Visualizing the next frontiers in wine yeast research.

Saccharomyces cerevisiae minimal genome pan-genome supernumerary neochromosome synthetic communities synthetic genome wine yeast

Journal

FEMS yeast research
ISSN: 1567-1364
Titre abrégé: FEMS Yeast Res
Pays: England
ID NLM: 101085384

Informations de publication

Date de publication:
11 03 2022
Historique:
received: 22 12 2021
revised: 05 02 2022
accepted: 14 02 2022
pubmed: 18 2 2022
medline: 6 5 2022
entrez: 17 2 2022
Statut: ppublish

Résumé

A range of game-changing biodigital and biodesign technologies are coming of age all around us, transforming our world in complex ways that are hard to predict. Not a day goes by without news of how data-centric engineering, algorithm-driven modelling, and biocyber technologies-including the convergence of artificial intelligence, machine learning, automated robotics, quantum computing, and genome editing-will change our world. If we are to be better at expecting the unexpected in the world of wine, we need to gain deeper insights into the potential and limitations of these technological developments and advances along with their promise and perils. This article anticipates how these fast-expanding bioinformational and biodesign toolkits might lead to the creation of synthetic organisms and model systems, and ultimately new understandings of biological complexities could be achieved. A total of four future frontiers in wine yeast research are discussed in this article: the construction of fully synthetic yeast genomes, including minimal genomes; supernumerary pan-genome neochromosomes; synthetic metagenomes; and synthetic yeast communities. These four concepts are at varying stages of development with plenty of technological pitfalls to overcome before such model chromosomes, genomes, strains, and yeast communities could illuminate some of the ill-understood aspects of yeast resilience, fermentation performance, flavour biosynthesis, and ecological interactions in vineyard and winery settings. From a winemaker's perspective, some of these ideas might be considered as far-fetched and, as such, tempting to ignore. However, synthetic biologists know that by exploring these futuristic concepts in the laboratory could well forge new research frontiers to deepen our understanding of the complexities of consistently producing fine wines with different fermentation processes from distinctive viticultural terroirs. As the saying goes in the disruptive technology industry, it take years to create an overnight success. The purpose of this article is neither to glorify any of these concepts as a panacea to all ills nor to crucify them as a danger to winemaking traditions. Rather, this article suggests that these proposed research endeavours deserve due consideration because they are likely to cast new light on the genetic blind spots of wine yeasts, and how they interact as communities in vineyards and wineries. Future-focussed research is, of course, designed to be subject to revision as new data and technologies become available. Successful dislodging of old paradigms with transformative innovations will require open-mindedness and pragmatism, not dogmatism-and this can make for a catch-22 situation in an archetypal traditional industry, such as the wine industry, with its rich territorial and socio-cultural connotations.

Identifiants

pubmed: 35175339
pii: 6530195
doi: 10.1093/femsyr/foac010
pmc: PMC8916113
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© The Author(s) 2022. Published by Oxford University Press on behalf of FEMS.

