Biorefineries
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Synthetic sRNAs to knockdown genes in medical and industrial bacteria
Bacteria are intimately involved in our daily lives. These microorganisms have been used in human history for food such as cheese, yogurt, and wine, In more recent years, through metabolic engineering, microorganisms been used extensively as microbial cell factories to manufacture plastics, feed for livestock, dietary supplements, and drugs. However, in addition to these bacteria that are beneficial to human lives, pathogens such as Pneumonia, Salmonella, and Staphylococcus that cause various infectious diseases are also ubiquitously present. It is important to be able to metabolically control these beneficial industrial bacteria for high value-added chemicals production and to manipulate harmful pathogens to suppress its pathogenic traits.
KAIST (President Kwang Hyung Lee) announced on the 10th that a research team led by Distinguished Professor Sang Yup Lee of the Department of Biochemical Engineering has developed a new sRNA tool that can effectively inhibit target genes in various bacteria, including both Gram-negative and Gram-positive bacteria. The research results were published online on April 24 in Nature Communications.
※ Thesis title: Targeted and high-throughput gene knockdown in diverse bacteria using synthetic sRNAs
※ Author information : Jae Sung Cho (co-1st), Dongsoo Yang (co-1st), Cindy Pricilia Surya Prabowo (co-author), Mohammad Rifqi Ghiffary (co-author), Taehee Han (co-author), Kyeong Rok Choi (co-author), Cheon Woo Moon (co-author), Hengrui Zhou (co-author), Jae Yong Ryu (co-author), Hyun Uk Kim (co-author) and Sang Yup Lee (corresponding author).
sRNA is an effective tool for synthesizing and regulating target genes in E. coli, but it has been difficult to apply to industrially useful Gram-positive bacteria such as Bacillus subtilis and Corynebacterium in addition to Gram-negative bacteria such as E. coli.
To address this issue, a research team led by Distinguished Professor Lee Sang Yup Lee of the Department of Chemical and Biomolecular Engineering at KAIST developed a new sRNA platform that can effectively suppress target genes in various bacteria, including both Gram-negative and positive bacteria. The research team surveyed thousands of microbial-derived sRNA systems in the microbial database, and eventually designated the sRNA system derived from 'Bacillus subtilis' that showed the highest gene knockdown efficiency, and designated it as “Broad-Host-Range sRNA”, or BHR-sRNA.
A similar well-known system is the CRISPR interference (CRISPRi) system, which is a modified CRISPR system that knocks down gene expression by suppressing the gene transcription process. However, the Cas9 protein in the CRISPRi system has a very high molecular weight, and there have been reports growth inhibition in bacteria. The BHR-sRNA system developed in this study did not affect bacterial growth while showing similar gene knockdown efficiencies to CRISPRi.
< Figure 1. a) Schematic illustration demonstrating the mechanism of syntetic sRNA b) Phylogenetic tree of the 16 Gram-negative and Gram-positive bacterial species tested for gene knockdown by the BHR-sRNA system. >
To validate the versatility of the BHR-sRNA system, 16 different gram-negative and gram-positive bacteria were selected and tested, where the BHR-sRNA system worked successfully in 15 of them. In addition, it was demonstrated that the gene knockdown capability was more effective than that of the existing E. coli-based sRNA system in 10 bacteria. The BHR-sRNA system proved to be a universal tool capable of effectively inhibiting gene expression in various bacteria.
In order to address the problem of antibiotic-resistant pathogens that have recently become more serious, the BHR-sRNA was demonstrated to suppress the pathogenicity by suppressing the gene producing the virulence factor. By using BHR-sRNA, biofilm formation, one of the factors resulting in antibiotic resistance, was inhibited by 73% in Staphylococcus epidermidis a pathogen that can cause hospital-acquired infections. Antibiotic resistance was also weakened by 58% in the pneumonia causing bacteria Klebsiella pneumoniae. In addition, BHR-sRNA was applied to industrial bacteria to develop microbial cell factories to produce high value-added chemicals with better production performance. Notably, superior industrial strains were constructed with the aid of BHR-sRNA to produce the following chemicals: valerolactam, a raw material for polyamide polymers, methyl-anthranilate, a grape-flavor food additive, and indigoidine, a blue-toned natural dye.
