iGEM 2024 — Team sre-china

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1. July 1, 2024

   Participants: Yijia Huang, Jinming Cui, Zikuan Bai, Yuesi Xu, Jingchen Shi, Xiaoman Li, Yanzhi Xiao, Ziqing Jin, Yufan Dong, Andi Hu, Huier Peng, Jianing Wu, Hang Su, Chengjun Jiang, Hamo Wan, Yihao Tao, Jeffrey (Jiarui) Shen, Qiyun Wang, Jiayang Wang, Zhuxu Zhang, Haonan Deng, Junqiao Chen, Yanrong Li, Chenming Jin, Chang Wang, Yueyan Ma

   A.Understand the phenomenon of aging and the mechanisms of action of antioxidants and anti-aging substances.
   B.Learn the background related to Alzheimer’s disease.
   C.Study basic knowledge and common command-line operations about Linux.
2. July 2, 2024

   Participants: Xu Yuesi, Bai Zikuan, Huang Yijia, Wan Hamo, Wang Chang, Chen Junqiao, Li Yanrong, Wu Jianing, Jin Chenming, Peng Huier, Ma Yueyan

   A.Learn how to install common programs on Linux and understand virtual machines.
   B.Study the Notepad++ text editor and learn how to use it for coding and text editing.
3. July 3, 2024

   Participants: Xu Yuesi, Cui Jinming, Li Xiaoman, Xiao Yanzhi, Jin Ziqing, Huang Yijia, Wang Chang, Chen Junqiao,Wu Jianing Su Hang, Jiang, Chengjun Jiang

   A.Know about the background related to wgx-50 molecule screening and discovery.
   B.Learn statistics and related knowledge of hypothesis testing.
   C.Learn common statistical analyses such as t-tests, ANOVA, non-parametric tests, survival
   analysis (Kaplan-Meier survival curve), correlation analysis, and regression analysis.
4. July 4, 2024

   Participants: Xu Yuesi, Cui Jinming, Li Xiaoman, Xiao Yanzhi, Jin Ziqing, Huang Yijia, Wang Chang, Chen Junqiao

   A.Further study the functions and skills of text editors, such as syntax highlighting, code folding, multi-tab editing, document comparison, etc.
   B.Learn how to study the survival of nematodes under antioxidant treatment and understand the steps of survival analysis.
5. July 5, 2024

   Participants: Xu Yuesi, Cui Jinming, Li Xiaoman, Xiao Yanzhi, Jin Ziqing, Huang Yijia, Wang Chang, Chen Junqiao

   A.Learn about popular text editors in scientific research, including Visual Studio Code (VS Code), Sublime Text, and Vim.
   B.Understand how to set up experimental and control groups for survival analysis experiments: Experimental group: Nematodes are exposed to antioxidants or other oxidative stress conditions (e.g., hydrogen peroxide, para-hydroxybenzaldehyde). Control Group: No antioxidant treatment is administered. For example, the subjects are only exposed to the same oxidative stress conditions as the experimental group but without the addition of antioxidants.
6. July 6, 2024

   Participants: Yijia Huang, Jinming Cui, Zikuan Bai, Yuesi Xu, Jingchen Shi, Xiaoman Li, Yanzhi Xiao, Ziqing Jin, Yufan Dong, Andi Hu, Huier Peng, Jianing Wu, Hang Su, Chengjun Jiang, Hamo Wan, Yihao Tao, Jeffrey (Jiarui) Shen, Haonan Deng, Junqiao Chen, Yanrong Li, Chenming Jin

   A.Learn to use the Windows terminal emulator Xshell and master how to remotely log in and
   manage servers via SSH, Telnet, Rlogin, SFTP, and Serial protocols.
   B.Understand how to prepare nematodes for survival analysis experiments, and select a commonly used nematode species among model organisms: Caenorhabditis elegans (C. elegans), to perform nematode synchronization (to make them at the same developmental stage). A common method is to use eggs floating in egg solution for synchronization.
7. July 7, 2024

   Participants: Xu Yuesi, Cui Jinming, Li Xiaoman, Xiao Yanzhi, Jin Ziqing, Huang Yijia, Wang Chang, Junqiao Chen

   A.Advanced learning of XShell scripting and automation, text editing and searching, batch operations and automation.
   B.Understand how to handle antioxidants in survival analysis experiments.
   C.Preparation of medium: Dissolve antioxidants into the standard Nematode Growth Medium (NGM) with concentrations set according to the experimental design. Different concentration gradients of antioxidants can be used.
   D.Addition of nematodes: Distribute synchronized nematodes into different petri dishes, with each dish containing nematodes from the same treatment group.
8. July 8, 2024

