CoChat
Sign Up
Cited by 12Biofuel production and bioconversionFungal and yeast genetics researchEnzyme Production and Characterization

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Proteome profiling of enriched membrane-associated proteins unraveled a novel sophorose and cello-oligosaccharide transporter in Trichoderma reesei

Open access
Nogueira, Karoline Maria Vieira, Mendes, Vanessa, Kamath, Karthik Shantharam, Cheruku, Anusha, Oshiquiri, Letícia Harumi, de Paula, Renato Graciano, Carraro, Claudia, Pedersoli, Wellington Ramos, Pereira, Lucas Matheus Soares, Vieira, Luiz Carlos, Steindorff, Andrei Stecca, Amirkhani, Ardeshir, McKay, Matthew J., Nevalainen, Helena, Molloy, Mark P., Silva, Roberto N.·2024·Microbial Cell Factories
DOI 10.1186/s12934-023-02279-9PMC PMC10790555

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

Background: Trichoderma reesei is an organism extensively used in the bioethanol industry, owing to its capability to produce enzymes capable of breaking down holocellulose into simple sugars. The uptake of carbohydrates generated from cellulose breakdown is crucial to induce the signaling cascade that triggers cellulase production. However, the sugar transporters involved in this process in T. reesei remain poorly identified and characterized. Results: To address this gap, this study used temporal membrane proteomics analysis to identify five known and nine putative sugar transporters that may be involved in cellulose degradation by T. reesei . Docking analysis pointed out potential ligands for the putative sugar transporter Tr44175. Further functional validation of this transporter was carried out in Saccharomyces cerevisiae . The results showed that Tr44175 transports a variety of sugar molecules, including cellobiose, cellotriose, cellotetraose, and sophorose. Conclusion: This study has unveiled a transporter Tr44175 capable of transporting cellobiose, cellotriose, cellotetraose, and sophorose. Our study represents the first inventory of T. reesei sugar transportome once exposed to cellulose, offering promising potential targets for strain engineering in the context of bioethanol production.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s12934-023-02279-9

