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Rosado-Ramos R, Poças GM, Marques D, Foito A, M Sevillano D, Lopes-da-Silva M, Gonçalves LG, Menezes R, Ottens M, Stewart D, Ibáñez de Opakua A, Zweckstetter M, Seabra MC, Mendes CS, Outeiro TF, Domingos PM, Santos CN. Genipin prevents alpha-synuclein aggregation and toxicity by affecting endocytosis, metabolism and lipid storage. Nat Commun 2023; 14:1918. [PMID: 37024503 PMCID: PMC10079842 DOI: 10.1038/s41467-023-37561-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 03/20/2023] [Indexed: 04/08/2023] Open
Abstract
Parkinson's Disease (PD) is a common neurodegenerative disorder affecting millions of people worldwide for which there are only symptomatic therapies. Small molecules able to target key pathological processes in PD have emerged as interesting options for modifying disease progression. We have previously shown that a (poly)phenol-enriched fraction (PEF) of Corema album L. leaf extract modulates central events in PD pathogenesis, namely α-synuclein (αSyn) toxicity, aggregation and clearance. PEF was now subjected to a bio-guided fractionation with the aim of identifying the critical bioactive compound. We identified genipin, an iridoid, which relieves αSyn toxicity and aggregation. Furthermore, genipin promotes metabolic alterations and modulates lipid storage and endocytosis. Importantly, genipin was able to prevent the motor deficits caused by the overexpression of αSyn in a Drosophila melanogaster model of PD. These findings widens the possibility for the exploitation of genipin for PD therapeutics.
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Affiliation(s)
- Rita Rosado-Ramos
- iBET, Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB NOVA), Oeiras, Portugal
- iNOVA4Health, NOVA Medical School Faculdade de Ciências Médicas, NMS FCM, Universidade Nova de Lisboa, Lisboa, Portugal
| | - Gonçalo M Poças
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB NOVA), Oeiras, Portugal
| | - Daniela Marques
- iNOVA4Health, NOVA Medical School Faculdade de Ciências Médicas, NMS FCM, Universidade Nova de Lisboa, Lisboa, Portugal
| | - Alexandre Foito
- Environmental and Biochemical Sciences, The James Hutton Institute, DD2 5DA, Dundee, Scotland
| | - David M Sevillano
- Department of Biotechnology, Delft University of Technology, Delft, Netherlands
| | - Mafalda Lopes-da-Silva
- iNOVA4Health, NOVA Medical School Faculdade de Ciências Médicas, NMS FCM, Universidade Nova de Lisboa, Lisboa, Portugal
| | - Luís G Gonçalves
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB NOVA), Oeiras, Portugal
| | - Regina Menezes
- iBET, Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
- iNOVA4Health, NOVA Medical School Faculdade de Ciências Médicas, NMS FCM, Universidade Nova de Lisboa, Lisboa, Portugal
- CBIOS - Universidade Lusófona's Research Center for Biosciences & Health Technologies, Campo Grande 376, 1749-024, Lisboa, Portugal
| | - Marcel Ottens
- Department of Biotechnology, Delft University of Technology, Delft, Netherlands
| | - Derek Stewart
- Environmental and Biochemical Sciences, The James Hutton Institute, DD2 5DA, Dundee, Scotland
| | | | - Markus Zweckstetter
- German Center for Neurodegenerative Diseases (DZNE), 37075, Göttingen, Germany
- Max Planck Institute for Multidisciplinary Sciences, Department of NMR-based Structural Biology, Am Fassberg 11, 37077, Göttingen, Germany
| | - Miguel C Seabra
- iNOVA4Health, NOVA Medical School Faculdade de Ciências Médicas, NMS FCM, Universidade Nova de Lisboa, Lisboa, Portugal
| | - César S Mendes
- iNOVA4Health, NOVA Medical School Faculdade de Ciências Médicas, NMS FCM, Universidade Nova de Lisboa, Lisboa, Portugal
| | - Tiago Fleming Outeiro
- German Center for Neurodegenerative Diseases (DZNE), 37075, Göttingen, Germany
- Department of Experimental Neurodegeneration, Center for Biostructural Imaging of Neurodegeneration, University Medical Center Göttingen, Göttingen, Germany
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, NE2 4HH, UK
- Scientific employee with an honorary contract at German Center for Neurodegenerative Diseases (DZNE), 37075, Göttingen, Germany
- Max Planck Institute for Multidisciplinary Sciences, 37075, Göttingen, Germany
| | - Pedro M Domingos
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB NOVA), Oeiras, Portugal
| | - Cláudia N Santos
- iBET, Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal.
