.; La Reau, A.; Xie, D.; Tian, M.; Piotrowski, J.S.; Reed, J.L.; Zhang, Y.; Coon, J.J.; Hittinger, C.T.; Gasch, A.P.; Landick, R. Correction: Directed evolution reveals unexpected epistatic interactions that alter metabolic regulation and enable anaerobic xylose use by Saccharomyces cerevisiae. PLoS Genet., 2016, 12(11), e1006447.
[147]
Sharma, N.K.; Behera, S.; Arora, R.; Kumar, S.; Sani, R.K. Xylose transport in yeast for lignocellulosic ethanol production: Current status. J. Biosci. Bioeng., 2018, 125(3), 259-267.
[148]
Liu, Z.H.; Qin, L.; Jin, M.J.; Pang, F.; Li, B.Z.; Kang, Y.; Dale, B.E.; Yuan, Y.J. Evaluation of storage methods for the conversion of corn stover biomass to sugars based on steam explosion pretreatment. Bioresour. Technol., 2013, 132, 5-15.
[149]
Qin, L.; Liu, Z.H.; Li, B.Z.; Dale, B.E.; Yuan, Y.J. Mass balance and transformation of corn stover by pretreatment with different dilute organic acids. Bioresour. Technol., 2012, 112, 319-326.
[150]
Lv, Y.J.; Wang, X.; Ma, Q.; Bai, X.; Li, B.Z.; Zhang, W.; Yuan, Y.J. Proteomic analysis reveals complex metabolic regulation in Saccharomyces cerevisiae cells against multiple inhibitors stress. Appl. Microbiol. Biotechnol., 2014, 98(5), 2207-2221.
[151]
Koppram, R.; Mapelli, V.; Albers, E.; Olsson, L. The presence of pretreated lignocellulosic solids from birch during Saccharomyces cerevisiae fermentations leads to increased tolerance to inhibitors--A proteomic study of the effects. PLoS One, 2016, 11(2), e0148635.
[152]
Zheng, D.; Zhang, K.; Gao, K.; Liu, Z.; Zhang, X.; Li, O.; Sun, J.; Du, F.; Sun, P.; Qu, A.; Wu, X. Construction of novel Saccharomyces cerevisiae strains for bioethanol active dry yeast (ADY) production. PLoS One, 2013, 8(12), e85022.
[153]
Shui, W.; Xiong, Y.; Xiao, W.; Qi, X.; Zhang, Y.; Lin, Y.; Guo, Y.; Zhang, Z.; Wang, Q.; Ma, Y. Understanding the mechanism of thermotolerance distinct from heat shock response through proteomic analysis of industrial strains of Saccharomyces cerevisiae. Mol. Cell. Proteomics, 2015, 14(7), 1885-1897.
[154]
Qi, F.; Zhao, X.; Kitahara, Y.; Li, T.; Ou, X.; Du, W.; Liu, D.; Huang, J. Integrative transcriptomic and proteomic analysis of the mutant lignocellulosic hydrolyzate-tolerant Rhodosporidium toruloides. Eng. Life Sci., 2017, 17(3), 249-261.
[155]
Ahmad, S.; Ahmad, A.; Neeves, K.B.; Hendry-Hofer, T.; Loader, J.E.; White, C.W.; Veress, L. In vitro cell culture model for toxic inhaled chemical testing. J. Vis. Exp., 2014, (87)
[http://dx.doi.org/10.3791/51539]
[http://dx.doi.org/10.3791/51539]
[156]
Paulo, J.A.; O’Connell, J.D.; Gaun, A.; Gygi, S.P. Proteome-wide quantitative multiplexed profiling of protein expression: carbon-source dependency in Saccharomyces cerevisiae. Mol. Biol. Cell, 2015, 26(22), 4063-4074.
[157]
Paulo, J.A.; O’Connell, J.D.; Everley, R.A.; O’Brien, J.; Gygi, M.A.; Gygi, S.P. Quantitative mass spectrometry-based multiplexing compares the abundance of 5000 S. cerevisiae proteins across 10 carbon sources. J. Proteomics, 2016, 148, 85-93.
[158]
Ghiaci, P.; Norbeck, J.; Larsson, C. Physiological adaptations of Saccharomyces cerevisiae evolved for improved butanol tolerance. Biotechnol. Biofuels, 2013, 6(1), 101.