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 54

  1. Bischof RH, Ramoni J, Seiboth B. Cellulases and beyond: the first 70 years of the enzyme producer Trichoderma reesei. Microb Cell Fact. 2016;15:1–13. · DOI
  2. Saini JK, Saini R, Tewari L. Lignocellulosic agriculture wastes as biomass feedstocks for second-generation bioethanol production: concepts and recent developments. 3 Biotech. 2015;5:337–53. · DOI
  3. Znameroski EA, Glass NL. Using a model filamentous fungus to unravel mechanisms of lignocellulose deconstruction. Biotechnol Biofuels. 2013;6:6. · DOI
  4. Zabed H, Sahu JN, Suely A, Boyce AN, Faruq G. Bioethanol production from renewable sources: current perspectives and technological progress. Renew Sustain Energy Rev. 2017;71:1–27. https://doi.org/10.1016/j.rser.2016.12.076. · DOI · Paper page
  5. Castro LDS, Antoniêto ACC, Pedersoli WR, Silva-Rocha R, Persinoti GF, Silva RN. Expression pattern of cellulolytic and xylanolytic genes regulated by transcriptional factors XYR1 and CRE1 are affected by carbon source in Trichoderma reesei. Gene Expr Patterns. 2014;14:88–95. · DOI
  6. Furukawa T, Shida Y, Kitagami N, Mori K, Kato M, Kobayashi T, et al. Identification of specific binding sites for XYR1, a transcriptional activator of cellulolytic and xylanolytic genes in Trichoderma reesei. Fungal Genet Biol. 2009;46:564–74. · DOI
  7. Nakari-Setälä T, Paloheimo M, Kallio J, Vehmaanperä J, Penttilä M, Saloheimo M. Genetic modification of carbon catabolite repression in Trichoderma reesei for improved protein production. Appl Environ Microbiol. 2009;75:4853–60. · DOI
  8. Nogueira KMV, Mendes V, Carraro CB, Taveira IC, Oshiquiri LH, Gupta VK, et al. Sugar transporters from industrial fungi: Key to improving second-generation ethanol production. Renew Sustain Energy Rev. 2020;131: 109991. · DOI
  9. Antoniêto ACC, dos Santos CL, Silva-Rocha R, Persinoti GF, Silva RN. Defining the genome-wide role of CRE1 during carbon catabolite repression in Trichoderma reesei using RNA-Seq analysis. Fungal Genet Biol. 2014;73:93–103. · DOI
  10. Zhang W, Kou Y, Xu J, Cao Y, Zhao G, Shao J, et al. Two major facilitator superfamily sugar transporters from Trichoderma reesei and their roles in induction of cellulase. J Biol Chem. 2013;288:32861–72. · DOI
  11. Amore A, Giacobbe S, Faraco V. Regulation of cellulase and hemicellulase gene expression in fungi. Curr Genomics. 2013;14:230–49. · DOI
  12. Nogueira KMV, De PRG, Cristina A, Antoniêto C, Reis TF, Carraro CB, et al. Biotechnology for biofuels characterization of a novel sugar transporter involved in sugarcane bagasse degradation in Trichoderma reesei. Biotechnol Biofuel. 2018;11:84. · DOI
  13. Daly P, van Munster JM, Raulo R, Archer DB. In: Silva RN, editor. Fungal Biothecnology for Biofuel Production. 2016. p. 82–127. · DOI
  14. dos Santos CL, de Paula RG, Antoniêto ACC, Persinoti GF, Silva-Rocha R, Silva RN. Understanding the role of the master regulator XYR1 in Trichoderma reesei by globaltranscriptional analysis. Front Microbiol. 2016;7:1–16.
  15. Chaudhary N, Kumari I, Sandhu P, Ahmed M, Akhter Y. Proteome scale census of major facilitator superfamily transporters in Trichoderma reesei using protein sequence and structure based classification enhanced ranking. Gene. 2016;585:166–76. · DOI
  16. Sloothaak J, Odoni DI, de Graaff LH, Martins Dos Santos VAP, Schaap PJ, Tamayo-Ramos JA. Aspergillus niger membrane-associated proteome analysis for the identification of glucose transporters. Biotechnol Biofuels. 2015;8:150. · DOI
  17. Havukainen S, Pujol-Giménez J, Valkonen M, Westerholm-Parvinen A, Hediger MA, Landowski CP. Electrophysiological characterization of a diverse group of sugar transporters from Trichoderma reesei. Sci Rep. 2021;11:14678. · DOI
  18. Havukainen S, Valkonen M, Koivuranta K, Landowski CP. Studies on sugar transporter CRT1 reveal new characteristics that are critical for cellulase induction in Trichoderma reesei. Biotechnol Biofuels. 2020;13:158. · DOI
  19. Zhang W, Cao Y, Gong J, Bao X, Chen G, Liu W. Identification of residues important for substrate uptake in a glucose transporter from the filamentous fungus Trichoderma reesei. Sci Rep. 2015;5:1–10.
  20. Huang Z, Chen X, Qin L, Wu H, Su X. A novel major facilitator transporter TrSTR1 is essential for pentose utilization and involved in xylanase induction in Trichoderma reesei. Biochem Biophys Res Commun. 2015;3045:1–7.
  21. Sloothaak J, Antonio J, Ramos T, Odoni DI, Laothanachareon T, Derntl C, et al. Biotechnology for biofuels identification and functional characterization of novel xylose transporters from the cell factories Aspergillus niger and Trichoderma reesei. Biotechnol Biofuels. 2016;9:1–15. · DOI
  22. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2^(-ΔΔCt) method. Methods. 2001;25(4):402–8. · DOI · Paper page
  23. Ries L, Pullan ST, Delmas S, Malla S, Blythe MJ, Archer DB. Genome-wide transcriptional response of Trichoderma reesei to lignocellulose using RNA sequencing and comparison with Aspergillus niger. BMC Genomics. 2013;14:541. · DOI
  24. Dos Santos CL, Pedersoli W, Antoniêto A, Steindorff A, Silva-Rocha R, Martinez-Rossi N, et al. Comparative metabolism of cellulose, sophorose and glucose in Trichoderma reesei using high-throughput genomic and proteomic analyses. Biotechnol Biofuels. 2014;7:41. · DOI