   Participants: Xu Yuesi, Jin Ziqing, Xiao Yanzhi, Bai Zikuan, Huang Yijia, Wan Hamo, Wang Chang, Chen Junqiao, Li Yanrong, Wu Jianing, Jin Chenming, Peng Huier,

   A.Learn about and understand the PDB database(https://www.rcsb.org/) , its introduction and usage, and study how to download the crystal structure of proteins.
   B.Learn about how to conduct temperature control in survival analysis experiments: typically, worms are cultured at a specific temperature (e.g., 20℃) to ensure their normal growth. Learn how to regularly observe the survival status of worms. Learn to use a microscope to confirm whether worms are alive. Living worms will exhibit obvious movement, while dead worms will show no response and have stiff bodies.
   C.The experiment on prolonging the lifespan of nematodes (C. elegans, N2 strain) with WGX-50 (a type of xanthoxylin) at 20℃ involves treating wild-type nematodes with different concentration gradients of WGX-50 to determine the optimal concentration, recording the data, and plotting a lifespan curve.
9. July 9, 2024

   Participants: Xu Yuesi, Cui Jinming, Li Xiaoman, Xiao Yanzhi, Jin Ziqing, Huang Yijia, Wang Chang, Chen Junqiao

   A.Learn to use the biological macromolecule visualization software Pymol （https://pymolwiki.org/index.php/Main\_Page）
   B.Observe the state of nematodes regularly: Check the survival status of nematodes daily. Use a microscope to confirm whether the nematodes are alive. Living nematodes will show obvious movement, while dead nematodes will be unresponsive and have stiff bodies.
   C.Learn about the settings of the survival experiment, which is divided into 10 experimental groups (2 mM, 1 mM, 0.5 mM, 50 μM, 125 μM, 62.5 μM, 31.25 μM, 15 μM, 7.5 μM, 3.75 μM) and 1 control group (0.1% ethanol). On the nematode solid growth medium containing OP50 (nematode food), wild-type nematodes are first subjected to synchronized culture.
10. July 10, 2024

    Participants: Xu Yuesi, Cui Jinming, Li Xiaoman, Xiao Yanzhi, Jin Ziqing, Huang Yijia, Wang Chang, Junqiao Chen

    A.Learn about the Pymol interface, how to set the working path, how to import proteins, and how to select objects.
    B.Record the data for nematode survival analysis: Record the number of surviving nematodes in each group daily. Dead nematodes should be removed promptly to prevent affecting the health of other nematodes.
    C.Understand the basic principles and concepts of RNA-seq (RNA Sequencing).
    D.Conduct the survival experiment: When the synchronized nematodes have developed to the L4 stage, use a nematode picker under a stereomicroscope (Motic Company) to select well-grown nematodes of uniform size and transfer 30 nematodes each to the NGM solid medium containing 5-fluoro-2'-deoxyuridine (FUDR, Beijing Puxitang Biotechnology) for each experimental group. This process ensures that the nematodes are of the same age and developmental stage to avoid experimental errors. Set up 5 culture dishes for each group, meaning approximately 150 nematodes are included in each group as observation subjects.
11. July 11, 2024

    Participants: Xu Yuesi, Bai Zikuan, Huang Yijia, Wan Hamo, Wang Chang, Chen Junqiao, Li Yanrong, Wu Jianing, Jin Chenming

    A.Learn about the measurement tools in Pymol software, how to set conformation parameters, and how to save files and export images.
    B.Master the RNA-seq experimental process, including RNA extraction and RNA quality control.
    C.Learn Adobe Illustrator, a vector-based graphic design software from Adobe Systems, and learn to use the “Pen Tool” to set “Anchor Points” and “Direction Lines” for graphic drawing in the later stage, and learn about advanced drawing tools and controls.
    D.After the experiment, data was organized, survival curves were created, experimental results were compared to draw conclusions, and the optimal drug concentration of WGX-50 for extending C. elegans lifespan was determined.
12. July 12, 2024

    Participants: Xu Yuesi, Cui Jinming, Li Xiaoman, Xiao Yanzhi, Jin Ziqing, Huang Yijia, Wang Chang, Junqiao Chen

    A.Learn about Pymol software, including how to modify label properties, set custom colors, and perform breakthrough rendering.
    B.Continue learning to use Adobe Illustrator.
    C.Perform RNA-seq library construction, including fragmentation, end modification, and PCR amplification.
    D.Control group: 50 µM of WGX-50; Treatment group: 0.1% ethanol. Three biological replicates per group, treated for 5 days at 25℃. C. elegans are washed from NGM plates using M9 buffer.
13. July 13, 2024