[159]
Li, H.; Ma, M.L.; Luo, S.; Zhang, R.M.; Han, P.; Hu, W. Metabolic responses to ethanol in Saccharomyces cerevisiae using a gas chromatography tandem mass spectrometry-based metabolomics approach. Int. J. Biochem. Cell Biol., 2012, 44(7), 1087-1096.
[160]
Dong, S.J.; Yi, C.F.; Li, H. Changes of Saccharomyces cerevisiae cell membrane components and promotion to ethanol tolerance during the bioethanol fermentation. Int. J. Biochem. Cell Biol., 2015, 69, 196-203.
[161]
Vadia, S.; Tse, J.L.; Lucena, R.; Yang, Z.; Kellogg, D.R.; Wang, J.D.; Levin, P.A. Fatty acid availability sets cell envelope capacity and dictates microbial cell size. Curr. Biol., 2017, 27(12), 1757-1767.
[162]
Xu, K.; Yu, L.; Bai, W.; Xiao, B.; Liu, Y.; Lv, B.; Li, J.; Li, C. Construction of thermo-tolerant yeast based on an artificial protein quality control system (APQC) to improve the production of bio-ethanol. Chem. Eng. Sci., 2018, 177, 410-416.
[163]
Xiao, W.; Duan, X.; Lin, Y.; Cao, Q.; Li, S.; Guo, Y.; Gan, Y.; Qi, X.; Zhou, Y.; Guo, L.; Qin, P.; Wang, Q.; Shui, W. Distinct proteome remodeling of industrial Saccharomyces cerevisiae in response to prolonged thermal stress or transient heat shock. J. Proteome Res., 2018, 17(5), 1812-1825.
[164]
Hao, X.C.; Yang, X.S.; Wan, P.; Tian, S. Comparative proteomic analysis of a new adaptive Pichia stipitis strain to furfural, a lignocellulosic inhibitory compound. Biotechnol. Biofuels, 2013, 6(1), 34.
[165]
Passoth, V. Molecular Mechanisms in Yeast Carbon Metabolism: Bioethanol and Other Biofuels.In: Molecular Mechanisms in Yeast Carbon Metabolism; Piškur, J.; Compagno, C., Eds.; Springer Berlin Heidelberg: Berlin, Heidelberg, 2014, pp. 217-259.
[166]
Xu, J.; Liu, D. Exploitation of genus Rhodosporidium for microbial lipid production. World J. Microbiol. Biotechnol., 2017, 33(3), 54.
[167]
Liu, H.; Zhao, X.; Wang, F.; Li, Y.; Jiang, X.; Ye, M.; Zhao, Z.K.; Zou, H. Comparative proteomic analysis of Rhodosporidium toruloides during lipid accumulation. Yeast, 2009, 26(10), 553-566.
[168]
Liu, H.; Zhao, X.; Wang, F.; Jiang, X.; Zhang, S.; Ye, M.; Zhao, Z.K.; Zou, H. The proteome analysis of oleaginous yeast Lipomyces starkeyi. FEMS Yeast Res., 2011, 11(1), 42-51.
[169]
Capusoni, C.; Rodighiero, V.; Cucchetti, D.; Galafassi, S.; Bianchi, D.; Franzosi, G.; Compagno, C. Characterization of lipid accumulation and lipidome analysis in the oleaginous yeasts Rhodosporidium azoricum and Trichosporon oleaginosus. Bioresour. Technol., 2017, 238, 281-289.
[170]
Kumar, C.; Mann, M. Bioinformatics analysis of mass spectrometry-based proteomics data sets. FEBS Lett., 2009, 583(11), 1703-1712.
[171]
Cristoni, S.; Mazzuca, S. Bioinformatics Applied to Proteomics.In: Bioinformatics and Computational Modeling; Yang, N-S., Ed.; INTECH Open Access Publisher, 2011.
[173]
Vizcaino, J.A.; Csordas, A.; Del-Toro, N.; Dianes, J.A.; Griss, J.; Lavidas, I.; Mayer, G.; Perez-Riverol, Y.; Reisinger, F.; Ternent, T.; Xu, Q.W.; Wang, R.; Hermjakob, H. 2016 update of the PRIDE database and its related tools. Nucleic Acids Res., 2016, 44(22), 11033.
