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Mejía-Manzano LA, Ortiz-Alcaráz CI, Parra Daza LE, Suarez Medina L, Vargas-Cortez T, Fernández-Niño M, González Barrios AF, González-Valdez J. Saccharomyces cerevisiae biofactory to produce naringenin using a systems biology approach and a bicistronic vector expression strategy in flavonoid production. Microbiol Spectr 2024; 12:e0337423. [PMID: 38088543 PMCID: PMC10871697 DOI: 10.1128/spectrum.03374-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 11/21/2023] [Indexed: 01/13/2024] Open
Abstract
IMPORTANCE Flavonoids are a group of compounds generally produced by plants with proven biological activity, which have recently beeen recommended for the treatment and prevention of diseases and ailments with diverse causes. In this study, naringenin was produced in adequate amounts in yeast after in silico design. The four genes of the involved enzymes from several organisms (bacteria and plants) were multi-expressed in two vectors carrying each two genes linked by a short viral peptide sequence. The batch kinetic behavior of the product, substrate, and biomass was described at lab scale. The engineered strain might be used in a more affordable and viable bioprocess for industrial naringenin procurement.
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Affiliation(s)
| | | | - Laura E. Parra Daza
- School of Engineering and Science, Tecnologico de Monterrey, Monterrey, Nuevo León, Mexico
- Department of Chemical and Food Engineering, Grupo de Diseño de Productos y Procesos (GDPP), Universidad de los Andes, Bogotá, Colombia
| | - Lina Suarez Medina
- Department of Chemical and Food Engineering, Grupo de Diseño de Productos y Procesos (GDPP), Universidad de los Andes, Bogotá, Colombia
| | - Teresa Vargas-Cortez
- School of Engineering and Science, Tecnologico de Monterrey, Monterrey, Nuevo León, Mexico
| | - Miguel Fernández-Niño
- Department of Chemical and Food Engineering, Grupo de Diseño de Productos y Procesos (GDPP), Universidad de los Andes, Bogotá, Colombia
- Department of Bioorganic Chemistry, Leibniz-Institute of Plant Biochemistry, Halle, Germany
| | - Andrés Fernando González Barrios
- Department of Chemical and Food Engineering, Grupo de Diseño de Productos y Procesos (GDPP), Universidad de los Andes, Bogotá, Colombia
| | - José González-Valdez
- School of Engineering and Science, Tecnologico de Monterrey, Monterrey, Nuevo León, Mexico
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Performance and Application of 16S rRNA Gene Cycle Sequencing for Routine Identification of Bacteria in the Clinical Microbiology Laboratory. Clin Microbiol Rev 2020; 33:33/4/e00053-19. [PMID: 32907806 DOI: 10.1128/cmr.00053-19] [Citation(s) in RCA: 127] [Impact Index Per Article: 31.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
This review provides a state-of-the-art description of the performance of Sanger cycle sequencing of the 16S rRNA gene for routine identification of bacteria in the clinical microbiology laboratory. A detailed description of the technology and current methodology is outlined with a major focus on proper data analyses and interpretation of sequences. The remainder of the article is focused on a comprehensive evaluation of the application of this method for identification of bacterial pathogens based on analyses of 16S multialignment sequences. In particular, the existing limitations of similarity within 16S for genus- and species-level differentiation of clinically relevant pathogens and the lack of sequence data currently available in public databases is highlighted. A multiyear experience is described of a large regional clinical microbiology service with direct 16S broad-range PCR followed by cycle sequencing for direct detection of pathogens in appropriate clinical samples. The ability of proteomics (matrix-assisted desorption ionization-time of flight) versus 16S sequencing for bacterial identification and genotyping is compared. Finally, the potential for whole-genome analysis by next-generation sequencing (NGS) to replace 16S sequencing for routine diagnostic use is presented for several applications, including the barriers that must be overcome to fully implement newer genomic methods in clinical microbiology. A future challenge for large clinical, reference, and research laboratories, as well as for industry, will be the translation of vast amounts of accrued NGS microbial data into convenient algorithm testing schemes for various applications (i.e., microbial identification, genotyping, and metagenomics and microbiome analyses) so that clinically relevant information can be reported to physicians in a format that is understood and actionable. These challenges will not be faced by clinical microbiologists alone but by every scientist involved in a domain where natural diversity of genes and gene sequences plays a critical role in disease, health, pathogenicity, epidemiology, and other aspects of life-forms. Overcoming these challenges will require global multidisciplinary efforts across fields that do not normally interact with the clinical arena to make vast amounts of sequencing data clinically interpretable and actionable at the bedside.