    Participants: Xu Yuesi, Cui Jinming, Li Xiaoman, Xiao Yanzhi, Jin Ziqing, Huang Yijia, Wang Chang, Junqiao Chen

    A.Gain initial understanding of quantum chemistry software Gaussian and its development history, and grasp how to simulate the quantum mechanical behavior of molecular systems through calculations.
    B.Learn about how to analyze sequencing data through bioinformatics to obtain quantitative and qualitative gene expression data.
    C.Conduct quality control for RNA-seq: The constructed library needs to undergo quality inspection to confirm whether the fragment size distribution and concentration meet the sequencing requirements. Agilent Bioanalyzer or qPCR is selected for library quantification and fragment analysis.
14. July 14, 2024

    Participants: Xu Yuesi, Cui Jinming, Li Xiaoman, Xiao Yanzhi, Jin Ziqing, Huang Yijia, Wang Chang, Junqiao Chen

    A.Learn how to install and run Gaussian software on both Windows and Linux versions, and understand the input and output files for Gaussian.
    B.Master raw data processing for RNA-Seq: Including removing adapters and low-quality sequences, and aligning to the reference genome.
    C.Continue with the RNA-Seq experiment. To avoid phenotypic changes related to reproduction inhibition, reproduction-inhibiting drugs are not used when handling the nematodes. Therefore, throughout the research process, we transferred the adult worms to newly prepared plates every day.
15. July 15, 2024

    Participants: Xu Yuesi, Cui Jinming, Li Xiaoman, Xiao Yanzhi, Jin Ziqing, Huang Yijia, Wang Chang, Junqiao Chen

    A.Learn the visualization and modeling tool GaussView accompanying Gaussian software. Study how to use Gaussian for geometric optimization of structures, understand commonly used methods in quantum chemistry calculations to aid in the convergence of geometric optimization, master how to use different coordinate systems and coordinate definition methods, how to adjust the maximum step size, and how to adjust symmetry.
    B.Continue to transfer the adult worms to newly prepared plates.
    C.Learn to conduct differential expression analysis: Use differential expression analysis tools (such as DESeq2, edgeR) to compare gene expression levels between different experimental groups and identify significantly up-regulated or down-regulated genes.
    D.Continue learning and getting familiar with the use of Adobe Illustrator software.
16. July 16, 2024

    Participants: Xu Yuesi, Jin Ziqing, Xiao Yanzhi, Bai Zikuan, Huang Yijia, Wan Hamo, Wang Chang, Chen Junqiao, Li Yanrong, Wu Jianing, Jin Chenming, Peng Huier, Ma Yueyan

    A.Learn the chemical structure visualization software tool CYLView and master the main techniques for displaying and analyzing molecular structures and reaction mechanisms. Further deepen the understanding of commonly used methods in quantum chemistry calculations to aid in the convergence of geometric optimization, and grasp how to try different initial structures, as well as various theoretical methods and basis sets.
    B.Study the molecular biology method of RT-qPCR (Reverse Transcription Quantitative Polymerase Chain Reaction), understanding its applications in quantitative gene expression, viral load detection, miRNA and lncRNA detection, and gene mutation analysis.
    C.Continue learning and getting familiar with the use of Adobe Illustrator software.
17. July 17, 2024

    Participants: Xu Yuesi, Jin Ziqing, Xiao Yanzhi

    A.Further study the data analysis and output features of the CYLView software, as well as the analysis of molecular simulation and computational chemistry results.
    B.Understand the process of RT-qPCR, including how to perform reverse transcription of RNA to convert it into complementary DNA (cDNA); then use quantitative real-time PCR (qPCR) to amplify the cDNA while monitoring the amount of amplification products in real-time.
    C.Aspirate the supernatant from the worm samples, freeze the nematodes in liquid nitrogen, store the frozen samples using dry ice, and then proceed with the specific sequencing work.
18. July 18, 2024

    Participants: Xu Yuesi, Jin Ziqing, Xiao Yanzhi, Cui Jinming, Li Xiaoman, Huang Yijia, Wang Chang, Chen Junqiao