Références

Nature. 1996 Feb 15;379(6566):597-600
pubmed: 8628394
Genome Biol. 2020 Aug 13;21(1):205
pubmed: 32791980
Crit Rev Biotechnol. 2017 Feb;37(1):112-136
pubmed: 27535766
Synth Biol (Oxf). 2017 Jan 29;2(1):ysw002
pubmed: 32995501
Trends Biotechnol. 2019 Feb;37(2):181-197
pubmed: 30497870
Nat Rev Microbiol. 2018 Sep;16(9):567-576
pubmed: 29789680
Curr Opin Biotechnol. 2013 Apr;24(2):192-9
pubmed: 22947601
Trends Genet. 2019 Dec;35(12):956-957
pubmed: 31630852
Science. 2017 Mar 10;355(6329):1040-1044
pubmed: 28280199
Metab Eng. 2006 Jul;8(4):315-23
pubmed: 16621641
FEMS Yeast Res. 2020 Feb 1;20(1):
pubmed: 31830254
FEMS Yeast Res. 2008 Nov;8(7):1185-95
pubmed: 18778279
Genetics. 2015 Aug;200(4):1021-8
pubmed: 26272997
Bioeng Bugs. 2012 May-Jun;3(3):147-56
pubmed: 22572786
Int J Mol Sci. 2020 Sep 28;21(19):
pubmed: 32998303
Trends Biotechnol. 2016 May;34(5):371-381
pubmed: 26948437
Molecules. 2020 Jun 16;25(12):
pubmed: 32560189
FEMS Yeast Res. 2012 Feb;12(1):88-96
pubmed: 22136070
FEMS Yeast Res. 2011 Nov;11(7):540-51
pubmed: 22093681
Mol Ecol. 2015 Nov;24(21):5412-27
pubmed: 26248006
Trends Biotechnol. 2002 Oct;20(10):426-32
pubmed: 12220905
G3 (Bethesda). 2016 Apr 07;6(4):957-71
pubmed: 26869621
Nat Commun. 2018 May 22;9(1):1935
pubmed: 29789594
DNA Res. 2011 Dec;18(6):423-34
pubmed: 21900213
Microb Biotechnol. 2017 Mar;10(2):264-278
pubmed: 28083938
FEMS Yeast Res. 2018 Jun 1;18(4):
pubmed: 29648592
Proc Natl Acad Sci U S A. 2009 Sep 22;106(38):16333-8
pubmed: 19805302
Int J Food Microbiol. 2017 Jul 3;252:24-34
pubmed: 28458189
Mol Biol Evol. 2014 Apr;31(4):872-88
pubmed: 24425782
NPJ Biofilms Microbiomes. 2015 Jun 17;1:15007
pubmed: 28721231
Nature. 2018 Aug;560(7718):392-396
pubmed: 30069047
FEMS Yeast Res. 2015 May;15(3):
pubmed: 25725024
Proc Natl Acad Sci U S A. 2018 Mar 6;115(10):2526-2531
pubmed: 29463749
Genome Res. 2016 Jan;26(1):36-49
pubmed: 26566658
Nat Commun. 2018 May 22;9(1):1936
pubmed: 29789543
Curr Biol. 2019 May 20;29(10):R381-R393
pubmed: 31112692
Trends Genet. 2013 Apr;29(4):263-71
pubmed: 23218459
Nat Commun. 2018 May 22;9(1):1932
pubmed: 29789540
PLoS Genet. 2011 Feb 03;7(2):e1001287
pubmed: 21304888
FEMS Yeast Res. 2015 Nov;15(7):
pubmed: 26205244
EMBO Rep. 2010 Dec;11(12):914-20
pubmed: 21072064
Yeast. 2000 Jun 15;16(8):675-729
pubmed: 10861899
Genetics. 2015 Feb;199(2):281-91
pubmed: 25657346
Trends Biotechnol. 2022 Jan;40(1):124-135
pubmed: 34108075
Genetics. 2007 Dec;177(4):2293-307
pubmed: 18073433
Elife. 2015 Mar 25;4:
pubmed: 25807086
Nature. 2018 Apr;556(7701):339-344
pubmed: 29643504
Trends Biotechnol. 2008 Sep;26(9):483-9
pubmed: 18675483
Genes (Basel). 2018 Jul 26;9(8):
pubmed: 30050028
G3 (Bethesda). 2014 Mar 20;4(3):389-98
pubmed: 24374639
Nature. 2009 Mar 19;458(7236):337-41
pubmed: 19212322
Appl Microbiol Biotechnol. 2021 Apr;105(8):3027-3043
pubmed: 33834254
Nat Commun. 2018 May 22;9(1):1931
pubmed: 29789561
Nat Commun. 2018 May 22;9(1):1933
pubmed: 29789567
Science. 1996 Oct 25;274(5287):546, 563-7
pubmed: 8849441
Microb Cell Fact. 2016 Mar 04;15:49
pubmed: 26944880
Nature. 2018 Aug;560(7718):331-335
pubmed: 30069045
Science. 2017 Mar 10;355(6329):
pubmed: 28280154
Nat Commun. 2021 Jan 15;12(1):388
pubmed: 33452260
EMBO Rep. 2017 Nov;18(11):1875-1884
pubmed: 29061873
Nat Commun. 2021 Mar 11;12(1):1599
pubmed: 33707418
Cell. 2016 Sep 8;166(6):1397-1410.e16
pubmed: 27610566
Nat Commun. 2018 May 22;9(1):1930
pubmed: 29789541
Nat Commun. 2018 May 22;9(1):1934
pubmed: 29789590
Trends Biotechnol. 2002 Nov;20(11):472-8
pubmed: 12413822
EMBO Rep. 2020 Mar 4;21(3):e50036
pubmed: 32043291
Nat Commun. 2022 Jun 24;13(1):3628
pubmed: 35750675
Nat Commun. 2019 May 9;10(1):2040
pubmed: 31068573
Mol Biol Evol. 2018 Jul 1;35(7):1712-1727
pubmed: 29746697

Auteurs

I S Pretorius (IS)

ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, NSW 2109, Australia.

Articles similaires

Humans Artificial Intelligence COVID-19 SARS-CoV-2 Pandemics
Humans Algorithms Software Artificial Intelligence Computer Simulation
Humans Artificial Intelligence Neoplasms Prognosis Image Processing, Computer-Assisted

Classifications MeSH