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Chen PW, Theisen MK, Liao JC. Metabolic systems modeling for cell factories improvement. Curr Opin Biotechnol 2017; 46:114-119. [PMID: 28388485 DOI: 10.1016/j.copbio.2017.02.005] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Revised: 02/10/2017] [Accepted: 02/13/2017] [Indexed: 12/23/2022]
Abstract
Techniques for modeling microbial bioproduction systems have evolved over many decades. Here, we survey recent literature and focus on modeling approaches for improving bioproduction. These techniques from systems biology are based on different methodologies, starting from stoichiometry only to various stoichiometry with kinetics approaches that address different issues in metabolic systems. Techniques to overcome unknown kinetic parameters using random sampling have emerged to address meaningful questions. Among those questions, pathway robustness seems to be an important issue for metabolic engineering. We also discuss the increasing significance of databases in biology and their potential impact for biotechnology.
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Affiliation(s)
- Po-Wei Chen
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, United States
| | - Matthew K Theisen
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, United States
| | - James C Liao
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, United States; Academia Sinica, Taipei 11529, Taiwan.
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Davis JJ, Gerdes S, Olsen GJ, Olson R, Pusch GD, Shukla M, Vonstein V, Wattam AR, Yoo H. PATtyFams: Protein Families for the Microbial Genomes in the PATRIC Database. Front Microbiol 2016; 7:118. [PMID: 26903996 PMCID: PMC4744870 DOI: 10.3389/fmicb.2016.00118] [Citation(s) in RCA: 118] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2015] [Accepted: 01/22/2016] [Indexed: 01/12/2023] Open
Abstract
The ability to build accurate protein families is a fundamental operation in bioinformatics that influences comparative analyses, genome annotation, and metabolic modeling. For several years we have been maintaining protein families for all microbial genomes in the PATRIC database (Pathosystems Resource Integration Center, patricbrc.org) in order to drive many of the comparative analysis tools that are available through the PATRIC website. However, due to the burgeoning number of genomes, traditional approaches for generating protein families are becoming prohibitive. In this report, we describe a new approach for generating protein families, which we call PATtyFams. This method uses the k-mer-based function assignments available through RAST (Rapid Annotation using Subsystem Technology) to rapidly guide family formation, and then differentiates the function-based groups into families using a Markov Cluster algorithm (MCL). This new approach for generating protein families is rapid, scalable and has properties that are consistent with alignment-based methods.
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Affiliation(s)
- James J Davis
- Computation Institute, University of ChicagoChicago, IL, USA; Computing, Environment and Life Sciences, Argonne National LaboratoryArgonne IL, USA
| | - Svetlana Gerdes
- Computing, Environment and Life Sciences, Argonne National LaboratoryArgonne IL, USA; Fellowship for Interpretation of GenomesBurr Ridge, IL, USA
| | - Gary J Olsen
- Department of Microbiology and Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign Urbana, IL, USA
| | - Robert Olson
- Computation Institute, University of ChicagoChicago, IL, USA; Mathematics and Computer Science Division, Argonne National LaboratoryArgonne, IL, USA
| | - Gordon D Pusch
- Computing, Environment and Life Sciences, Argonne National LaboratoryArgonne IL, USA; Fellowship for Interpretation of GenomesBurr Ridge, IL, USA
| | - Maulik Shukla
- Computation Institute, University of ChicagoChicago, IL, USA; Computing, Environment and Life Sciences, Argonne National LaboratoryArgonne IL, USA
| | - Veronika Vonstein
- Computing, Environment and Life Sciences, Argonne National LaboratoryArgonne IL, USA; Fellowship for Interpretation of GenomesBurr Ridge, IL, USA
| | - Alice R Wattam
- Virginia Bioinformatics Institute, Virginia Tech University Blacksburg, VA, USA
| | - Hyunseung Yoo
- Computation Institute, University of ChicagoChicago, IL, USA; Computing, Environment and Life Sciences, Argonne National LaboratoryArgonne IL, USA
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