The BHR-sRNA developed through this study will help expedite the commercialization of bioprocesses to produce high value-added compounds and materials such as artificial meat, jet fuel, health supplements, pharmaceuticals, and plastics. It is also anticipated that to help eradicating antibiotic-resistant pathogens in preparation for another upcoming pandemic. “In the past, we could only develop new tools for gene knockdown for each bacterium, but now we have developed a tool that works for a variety of bacteria” said Distinguished Professor Sang Yup Lee.
This work was supported by the Development of Next-generation Biorefinery Platform Technologies for Leading Bio-based Chemicals Industry Project and the Development of Platform Technologies of Microbial Cell Factories for the Next-generation Biorefineries Project from NRF supported by the Korean MSIT.
2023.05.10 View 4927 -
Deep Learning-Powered 'DeepEC' Helps Accurately Understand Enzyme Functions
(Figure: Overall scheme of DeepEC)
A deep learning-powered computational framework, ‘DeepEC,’ will allow the high-quality and high-throughput prediction of enzyme commission numbers, which is essential for the accurate understanding of enzyme functions.
A team of Dr. Jae Yong Ryu, Professor Hyun Uk Kim, and Distinguished Professor Sang Yup Lee at KAIST reported the computational framework powered by deep learning that predicts enzyme commission (EC) numbers with high precision in a high-throughput manner.
DeepEC takes a protein sequence as an input and accurately predicts EC numbers as an output. Enzymes are proteins that catalyze biochemical reactions and EC numbers consisting of four level numbers (i.e., a.b.c.d) indicate biochemical reactions. Thus, the identification of EC numbers is critical for accurately understanding enzyme functions and metabolism.
EC numbers are usually given to a protein sequence encoding an enzyme during a genome annotation procedure. Because of the importance of EC numbers, several EC number prediction tools have been developed, but they have room for further improvement with respect to computation time, precision, coverage, and the total size of the files needed for the EC number prediction.
DeepEC uses three convolutional neural networks (CNNs) as a major engine for the prediction of EC numbers, and also implements homology analysis for EC numbers if the three CNNs do not produce reliable EC numbers for a given protein sequence. DeepEC was developed by using a gold standard dataset covering 1,388,606 protein sequences and 4,669 EC numbers.
In particular, benchmarking studies of DeepEC and five other representative EC number prediction tools showed that DeepEC made the most precise and fastest predictions for EC numbers. DeepEC also required the smallest disk space for implementation, which makes it an ideal third-party software component.
Furthermore, DeepEC was the most sensitive in detecting enzymatic function loss as a result of mutations in domains/binding site residue of protein sequences; in this comparative analysis, all the domains or binding site residue were substituted with L-alanine residue in order to remove the protein function, which is known as the L-alanine scanning method.
This study was published online in the Proceedings of the National Academy of Sciences of the United States of America (PNAS) on June 20, 2019, entitled “Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers.”
“DeepEC can be used as an independent tool and also as a third-party software component in combination with other computational platforms that examine metabolic reactions. DeepEC is freely available online,” said Professor Kim.
Distinguished Professor Lee said, “With DeepEC, it has become possible to process ever-increasing volumes of protein sequence data more efficiently and more accurately.”
This work was supported by the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries from the Ministry of Science and ICT through the National Research Foundation of Korea. This work was also funded by the Bio & Medical Technology Development Program of the National Research Foundation of Korea funded by the Korean government, the Ministry of Science and ICT.
Profile:
-Professor Hyun Uk Kim (ehukim@kaist.ac.kr)
https://sites.google.com/view/ehukim
Department of Chemical and Biomolecular Engineering
-Distinguished Professor Sang Yup Lee (leesy@kaist.ac.kr)
Department of Chemical and Biomolecular Engineering
http://mbel.kaist.ac.kr
2019.07.09 View 34502 -
Efficiently Producing Fatty Acids and Biofuels from Glucose
Researchers have presented a new strategy for efficiently producing fatty acids and biofuels that can transform glucose and oleaginous microorganisms into microbial diesel fuel, with one-step direct fermentative production.
The newly developed strain, created by Distinguished Professor Sang Yup Lee and his team, showed the highest efficiency in producing fatty acids and biodiesels ever reported. It will be expected to serve as a new platform to sustainably produce a wide array of fatty acid-based products from glucose and other carbon substrates.
Fossil fuels, which have long been energy resources for our daily lives, are now facing serious challenges: depletion of their reserves and their role in global warming. The production of sustainable bio-based renewable energy has emerged as an essential alternative and many studies to replace fossil fuels are underway. One of the representative examples is biodiesel. Currently, it is mainly being produced through the transesterification of vegetable oils or animal fats.