    A.Understand the concepts in computational chemistry and computational physics, comprehend the basic principles of molecular dynamics simulations, and master simulating the behavior of molecular systems as they evolve over time. Master how to numerically solve the classical mechanical equations between molecules.
    B.Understand the main steps of RT-qPCR reactions, including Denaturation, Annealing, and Extension.
    C.Sequencing: Worms are lysed in TRIzol (Thermo Fisher Scientific, USA) reagent using a tissue homogenizer to extract total RNA. The concentration and purity of RNA samples are measured at 260nm using a Nanodrop 2000 (Thermo Fisher Scientific, USA). The integrity of RNA is assessed using an Agilent 2100 Bioanalyzer and RNA nano 6000 Assay Kit (Agilent Technologies, USA). Poly-A mRNA is enriched using Oligo-(dT) magnetic beads provided by the TIANSeq mRNA Capture Kit (TIANGEN, China).
19. July 19, 2024

    Participants: Xu Yuesi, Jin Ziqing, Xiao Yanzhi

    A.Learn about the concept and basic principles of molecular docking, and understand the
    characteristics of common molecular docking software such as AutoDock, MOE, Glide, DOCK, etc.
    B.Understand the functions and main features of AlphaFold3, and the AlphaFold 3 model based on diffusion-based architecture, which is capable of jointly predicting the structures of complexes including proteins, nucleic acids, small molecules, ions, and modified residues.
    C.Continue learning and getting familiar with the use of Adobe Illustrator software.
    D.Continue learning and getting familiar with the use of Pymol software.
20. July 20, 2024

    Participants: Xu Yuesi, Jin Ziqing, Xiao Yanzhi

    A.Understand and master RT-expression level changes: Demonstrate the relative or absolute expression levels of target genes under different treatment conditions based on calculated ΔΔCt values or standard curves.
    B.Continue with the sequencing steps by generating a transcriptome RNA-seq library using the TIANSeq Fast RNA Library Prep Kit (Illumina).
    C.Subsequently, the enriched poly-A mRNA samples undergo random fragmentation, complementary cDNA strand synthesis, end repair, and the addition of fluorescently labeled tags.
    D.Measure the prepared mRNA-cDNA samples (with activity > 2 nM) using a Qubit 2.0 Fluorometer (Life Technologies, USA) and dilute them to 1 ng/µL.
    E.Use an Agilent 2100 Bioanalyzer to assess the size of the mRNA-cDNA sample fragments mentioned above.
    F.Following the manufacturer’s instructions, perform bridge amplification on the cBot Cluster Generation System (Illumina, USA) using the TruSeq PE Cluster Kit v3-cBot-HS (Illumina, USA).
21. July 21, 2024

    Participants: Xu Yuesi, Cui Jinming, Li Xiaoman, Xiao Yanzhi, Jin Ziqing, Huang Yijia, Wang Chang, Chen Junqiao

    A.Learn about the concept, applications, fundamental principles, and processes of virtual screening, including how to rapidly and effectively identify potential candidate compounds with high binding affinity to specific targets (such as proteins, enzymes, receptors, etc.) from large compound libraries. Understand how computer models and algorithms can be used to predict the interactions between compounds and targets, screening out molecules with higher drug activity.
    B.Further explore and comprehend the contents of virtual screening, including Ligand-Based Virtual Screening (LBVS) and Structure-Based Virtual Screening (SBVS).
    C.Continue with the sequencing steps by completing paired-end sequencing of the mRNA-cDNA on the NovaSeq 6000 Sequencer (Illumina, USA).
    D.Continue learning and gaining familiarity with the use of Adobe Illustrator software.
22. July 22, 2024

    Participants: Xu Yuesi, Cui Jinming, Li Xiaoman, Xiao Yanzhi, Jin Ziqing, Huang Yijia, Wang Chang, Chen Junqiao

    A.Further understand the concepts of false positives and false negatives, and comprehend the reasons why virtual screening generates false positives (inactive compounds mistakenly identified as active) and false negatives (active compounds excluded from the selection).
    B.Learn about the fundamental principles of gene silencing mechanism RNA interference (RNAi), its application in bacteria, and study CRISPR interference (CRISPRi).
    C.For the obtained raw sequencing data in fastq format, use Trimmomatic (v0.39) software for analysis, whcih involves reading the sequencing sequences, removing the artificially added fluorescent tag sequences at the ends of the sequences, and low-confidence sequencing sequences, ultimately obtaining accurate sequencing DNA sequence data (clean data).
    D.Utilize FastQC (v0.12.0) to evaluate sequence quality and sequencing depth, thereby conducting quality control over the entire sequencing process.
23. July 23, 2024

    Participants: Xu Yuesi, Cui Jinming, Li Xiaoman, Xiao Yanzhi, Jin Ziqing, Huang Yijia, Wang Chang, Chen Junqiao,Wu Jianing Su Hang, Jiang, Chengjun Jiang