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB NOVA), Oeiras, Portugal.
- iNOVA4Health, NOVA Medical School Faculdade de Ciências Médicas, NMS FCM, Universidade Nova de Lisboa, Lisboa, Portugal.
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2
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Developing New Tools to Fight Human Pathogens: A Journey through the Advances in RNA Technologies. Microorganisms 2022; 10:microorganisms10112303. [DOI: 10.3390/microorganisms10112303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 11/12/2022] [Accepted: 11/15/2022] [Indexed: 11/22/2022] Open
Abstract
A long scientific journey has led to prominent technological advances in the RNA field, and several new types of molecules have been discovered, from non-coding RNAs (ncRNAs) to riboswitches, small interfering RNAs (siRNAs) and CRISPR systems. Such findings, together with the recognition of the advantages of RNA in terms of its functional performance, have attracted the attention of synthetic biologists to create potent RNA-based tools for biotechnological and medical applications. In this review, we have gathered the knowledge on the connection between RNA metabolism and pathogenesis in Gram-positive and Gram-negative bacteria. We further discuss how RNA techniques have contributed to the building of this knowledge and the development of new tools in synthetic biology for the diagnosis and treatment of diseases caused by pathogenic microorganisms. Infectious diseases are still a world-leading cause of death and morbidity, and RNA-based therapeutics have arisen as an alternative way to achieve success. There are still obstacles to overcome in its application, but much progress has been made in a fast and effective manner, paving the way for the solid establishment of RNA-based therapies in the future.
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Garcia BJ, Urrutia J, Zheng G, Becker D, Corbet C, Maschhoff P, Cristofaro A, Gaffney N, Vaughn M, Saxena U, Chen YP, Gordon DB, Eslami M. A toolkit for enhanced reproducibility of RNASeq analysis for synthetic biologists. SYNTHETIC BIOLOGY (OXFORD, ENGLAND) 2022; 7:ysac012. [PMID: 36035514 PMCID: PMC9408027 DOI: 10.1093/synbio/ysac012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 06/17/2022] [Accepted: 08/22/2022] [Indexed: 11/13/2022]
Abstract
Sequencing technologies, in particular RNASeq, have become critical tools in the design, build, test and learn cycle of synthetic biology. They provide a better understanding of synthetic designs, and they help identify ways to improve and select designs. While these data are beneficial to design, their collection and analysis is a complex, multistep process that has implications on both discovery and reproducibility of experiments. Additionally, tool parameters, experimental metadata, normalization of data and standardization of file formats present challenges that are computationally intensive. This calls for high-throughput pipelines expressly designed to handle the combinatorial and longitudinal nature of synthetic biology. In this paper, we present a pipeline to maximize the analytical reproducibility of RNASeq for synthetic biologists. We also explore the impact of reproducibility on the validation of machine learning models. We present the design of a pipeline that combines traditional RNASeq data processing tools with structured metadata tracking to allow for the exploration of the combinatorial design in a high-throughput and reproducible manner. We then demonstrate utility via two different experiments: a control comparison experiment and a machine learning model experiment. The first experiment compares datasets collected from identical biological controls across multiple days for two different organisms. It shows that a reproducible experimental protocol for one organism does not guarantee reproducibility in another. The second experiment quantifies the differences in experimental runs from multiple perspectives. It shows that the lack of reproducibility from these different perspectives can place an upper bound on the validation of machine learning models trained on RNASeq data.