The research team engineered oleaginous microorganisms, Rhodococcus opacus, to produce fatty acids and their derivatives that can be used as biodiesel from glucose, one of the most abundant and cheap sugars derived from non-edible biomass.
Professor Lee’s team has already engineered Escherichia coli to produce short-chain hydrocarbons, which can be used as gasoline (published in Nature as the cover paper in 2013). However, the production efficiency of the short-chain hydrocarbons using E. coli (0.58 g/L) fell short of the levels required for commercialization.
To overcome these issues, the team employed oil-accumulating Rhodococcus opacus as a host strain in this study. First, the team optimized the cultivation conditions of Rhodococcus opacus to maximize the accumulation of oil (triacylglycerol), which serves as a precursor for the biosynthesis of fatty acids and their derivatives. Then, they systematically analyzed the metabolism of the strain and redesigned it to enable higher levels of fatty acids and two kinds of fatty acid-derived biodiesels (fatty acid ethyl esters and long-chain hydrocarbons) to be produced.
They found that the resulting strains produced 50.2, 21.3, and 5.2 g/L of fatty acids, fatty acid ethyl esters, and long-chain hydrocarbons, respectively. These are all the highest concentrations ever reported by microbial fermentations. It is expected that these strains can contribute to the future industrialization of microbial-based biodiesel production.
“This technology creates fatty acids and biodiesel with high efficiency by utilizing lignocellulose, one of the most abundant resources on the Earth, without depending on fossil fuels and vegetable or animal oils. This will provide new opportunities for oil and petroleum industries, which have long relied on fossil fuels, to turn to sustainable and eco-friendly biotechnologies,” said Professor Lee.
This paper titled “Engineering of an oleaginous bacterium for the production of fatty acids and fuels” was published in Nature Chemical Biology on June 17.
This work was supported by the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries from the Ministry of Science and ICT through the National Research Foundation (NRF) of Korea (NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557).
(Figure: Metabolic engineering for the production of free fatty acids (FFAs), fatty acid ethyl esters (FAEEs), and long-chain hydrocarbons (LCHCs) in Rhodococcus opacus PD630. Researchers have presented a new strategy for efficiently producing fatty acids and biofuels that can transform glucose and oleaginous microorganisms into microbial diesel fuel, with one-step direct fermentative production.)
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Source:
Hye Mi Kim, Tong Un Chae, So Young Choi, Won Jun Kim and Sang Yup Lee. Engineering of an oleaginous bacterium for the production of fatty acids and fuels. Nature Chemical Biology ( https://www.nature.com/nchembio/ ) DOI: 10.1038/s41589-019-0295-5
Profile
Dr. Sang Yup Lee
leesy@kaist.ac.kr
Distinguished Professor at the Department of Chemical and Biomolecular Engineering
KAIST
2019.06.19 View 46388 -
Engineered Microbial Production of Grape Flavoring
(Image 1: Engineered bacteria that produce grape flavoring.)
Researchers report a microbial method for producing an artificial grape flavor. Methyl anthranilate (MANT) is a common grape flavoring and odorant compound currently produced through a petroleum-based process that uses large volumes of toxic acid catalysts.
Professor Sang-Yup Lee’s team at the Department of Chemical and Biomolecular Engineering demonstrated production of MANT, a naturally occurring compound, via engineered bacteria. The authors engineered strains of Escherichia coli and Corynebacetrium glutamicum to produce MANT through a plant-based engineered metabolic pathway.
The authors tuned the bacterial metabolic pathway by optimizing the levels of AAMT1, the key enzyme in the process. To maximize production of MANT, the authors tested six strategies, including increasing the supply of a precursor compound and enhancing the availability of a co-substrate. The most productive strategy proved to be a two-phase extractive culture, in which MANT was extracted into a solvent. This strategy produced MANT on the scale of 4.47 to 5.74 grams per liter, a significant amount, considering that engineered microbes produce most natural products at a scale of milligrams or micrograms per liter.
According to the authors, the results suggest that MANT and other related molecules produced through industrial processes can be produced at scale by engineered microbes in a manner that would allow them to be marketed as natural one, instead of artificial one.
This study, featured at the Proceeding of the National Academy of Sciences of the USA on May 13, was supported by the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries from the Ministry of Science and ICT.
(Image 2. Overview of the strategies applied for the microbial production of grape flavoring.)
2019.05.15 View 52746