    A.Learn about molecular docking and its general process. Understand how to search for and predict the structures of receptors and ligands. Learn how to evaluate the scoring function (Binding Score) and assess the quality and results after docking, such as whether the ligand’s geometry is reasonable, including whether the binding pose of the ligand to the receptor aligns with the ligand’s physicochemical properties. The ligand conformations in docking results should avoid excessive deformation or unreasonable distortions.
    B.Understand the functions and technical features of the deep learning-based structure prediction framework AlphaFold2, such as the incorporation of the attention mechanism, which allows the model to effectively communicate information between amino acid residues that interact over long distances in the protein sequence.
    C.The Evoformer module is a transformer network that incorporates Multiple Sequence Alignment (MSA), responsible for processing MSA: Evoformer extracts co-evolutionary information from a set of aligned sequences of homologous proteins, helping the model understand how different proteins retain specific structural features during evolution. Sequence-structure interaction: Evoformer simultaneously processes protein sequences and related distance matrices (representing spatial relationships between amino acid residues), effectively integrating both sequence and spatial information.
    D.Use HISAT2 (v2.1.0) software to align the clean data to the reference genome of the nematode (GCA\_000002985.3, Ensembl). The purpose of the alignment is to assign gene names to the high-quality sequences from the sequencing.
    E.Use HTSeq (v0.6.0) to calculate the read counts mapped to a specific gene on the nematode genome.
    F.Through gene reads, calculate the Fragments Per Kilobase of transcript per Million mapped reads (FPKM) for each gene, using the FPKM value as a measure of a gene’s abundance at the mRNA level.
24. July 24, 2024

    Participants: Xu Yuesi, Cui Jinming, Li Xiaoman, Xiao Yanzhi, Jin Ziqing, Huang Yijia, Chen Junqiao,Wu Jianing, Chengjun Jiang

    A.Learn about the format of PDB files, including information such as the coordinate data of each atom in a protein, protein secondary structure information, and the interactions between atoms.
    B.Understand the construction of an expression vector during RNA interference: inserting a designed RNA sequence into a bacterial expression vector, such as a plasmid.
    C.Conduct differential expression gene (DEG) analysis between two groups of nematodes (control group and xanthoxylin-treated group) using the DESeq2 R package (v1.30.0) for screening. The criteria for screening differentially expressed genes are that the expression level of a gene in the xanthoxylin group must be more than 1.5 times higher than that in the control group, and the significance test for such changes should achieve a p-value (corrected p-value or p-adj, or q-value) < 0.05.
25. July 25, 2024

    Participants: Xu Yuesi, Bai Zikuan, Huang Yijia, Wan Hamo, Wang Chang, Chen Junqiao, Li Yanrong, Wu Jianing, Jin Chenming

    A.Understand the popular molecular dynamics simulation software Amber (Assisted Model Building with Energy Refinement) in scientific research, and learn the basic concepts of molecular dynamics, applicable problems, conventional simulation processes, GPU acceleration, etc.
    B.Familiarize with the modules in Amber software for molecular dynamics simulations, free energy calculations, data analysis, and more.
    C.Understand the concept of Gene Set Enrichment Analysis (GSEA) used in genomic enrichment analysis. Annotate differentially expressed genes with their validated biological functions, identifying gene sets that collectively participate in processes such as cell mitosis, cell proliferation, or cell death. This approach can capture subtle changes in gene expression levels while integrating multiple functionally related genes, thereby enabling more accurate identification of biological signals.
    D.Continue learning and engaging with Pymol software.
26. July 26, 2024

    Participants: Shi Jingchen, Li Xiaoman, Xiao Yanzhi

    A.Master the process of molecular dynamics simulations and learn the relevant knowledge and simulation techniques involved in molecular dynamics, including phase space, ensembles, calculation of statistical thermodynamic quantities, energy minimization algorithms, integration algorithms for equations of motion, periodic boundary conditions, and the calculation of non-bonded interactions.
    B.Learn how to prepare the initial structure of a target molecular system (such as proteins, nucleic acids, small molecules, solvents, etc.), and how to select an appropriate force field.
    C.Utilize the Gene Set Enrichment Analysis (GSEA) method for genomic enrichment analysis. The initial steps involve preparing the input data, specifically: gene expression data, typically from microarray or RNA-seq, requiring expression values for genes in each sample; phenotype data, representing different groups in the experimental design, such as control and treatment groups; and a gene set database, which are predefined gene sets used for enrichment analysis.
27. July 27, 2024