Graphical Abstract
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Affiliation(s)
- Benjamin J Garcia
- Department of Biological Engineering, Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Joshua Urrutia
- Texas Advanced Computing Center, University of Texas at Austin, Austin, TX, USA
| | | | | | | | | | - Alexander Cristofaro
- Department of Biological Engineering, Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Niall Gaffney
- Texas Advanced Computing Center, University of Texas at Austin, Austin, TX, USA
| | - Matthew Vaughn
- Texas Advanced Computing Center, University of Texas at Austin, Austin, TX, USA
| | - Uma Saxena
- Department of Biological Engineering, Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - D Benjamin Gordon
- Department of Biological Engineering, Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA
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Lim HG, Rychel K, Sastry AV, Bentley GJ, Mueller J, Schindel HS, Larsen PE, Laible PD, Guss AM, Niu W, Johnson CW, Beckham GT, Feist AM, Palsson BO. Machine-learning from Pseudomonas putida KT2440 transcriptomes reveals its transcriptional regulatory network. Metab Eng 2022; 72:297-310. [PMID: 35489688 DOI: 10.1016/j.ymben.2022.04.004] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 03/23/2022] [Accepted: 04/23/2022] [Indexed: 11/17/2022]
Abstract
Bacterial gene expression is orchestrated by numerous transcription factors (TFs). Elucidating how gene expression is regulated is fundamental to understanding bacterial physiology and engineering it for practical use. In this study, a machine-learning approach was applied to uncover the genome-scale transcriptional regulatory network (TRN) in Pseudomonas putida KT2440, an important organism for bioproduction. We performed independent component analysis of a compendium of 321 high-quality gene expression profiles, which were previously published or newly generated in this study. We identified 84 groups of independently modulated genes (iModulons) that explain 75.7% of the total variance in the compendium. With these iModulons, we (i) expand our understanding of the regulatory functions of 39 iModulon associated TFs (e.g., HexR, Zur) by systematic comparison with 1993 previously reported TF-gene interactions; (ii) outline transcriptional changes after the transition from the exponential growth to stationary phases; (iii) capture group of genes required for utilizing diverse carbon sources and increased stationary response with slower growth rates; (iv) unveil multiple evolutionary strategies of transcriptome reallocation to achieve fast growth rates; and (v) define an osmotic stimulon, which includes the Type VI secretion system, as coordination of multiple iModulon activity changes. Taken together, this study provides the first quantitative genome-scale TRN for P. putida KT2440 and a basis for a comprehensive understanding of its complex transcriptome changes in a variety of physiological states.
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Affiliation(s)
- Hyun Gyu Lim
- Department of Bioengineering, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA; Joint BioEnergy Institute, 5885 Hollis Street, 4th Floor, Emeryville, CA, 94608, USA
| | - Kevin Rychel
- Department of Bioengineering, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA
| | - Anand V Sastry
- Department of Bioengineering, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA
| | - Gayle J Bentley
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO, 80401, USA; Agile BioFoundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Emeryville, CA, 94720, USA
| | - Joshua Mueller
- Department of Chemical & Biomolecular Engineering, University of Nebraska-Lincoln, 1400 R St, Lincoln, NE, 68588, USA
| | - Heidi S Schindel
- Biosciences Division, Oak Ridge National Laboratory, 5200 Bethel Valley Rd, Oak Ridge, TN, 37830, USA
| | - Peter E Larsen
- Biosciences Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL, 60539, USA
| | - Philip D Laible
- Biosciences Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL, 60539, USA
| | - Adam M Guss
- Agile BioFoundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Emeryville, CA, 94720, USA; Biosciences Division, Oak Ridge National Laboratory, 5200 Bethel Valley Rd, Oak Ridge, TN, 37830, USA
| | - Wei Niu
- Department of Chemical & Biomolecular Engineering, University of Nebraska-Lincoln, 1400 R St, Lincoln, NE, 68588, USA
| | - Christopher W Johnson
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO, 80401, USA; Agile BioFoundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Emeryville, CA, 94720, USA
| | - Gregg T Beckham
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO, 80401, USA; Agile BioFoundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Emeryville, CA, 94720, USA
| | - Adam M Feist
- Department of Bioengineering, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA; Joint BioEnergy Institute, 5885 Hollis Street, 4th Floor, Emeryville, CA, 94608, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs, Lyngby, Denmark
| | - Bernhard O Palsson
- Department of Bioengineering, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA; Joint BioEnergy Institute, 5885 Hollis Street, 4th Floor, Emeryville, CA, 94608, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs, Lyngby, Denmark; Department of Pediatrics, University of California, San Diego, CA, 92093, USA.