[174]
Desiere, F.; Deutsch, E.W.; King, N.L.; Nesvizhskii, A.I.; Mallick, P.; Eng, J.; Chen, S.; Eddes, J.; Loevenich, S.N.; Aebersold, R. The PeptideAtlas project. Nucleic Acids Res., 2006, 34(Database issue), D655-D658.
[175]
Colangelo, C.M.; Shifman, M.; Cheung, K.H.; Stone, K.L.; Carriero, N.J.; Gulcicek, E.E.; Lam, T.T.; Wu, T.; Bjornson, R.D.; Bruce, C.; Nairn, A.C.; Rinehart, J.; Miller, P.L.; Williams, K.R. YPED: An integrated bioinformatics suite and database for mass spectrometry-based proteomics research. Genomics Proteomics Bioinformatics, 2015, 13(1), 25-35.
[176]
Vialas, V.; Sun, Z.; Loureiro y Penha, C.V.; Carrascal, M.; Abian, J.; Monteoliva, L.; Deutsch, E.W.; Aebersold, R.; Moritz, R.L.; Gil, C. A Candida albicans PeptideAtlas. J. Proteomics, 2014, 97, 62-68.
[177]
Vialas, V.; Sun, Z.; Reales-Calderon, J.A.; Hernaez, M.L.; Casas, V.; Carrascal, M.; Abian, J.; Monteoliva, L.; Deutsch, E.W.; Moritz, R.L.; Gil, C. A comprehensive Candida albicans PeptideAtlas build enables deep proteome coverage. J. Proteomics, 2016, 131, 122-130.
[178]
Gnad, F.; de Godoy, L.M.; Cox, J.; Neuhauser, N.; Ren, S.; Olsen, J.V.; Mann, M. High-accuracy identification and bioinformatic analysis of in vivo protein phosphorylation sites in yeast. Proteomics, 2009, 9(20), 4642-4652.
[179]
Schmidt, A.; Forne, I.; Imhof, A. Bioinformatic analysis of proteomics data. BMC Systems. Biol., 2014, 8(2), S3.
[180]
Chen, C.; Huang, H.; Wu, C.H. Protein bioinformatics databases and resources. Methods Mol. Biol., 2017, 1558, 3-39.
[181]
Crowgey, E.L.; Matlock, A.; Venkatraman, V.; Fert-Bober, J.; Van Eyk, J.E. Mapping biological networks from quantitative data-independent acquisition mass spectrometry: Data to knowledge pipelines. Methods Mol. Biol., 2017, 1558, 395-413.
[182]
Pillich, R.T.; Chen, J.; Rynkov, V.; Welker, D.; Pratt, D. NDEx: A community resource for sharing and publishing of biological networks. Methods Mol. Biol., 2017, 1558, 271-301.
[183]
Kandasamy, K.; Mohan, S.S.; Raju, R.; Keerthikumar, S.; Kumar, G.S.; Venugopal, A.K.; Telikicherla, D.; Navarro, J.D.; Mathivanan, S.; Pecquet, C.; Gollapudi, S.K.; Tattikota, S.G.; Mohan, S.; Padhukasahasram, H.; Subbannayya, Y.; Goel, R.; Jacob, H.K.; Zhong, J.; Sekhar, R.; Nanjappa, V.; Balakrishnan, L.; Subbaiah, R.; Ramachandra, Y.L.; Rahiman, B.A.; Prasad, T.S.; Lin, J.X.; Houtman, J.C.; Desiderio, S.; Renauld, J.C.; Constantinescu, S.N.; Ohara, O.; Hirano, T.; Kubo, M.; Singh, S.; Khatri, P.; Draghici, S.; Bader, G.D.; Sander, C.; Leonard, W.J.; Pandey, A. NetPath: A public resource of curated signal transduction pathways. Genome Biol., 2010, 11(1), R3.
[184]
Schaefer, C.F.; Anthony, K.; Krupa, S.; Buchoff, J.; Day, M.; Hannay, T.; Buetow, K.H. PID: The pathway interaction database. Nucleic Acids Res., 2009, 37(Database issue), D674-D679.