    Participants: Shi Jingchen, Li Xiaoman, Xiao Yanzhi

    A.Continue learning the process of molecular dynamics simulations.
    B.Learn how to generate molecular force fields for small molecules.
    C.Continue utilizing the Gene Set Enrichment Analysis (GSEA) method for genomic enrichment analysis. The gene ranking step involves sorting genes based on their expression differences according to a specific phenotype. Common ranking statistics include t-values, signal-to-noise ratios, or other metrics derived from the expression data. The result of this ranking is a list of genes ordered from highest to lowest significance of their association with the phenotype.
28. July 28, 2024

    Participants: Shi Jingchen, Li Xiaoman, Xiao Yanzhi

    A.Understand and master the common wavefunction analysis software Multiwfn. Learn about the basic features of Multiwfn and how to get started with it, including downloading, installing, and executing the program. Download the program manual and begin reading it.
    B.Calculate the Enrichment Score (ES). For each predefined gene set, GSEA examines whether these genes tend to appear at the top or bottom of the ranked list. If a gene set shows clustering at the high or low ranks of the sorted list (i.e., most genes appear in regions of significant expression), the gene set is considered “enriched”.
    C.Continue learning and engaging with Pymol software.
29. July 29, 2024

    Participants: Shi Jingchen, Li Xiaoman, Xiao Yanzhi

    A.Continue studying the knowledge of MD (Molecular Dynamics) simulation, while learning about various amino acid types in protein structural biology, protein structural hierarchies, helices, folds, turns, coils, disulfide bonds, salt bridges, and the time scales of protein structure formation.
    B.Continue reading and studying the Multiwfn manual to gain a deeper understanding of the significance, functions, and applications of the Multiwfn wave function analysis program.
    C.Study MD simulation to master how to perform energy minimization on the system, eliminating unreasonable parts in the structure (such as excessively short interatomic distances). Grasp how to gradually increase the temperature to reach the target temperature (e.g., 300K) and achieve thermodynamic equilibrium by balancing the system at that temperature.
    D.Continue to conduct statistical significance evaluation of the GSEA (Gene Set Enrichment Analysis) analysis process. To assess the significance of the enrichment scores, GSEA uses a permutation-based test. By randomly permuting the labels of the experimental and control groups multiple times, a distribution of random enrichment scores is generated. The p-value of the actual gene set enrichment score is calculated by comparing it to the enrichment scores in the random distribution to determine its significance.
30. July 30, 2024

    Participants: Shi Jingchen, Li Xiaoman, Xiao Yanzhi, Xu Yuesi, Bai Zikuan, Huang Yijia, Wan Hamo, Wang Chang, Chen Junqiao, Li Yanrong, Wu Jianing, Jin Chenming, Peng Huier, Ma Yueyan

    A.Continue learning about MD (Molecular Dynamics) simulation knowledge, while also studying the definitions of backbone dihedral angles parameters, Ramachandran plots, and standard naming conventions for residue atoms.
    B.Learn the basic features and usage methods of Multiwfn, mastering the supported formats of wavefunction files; understand the real-space functions supported by Multiwfn, and learn how to inspect and modify wavefunctions through the Multiwfn software.
    C.Continue to conduct genome enrichment analysis, focusing on the normalization of enrichment scores (NES).
31. July 31, 2024

    Participants: Shi Jingchen, Li Xiaoman, Xiao Yanzhi

    A.Understand and learn about the introduction of relevant force fields involved in MD simulation.
    B.Conduct RNA interference analysis by constructing HT115 bacteria from the Escherichia coli strain, which should contain the L4440 plasmid within its cells. This plasmid harbors the cDNA sequence of the exon region (CDS) of the gene targeted for interference (silencing, reduction of expression level).
    C.Construct the L4440-target interference gene plasmid.
32. August 1, 2024

    Participants: Xu Yuesi, Shi Jingchen, Li Xiaoman, Xiao Yanzhi, Cui Jinming, Jin Ziqing, Huang Yijia, Wang Chang

    A.Learn about the Cpptraj module in Amber software. Master how to obtain trajectory files and structure files from MD simulations in Amber. Understand how to set up analysis tasks: write analysis scripts using Cpptraj to define the analysis tasks to be executed, such as calculating RMSD (Root Mean Square Deviation), hydrogen bonds, distances, etc. Grasp how to run Cpptraj analysis: execute Cpptraj via the command line, read trajectory files, and perform analysis commands in the script. Additionally, learn how to view the output results.
    B.Continue to conduct genome enrichment analysis. To control the false positive rate, GSEA applies multiple testing correction to the analysis results of multiple gene sets using the FDR (False Discovery Rate) correction method.
    C.According to the methods described in the sequencing protocol, extract total RNA from nematodes and obtain complementary DNA (cDNA) sequences of the total RNA using a reverse transcription kit. Design specific primers based on the gene to be interfered with, and use PCR to amplify the target gene, facilitating its cloning.
33. August 2, 2024

