Hero image of the page

Project Description

Inspiration

White Pollution —— A worldwide problem

More plastic demand and production will lead to more plastic contamination.

The global plastic production number is almost 45x larger than how it was in 1960. According to Our World in Data, global plastic production in 1960 was only 8 million tonnes. That number had more than quadrupled in 1970 and by the new century, the number was 213 million tonnes. In 2015, global plastic production is around 367 million tons (Figure 1) and plastic production is expected to reach 1.1 billion tons by 2050 (Data from ChinaIRN.COM).

Fig.1 Global plastic production
Fig.1 Global plastic production

As very little plastic is recycled, a great deal of plastic (91%) enters the environment and causes white pollution.

Around 400 million tons of plastic are produced each year, while 50 percent are single-use plastics that can take up to 450 years to decompose. In China, only a shocking 9 percent of all plastic is recycled. Where does the remaining 91 percent of plastic go? Our environment.

Fig.2 Plastic waste discarded in the environment
Fig.2 Plastic waste discarded in the environment

Plastic has a wide range of effects on all aspects of human health.

Increased plastic production leads to increased micro/nano plastics. Microplastics are categorized as plastics less than 5 mm in size and nano-plastics are plastics less than 1 nanometer in size. Both are formed during the degradation process of larger plastics or produced intentionally to put in medical packaging. Humans are exposed to micro and nano plastics in a variety of ways. For example, different foods have been found to have concentrations of micro/nano plastics. These include sugar, salt, alcohol, bottled water, fish, and plants (fruits and vegetables). This is a result of contaminated soil, air, or water from plastic contamination. If our reliance on plastic increases, there will only be a bigger accumulation of micro/nano plastics in our bodies.

Researchers have found that nano plastics can pass through the placenta and the blood-brain barrier. An essential lipid layer called the blood-brain barrier regulates what goes into and what doesn't go into the brain. The placenta serves as a vital source of nutrition and defense for developing fetuses inside the womb. According to numerous types of research, newborns exposed to nano plastics may experience neurological and developmental issues. Additionally, nano plastics have the potential to permeate our tissues and cells, causing longer-lasting harm such as DNA damage and oxidative stress. This resulted in pulmonary damage and gastrointestinal tract irritation. The fact that nano plastics can penetrate lung alveoli indicates they can enter the bloodstream and spread to other parts of the body.

Fig.3 The impact of plastics on human health
Fig.3 The impact of plastics on human healthSource: https://www.researchgate.net/figure/Impact-of-Plastics-on-Human-Health_fig2_321906991

Methods to solve the recycling of plastics

In last year's project, by comparing physical, chemical, and biological methods (https://2021.igem.org/Team:IvyMaker-China/Description), we determined that biological methods had the better potential for PET recycling. At the same time, we also confirmed in our experiments that our system with surface display of PETase and MHETase separately had a good capability of PET degradation (https://2021.igem.org/Team:IvyMaker-China/Results).

However, when the two enzymes were displayed independently, it may cause issues in subsequent industrial applications. What’s more, merely PETase or MHETase could not thoroughly break down the plastic.

To increase the efficiency and convenience of PET plastic degradation, we plan to display the two enzymes on the cell surface. Therefore, in this year's project, we plan to co-display PETase and MHETase and at the same time optimize their ratio to ensure better degradation efficiency and facilitate their subsequent industrial application.

Project Goals and Design

Overview

Based on previous studies, we developed a surface display system for Candida tropicalis. We used the system to display PETase and MHETase separately and found that both enzymes were active and had obvious degradation effects on PET film and powder. To increase the effectiveness and convenience of the degradation of PET plastic, we plan to display MHETase and PETase at the same time. Hence, our goal is to develop a self-assembled multi-enzyme display system for Candida tropicalis. To attain co-display, we combined our display system with two selective protein binding systems, SpyTag-SpyCatcher and SnoopTag-SnoopCatcher. To improve PET plastic degradation efficiency, we imitated cellulosome and controlled the ratio and display sequence of PETase and MHETase enzymes. Additionally, the surface display multi-enzyme self-assembly system that we developed can be applied to fields other than plastic degradation.