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Ankenbauer A, Schäfer RA, Viegas SC, Pobre V, Voß B, Arraiano CM, Takors R. Pseudomonas putida KT2440 is naturally endowed to withstand industrial-scale stress conditions. Microb Biotechnol 2020; 13:1145-1161. [PMID: 32267616 PMCID: PMC7264900 DOI: 10.1111/1751-7915.13571] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 03/11/2020] [Accepted: 03/15/2020] [Indexed: 12/17/2022] Open
Abstract
Pseudomonas putida is recognized as a very promising strain for industrial application due to its high redox capacity and frequently observed tolerance towards organic solvents. In this research, we studied the metabolic and transcriptional response of P. putida KT2440 exposed to large-scale heterogeneous mixing conditions in the form of repeated glucose shortage. Cellular responses were mimicked in an experimental setup comprising a stirred tank reactor and a connected plug flow reactor. We deciphered that a stringent response-like transcriptional regulation programme is frequently induced, which seems to be linked to the intracellular pool of 3-hydroxyalkanoates (3-HA) that are known to serve as precursors for polyhydroxyalkanoates (PHA). To be precise, P. putida is endowed with a survival strategy likely to access cellular PHA, amino acids and glycogen in few seconds under glucose starvation to obtain ATP from respiration, thereby replenishing the reduced ATP levels and the adenylate energy charge. Notably, cells only need 0.4% of glucose uptake to build those 3-HA-based energy buffers. Concomitantly, genes that are related to amino acid catabolism and β-oxidation are upregulated during the transient absence of glucose. Furthermore, we provide a detailed list of transcriptional short- and long-term responses that increase the cellular maintenance by about 17% under the industrial-like conditions tested.
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Affiliation(s)
- Andreas Ankenbauer
- Institute of Biochemical EngineeringUniversity of StuttgartAllmandring 3170569StuttgartGermany
| | - Richard A. Schäfer
- Institute of Biochemical EngineeringUniversity of StuttgartAllmandring 3170569StuttgartGermany
| | - Sandra C. Viegas
- ITQBInstituto de Tecnologia Química e Biológica António XavierUniversidade Nova de LisboaAv. da República2780‐157OeirasPortugal
| | - Vânia Pobre
- ITQBInstituto de Tecnologia Química e Biológica António XavierUniversidade Nova de LisboaAv. da República2780‐157OeirasPortugal
| | - Björn Voß
- Institute of Biochemical EngineeringUniversity of StuttgartAllmandring 3170569StuttgartGermany
| | - Cecília M. Arraiano
- ITQBInstituto de Tecnologia Química e Biológica António XavierUniversidade Nova de LisboaAv. da República2780‐157OeirasPortugal
| | - Ralf Takors
- Institute of Biochemical EngineeringUniversity of StuttgartAllmandring 3170569StuttgartGermany
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Thomas GH. Microbial Musings – February 2020. Microbiology (Reading) 2020; 166:93-95. [PMID: 32122459 PMCID: PMC7398560 DOI: 10.1099/mic.0.000901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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