    Participants: Xu Yuesi, Shi Jingchen, Li Xiaoman, Xiao Yanzhi

    A.Learn about the scientific graphing software Origin, and understand common 2D and 3D graph types used in scientific plotting, such as line charts, bar charts, scatter plots, contour plots, surface plots, etc., which are suitable for presenting experimental data results.
    B.Perform double enzyme digestion on the L4440 plasmid using XhoI and HindIII enzymes to obtain a linear plasmid with sticky ends. The plasmid is originally a closed circular DNA.
    C.Also perform double enzyme digestion on the cloned gene obtained above, and then use the Hieff clone®plus one-step cloning kit to insert it into the double-digested L4440 plasmid, ultimately obtaining a gene interference plasmid containing the cDNA sequence of the gene to be interfered with.
34. August 3, 2024

    Participants: Xu Yuesi, Shi Jingchen, Li Xiaoman, Xiao Yanzhi

    A.Continue learning about the scientific graphing software Origin, and understand common data analysis tools, including basic statistical analysis (such as mean, variance), regression analysis, curve fitting, signal processing (such as Fourier transform, filtering), etc.
    B.Continue with the RNAi experiment by amplifying the above gene interference plasmid and sending it for sequencing to confirm the correctness of the inserted target gene fragment sequence.
    C.Transform the sequencing-confirmed gene interference plasmid into HT115 bacteria.
    D.Concentrate the bacteria transformed with the plasmid on an ampicillin-resistant solid culture plate. After overnight incubation at 37℃, pick single clones and use the aforementioned PCR method to confirm again that the gene interference plasmid has been successfully transformed into HT115 bacteria.
35. August 4, 2024

    Participants: Xu Yuesi, Li Xiaoman, Xiao Yanzhi, Jin Ziqing

    A.Learn about conformational search and related algorithms, such as potential energy surface scanning, molecular dynamics, and Monte Carlo algorithms.
    B.Study the common conformational search software CREST (Conformer–Rotamer Ensemble Sampling Tool) and its accompanying XTB algorithm and program.
    C.Further learn about Origin software, including how to batch process data, use templates and themes, and export images.
    D.Continue with the RNAi experiment by recording the numbers of positive clones, inoculating them into liquid LB medium, and incubating them overnight at 37℃ in medium containing 50 μg/mL ampicillin.
36. August 5, 2024

    Participants: Xu Yuesi, Li Xiaoman, Xiao Yanzhi, Jin Ziqing

    A.Delve deeper into using CREST software and its accompanying XTB algorithm and program.
    B.Learn about the concepts and technical points of visualization analysis in non-covalent interactions.
    C.Continue with the RNAi experiment by centrifuging the above RNAi cultures to concentrate them to 20 mg/mL. Inoculate 250 μL of the concentrated culture onto each 6 cm nematode growth medium (NGM) plate. After air-drying the NGM plates in a clean bench, incubate them overnight at 25℃ for later use.
    D.Add 10 mM isopropyl β-D-thiogalactopyranoside (IPTG, from Bioteke Life Sciences Co., Ltd. in China) to the NGM plates. IPTG can induce the expression of dsRNA synthesis of the target gene in the L4440 plasmid within the gene interference bacteria.
    E.When nematodes consume the HT115 bacteria, the L4440 plasmid within their bodies can be induced to express double-stranded DNA of the target interference gene. After degradation by the dicer enzyme in the nematodes, 20-nucleotide double-stranded DNA fragments are formed, which then inhibit the expression of the target gene at the transcriptional level. Feed the interference bacteria to nematodes starting from the L1 stage, while also administering nematodes (control group, xanthohumol treatment group).
37. August 6, 2024

    Participants: Xu Yuesi, Cui Jinming, Li Xiaoman, Xiao Yanzhi, Jin Ziqing, Huang Yijia, Wang Chang, Junqiao Chen

    A.Use CREST software to perform conformational search for the low-energy structures of wgx-50, and optimize the structure of wgx-50 using Gaussian software.
    B.Learn about the principles of Bader's Atoms-in-Molecules (AIM) topology analysis in non-covalent interactions.
    C.Conduct an RT-qPCR experiment by washing the nematodes three times with M9 buffer to collect them from the plates.
    D.Extract total RNA from the nematodes using the RNAiso plus reagent kit (Takara Bio, Japan) as a reference method. Perform quantitative reverse transcription polymerase chain reaction (RT-qPCR) using the Hieff® qPCR SYBR Green Master Mix kit (Yeasen Biotech) to measure the expression levels of multiple genes at the mRNA level in nematode and mouse tissues.
38. August 7, 2024