Fig.4 Improvement of last year's project
Fig.4 Improvement of last year's project

Part One Construction of the co-display system

Construction of spy/snoop tag and catcher system

Introduction of spy/snoop tag and catcher system.

Tag-Catcher systems.

Tag-Catcher systems in our experiment include SpyTag/SpyCatcher and SnoopTag/SnoopCatcher.

SpyTag/SpyCatcher: It is based on a modified domain from a Streptococcus pyogenes surface protein (SpyCatcher), which recognizes a cognate 13-amino-acid peptide (SpyTag). Upon recognition, the two form a covalent isopeptide bond between the side chains of lysine in SpyCatcher and aspartate in SpyTag. This technology has been used, among other applications, to create covalently stabilized multi-protein complexes, for modular vaccine production, and to label proteins (e.g., for microscopy). The SpyTag system is versatile as the tag is a short, unfolded peptide that can be genetically fused to exposed positions in target proteins

SnoopTag/SnoopCatcher: Similarly, SpyCatcher can be fused to reporter proteins such as GFP, and epitope or purification tags. Additionally, an orthogonal system called SnoopTag-SnoopCatcher has been developed from an S. pneumoniae pilin that can be combined with SpyCatcher-SpyTag to produce protein fusions with multiple components.

In our experiment, GFP and RFP were used to indicate the successful construction of the Spycatcher/Spytag and Snoopcatcher/Snooptag systems. We utilized both Spycatcher/Spytag and Snoopcatcher/Snooptag systems. The specific and covalent SpyTag/SpyCatcher interaction provides a powerful way to build and link proteins into assemblies.

The construction of plasmid Ts-PGAPDH--TENO1A, the surface display system for displaying both GFP and RFP
Fig.5 The construction of plasmid Ts-PGAPDH--TENO1A, the surface display system for displaying both GFP and RFP (BBa_K4122017)SC: Spycatcher BBa_K4122008 ; SNC: Snoopcatcher BBa_K4122010; V5: V5 tag BBa_K3829004; CBM: carbohydrate binding domain BBa_K4122006
Construction of a surface display system for displaying both PETase and MHETase

When replacing RFP and GFP with MHETase and PETase, it did not work. Through modeling, we figured out that the problem was caused by the V5 tag being embedded. Therefore, we changed the position of the V5 tag to successfully improve the condition.

Successful construction of changing the position of V5-tag
Fig.6 Successful construction of changing the position of V5-tag

Part Two Optimization of scaffold for surface display

Optimization of the scaffold by exchanging tag and catcher & Optimize PETase and MHETase surface display Ratios

To reduce the molecular mass of the proteins transcribed, we change the spy catcher to spy tag and the snoop catcher to snoop tag.

Notes: Tag is much smaller than catcher.

Fig.7 The optimization of changing the position and ratios of catcher and tag
Fig.7 The optimization of changing the position and ratios of the catcher and tag

References

  1. Wei Zheng, Chengxin Zhang, Yang Li, Robin Pearce, Eric W. Bell, Yang Zhang. Folding non-homology proteins by coupling deep-learning contact maps with I-TASSER assembly simulations. Cell Reports Methods, 1: 100014 (2021).
  2. Chengxin Zhang, Peter L. Freddolino, and Yang Zhang. COFACTOR: improved protein function prediction by combining structure, sequence and protein-protein interaction information. Nucleic Acids Research, 45: W291-299 (2017).
  3. Jianyi Yang, Yang Zhang. I-TASSER server: new development for protein structure and function predictions, Nucleic Acids Research, 43: W174-W181, 2015.
  4. Lu, Hongyuan, et al. "Machine learning-aided engineering of hydrolases for PET depolymerization." Nature 604.7907 (2022): 662-667.