  25. Sternberg D, Mandels GR. Induction of cellulolytic enzymes in Trichoderma reesei by sophorose. J Bacteriol. 1979;139:761–9. · DOI
  26. Zhao J, Shi D, Yang S, Lin H, Chen H. Identification of an intracellular β-glucosidase in Aspergillus niger with transglycosylation activity. Appl Microbiol Biotechnol. 2020;104:8367–80. · DOI
  27. Bischof R, Fourtis L, Limbeck A, Gamauf C, Seiboth B, Kubicek CP. Comparative analysis of the Trichoderma reesei transcriptome during growth on the cellulase inducing substrates wheat straw and lactose. Biotechnol Biofuels. 2013;6:1–14. · DOI
  28. Borin GP, Sanchez CC, De Souza AP, De Santana ES, De Souza AT, Leme AFP, et al. Comparative secretome analysis of Trichoderma reesei and Aspergillus niger during growth on sugarcane biomass. PLoS ONE. 2015;10:1–20. · DOI
  29. de Paula RG, Antoniêto ACC, Ribeiro LFC, Carraro CB, Nogueira KMV, Lopes DCB, et al. New genomic approaches to enhance biomass degradation by the industrial fungus Trichoderma reesei. Int J Genomics. 2018;2018:1–17. · DOI
  30. Antoniêto ACC, Maués DB, Nogueira KMV, de Paula RG, Steindorff AS, Kennedy JF, Pandey A, Gupta VK, Silva RN. Engineering of holocellulase in biomass-degrading fungi for sustainable biofuel production. J Clean Prod. 2022;371(36):371.
  31. Silva-Rocha R, Castro LDS, Antoniêto ACC, Guazzaroni ME, Persinoti GF, Silva RN. Deciphering the cis-regulatory elements for XYR1 and CRE1 regulators in Trichoderma reesei. PLoS ONE. 2014;9: e99366. · DOI
  32. Campos Antoniêto AC, GracianodePaula R, dos Santos Castro L, Silva-Rocha R, Felix Persinoti G, Nascimento Silva R. Trichoderma reesei CRE1-mediated carbon catabolite repression in response to sophorose through RNA sequencing analysis. Curr Genomics. 2016;17:119–31. · DOI
  33. Carle-urioste JC, Escobar-vera J, El-gogary S, Henrique-silva F, Torigoi E, Crivellaro O, et al. Cellulase induction in Trichoderma reesei by cellulose requires its own basal expression. J Biol Chem. 1997;272:10169–74. · DOI
  34. Casa-Villegas M, Polaina J, Marín-Navarro J. Cellobiose fermentation by Saccharomyces cerevisiae: comparative analysis of intra versus extracellular sugar hydrolysis. Process Biochem. 2018;75:59–67. · DOI
  35. Yan S, Xu Y, Yu X-W. Role of cellulose response transporter-like protein CRT2 in cellulase induction in Trichoderma reesei. Biotechnol Biofuels Bioprod. 2023;16:118. · DOI
  36. Nordberg H, Cantor M, Dusheyko S, Hua S, Poliakov A, Shabalov I, et al. The genome portal of the department of energy joint genome institute: 2014 updates. Nucleic Acids Res. 2014;42:D26. · DOI
  37. Grigoriev IV, Nordberg H, Shabalov I, Aerts A, Cantor M, Goodstein D, et al. The genome portal of the department of energy joint genome institute. Nucleic Acids Res. 2012;40:D26. · DOI
  38. Katoh K, Rozewicki J, Yamada KD. MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization. Brief Bioinform. 2018;20:1160–6. · DOI · Paper page
  39. Price MN, Dehal PS, Arkin AP. FastTree 2 - approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5: e9490. · DOI · Paper page
  40. Letunic I, Bork P. Interactive tree of life (iTOL) v5: An online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49:W293–6. · DOI
  41. Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. ColabFold: making protein folding accessible to all. Nat Methods. 2022;19:679–82. · DOI · Paper page
  42. Laskowski RA, MacArthur MW, Moss DS, Thornton JM. PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr. 1993;26:283–91. · DOI · Paper page
  43. Studer G, Biasini M, Schwede T. Assessing the local structural quality of transmembrane protein models using statistical potentials (QMEANBrane). Bioinformatics. 2014;30:505–11. · DOI
  44. Colovos C, Yeates TO. Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci. 1993;2:1511–9. · DOI
  45. Wiederstein M, Sippl MJ. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 2007;35:407–10. · DOI · Paper page
  46. Allouche A. Software news and updates gabedit — a graphical user interface for computational chemistry softwares. J Comput Chem. 2012;32:174–82. · DOI
  47. Eberhardt J, Santos-Martins D, Tillack AF, Forli S. AutoDock Vina 1.2.0: new docking methods, expanded force field, and python bindings. J Chem Inf Model. 2021;61:3891–8. · DOI
  48. Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, et al. PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Res. 2021;49:D1388–95. · DOI · Paper page
  49. Yang JM, Chen CC. GEMDOCK: a generic evolutionary method for molecular docking. Proteins Struct Funct Genet. 2004;55:288–304. · DOI
  50. Laskowski RA, Swindells MB. LigPlot+: multiple ligand-protein interaction diagrams for drug discovery. J Chem Inf Model. 2011;51:2778–86. · DOI
  51. Lomize AL, Todd SC, Pogozheva ID. Spatial arrangement of proteins in planar and curved membranes by PPM 3.0. Protein Sci. 2022;31:209–20. · DOI
  52. Hallgren J, Tsirigos KD, Damgaard Pedersen M, Juan J, Armenteros A, Marcatili P, et al. DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks. https://doi.org/10.1101/2022.04.08.487609 · DOI
  53. Kyte J, Doolittle RF, Diego S, Jolla L. A simple method for displaying the hydropathic character of a protein. J Mol Biol. 1982;157:105–32. · DOI
  54. Gietz RD, Schiestl RH. High-efficiency yeast transformation using the LiAc/SS carrier DNA/PEG method. Nat Protoc. 2008;2:31–5. · DOI