[185]
Joshi-Tope, G.; Gillespie, M.; Vastrik, I.; D'Eustachio, P.; Schmidt, E.; de Bono, B.; Jassal, B.; Gopinath, G.R.; Wu, G.R.; Matthews, L.; Lewis, S.; Birney, E.; Stein, L. Reactome: A knowledgebase of biological pathways. Nucleic Acids Res., 2005, 33(suppl_1), D428-D432,
[186]
Fazekas, D.; Koltai, M.; Turei, D.; Modos, D.; Palfy, M.; Dul, Z.; Zsakai, L.; Szalay-Beko, M.; Lenti, K.; Farkas, I.J.; Vellai, T.; Csermely, P.; Korcsmaros, T. SignaLink 2 - a signaling pathway resource with multi-layered regulatory networks. BMC Syst. Biol., 2013, 7, 7.
[187]
Pico, A.R.; Kelder, T.; van Iersel, M.P.; Hanspers, K.; Conklin, B.R.; Evelo, C. WikiPathways: pathway editing for the people. PLoS Biol., 2008, 6(7), e184.
[188]
Caspi, R.; Altman, T.; Dreher, K.; Fulcher, C.A.; Subhraveti, P.; Keseler, I.M.; Kothari, A.; Krummenacker, M.; Latendresse, M.; Mueller, L.A.; Ong, Q.; Paley, S.; Pujar, A.; Shearer, A.G.; Travers, M.; Weerasinghe, D.; Zhang, P.; Karp, P.D. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res., 2012, 40(Database issue), D742-D753.
[189]
Kanehisa, M.; Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res., 2000, 28(1), 27-30.
[190]
Jewison, T.; Su, Y.; Disfany, F.M.; Liang, Y.; Knox, C.; Maciejewski, A.; Poelzer, J.; Huynh, J.; Zhou, Y.; Arndt, D.; Djoumbou, Y.; Liu, Y.; Deng, L.; Guo, A.C.; Han, B.; Pon, A.; Wilson, M.; Rafatnia, S.; Liu, P.; Wishart, D.S. SMPDB 2.0: Big improvements to the small molecule pathway database. Nucleic Acids Res., 2014, 42(Database issue), D478-D484.
[191]
Elkon, R.; Vesterman, R.; Amit, N.; Ulitsky, I.; Zohar, I.; Weisz, M.; Mass, G.; Orlev, N.; Sternberg, G.; Blekhman, R.; Assa, J.; Shiloh, Y.; Shamir, R. SPIKE--a database, visualization and analysis tool of cellular signaling pathways. BMC Bioinformatics, 2008, 9, 110.
[192]
King, Z.A.; Lu, J.; Drager, A.; Miller, P.; Federowicz, S.; Lerman, J.A.; Ebrahim, A.; Palsson, B.O.; Lewis, N.E. BiGG Models: A platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res., 2016, 44(D1), D515-D522.
[193]
Kramer, A.; Green, J.; Pollard, J., Jr; Tugendreich, S. Causal analysis approaches in ingenuity pathway analysis. Bioinformatics, 2014, 30(4), 523-530.
[194]
Nikitin, A.; Egorov, S.; Daraselia, N.; Mazo, I. Pathway studio--the analysis and navigation of molecular networks. Bioinformatics, 2003, 19(16), 2155-2157.
[195]
Szklarczyk, D.; Franceschini, A.; Wyder, S.; Forslund, K.; Heller, D.; Huerta-Cepas, J.; Simonovic, M.; Roth, A.; Santos, A.; Tsafou, K.P.; Kuhn, M.; Bork, P.; Jensen, L.J.; von Mering, C. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res., 2015, 43(Database issue), D447-D452.
Related Books
.jpeg)
1st Biotechnology World Congress - 2012
Decolorization by Thanatephorus Cucumeris Dec 1
Industrial Applications of Soil Microbes
The Future of Agriculture: IoT, AI and Blockchain Technology for Sustainable Farming
Handbook of Integrated Weed Management for Major Field Crops
Practical Biochemistry
Anticancer Drugs Sourced from Marine Life
Opportunities for Biotechnology Research and Entrepreneurship
Diseases of Ornamental, Aromatic and Medicinal Plants
Diabetes and Breast Cancer: An Analysis of Signaling Pathways
Article Metrics
24
4
1