Metadata

Authors
Nogueira, K. M. V., Mendes, V., Kamath, K. S., Cheruku, A., Oshiquiri, L. H., de Paula, R. G., Carraro, C., Pedersoli, W. R., Pereira, L. M. S., Vieira, L. C., Steindorff, A. S., Amirkhani, A., McKay, M. J., Nevalainen, H., Molloy, M. P., & Silva, R. N.
Affiliations
  • Universidade de São Paulo (BR)
  • Macquarie University (AU)
  • Universidade Federal do Espírito Santo (BR)
  • Lawrence Berkeley National Laboratory (US)
Corresponding author
Silva, Roberto N.
ORCID
Venue
Microbial Cell Factories
Publisher
Springer Science and Business Media LLC
Published
2024-01-16
Volume
23
Issue
1
Pages
22-22
ISSN
1475-2859 (ISSN-L)
DOI
10.1186/s12934-023-02279-9
PMC
PMC10790555
OpenAlex
W4390903271
Cited by
12
References
1
Citations by year
2026: 5 · 2025: 5 · 2024: 2
FWCI
1.31
Type
journal article
Language
en
Open access
Licence
Locations
  • Microbial Cell Factories (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Fundação de Amparo à Pesquisa do Estado de São Paulo (2018/25898-3, 2019/11655-4)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Biofuel production and bioconversion
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fungal and yeast genetics research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Biotechnology › Enzyme Production and Characterization
Keywords
Trichoderma reesei, sugar transporters, cellulose degradation, membrane proteomics, cellobiose, cellotriose, cellotetraose, cellulase induction, bioethanol production
MeSH terms
Sugars, Cellobiose, Cellulase, Cellulose, Glucans, Hypocreales, Membrane Proteins, Oligosaccharides, Saccharomyces cerevisiae, Trichoderma, Proteome, Membrane Transport Proteins

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review