    Participants: Xu Yuesi, Li Xiaoman, Xiao Yanzhi, Jin Ziqing

    A.Dock the optimized wgx-50 structure with the protein and perform MD (Molecular Dynamics) simulations using Amber software.
    B.Learn about the method for graphical representation of custom inter- and intra-fragment interactions called the independent gradient model based on Hirshfeld partition (IGMH). Study and compare the differences between IGMH and IGM methods for interaction analysis.
    C.Continue with the RT-qPCR experiment, collecting PCR data on the CFX Connect system. Use the 2-ΔΔCT method to calculate the relative expression of genes, with gpd-1 and gapdh serving as internal reference controls.
39. August 8, 2024

    Participants: Xu Yuesi, Li Xiaoman, Xiao Yanzhi, Jin Ziqing, Bai Zikuan, Huang Yijia, Wan Hamo, Wang Chang, Chen Junjiong, Li Yanrong, Wu Jianing, Jin Chenming, Peng Huier, Ma Yueyan

    A.Learn how to determine the protonation states of residue side chains and apply this to determine the protonation states of residues in the studied wgx-50 protein system.
    B.Conduct multiple parallel MD simulations and monitor the progress of the MD simulations.
    C.Perform fluorescence microscopy analysis. All nematodes are cultured at 25°C. Control group: L4 nematodes with 0.1% ethanol. Treatment group: L4 nematodes with 50 μM WGX-50. Harvest nematodes after treating them with WGX-50 (50 μM) for 5 days according to the protocol.
40. August 9, 2024

    Participants: Xu Yuesi, Li Xiaoman, Xiao Yanzhi, Jin Ziqing

    A.Understand the concept of binding free energy.
    B.Learn about popular free energy calculation methods in MD (Molecular Dynamics) simulations, including the Molecular Mechanics combined with Poisson-Boltzmann (PB) or Generalized Born (GB) Surface Area (SA) methods, known as MM/PBSA and MM/GBSA respectively.
    C.Study the nonpolar solvation free energy, which is typically calculated through the solvent-accessible surface area (SA) and is used to describe molecular surface tension and hydrophobic effects.
41. August 10, 2024

    Participants: Xu Yuesi, Li Xiaoman, Xiao Yanzhi, Jin Ziqing

    A.Learn how to add solvent, box, and counter ions, as well as perform restrained dynamics.
    B.Learn how to download MD (Molecular Dynamics) simulation trajectories, specifically the simulation trajectories of the wgx-50 protein complex.
    C.Learn how to calculate binding free energy in Amber.
    D.Understand how to read and interpret the output files for binding free energy, including the meaning of each energy term, such as ELE (electrostatic energy calculated by the molecular mechanics (MM) force field), VDW (van der Waals contribution from MM), and INT (internal energy arising from bond, angle, and dihedral terms in the MM force field).
42. August 11, 2024

    Participants: Xu Yuesi, Huang Yijia, Wan Hamo

    A.Understand the concepts of trajectory analysis, including Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF).
    B.Analyze the trajectories after performing MD (Molecular Dynamics) simulations on the wgx-50 protein complex system.
    C.Learn to use the Log-rank (Mantel-Cox) method for statistical hypothesis testing. For significance analysis between any two groups, a two-tailed unpaired t-test is used. For significance analysis among multiple groups, a one-way ANOVA analysis is employed.
    D.Learn data analysis and plotting using GraphPad Prism software. Data graphs are plotted using GraphPad Prism v9.5 (GraphPad Software, USA) and then arranged and exported using Adobe Illustrator CC (Adobe Systems Inc., USA).
43. August 12, 2024

    Participants: Xu Yuesi, Huang Yijia, Wan Hamo

    A.Calculate the RMSD (Root Mean Square Deviation) values for the wgx-50 protein complex system and create corresponding graphs.
    B.Learn and consider methods to eliminate translational and rotational motions of the system, remove water from the trajectories, and check the distance between the protein and its image.
    C.Continue with the fluorescence microscopy analysis experiment. After treatment, wash the nematodes twice with M9 buffer. Randomly select 10-30 nematodes, transfer them onto a 2% agar pad, anesthetize them with an appropriate amount of 10 mM levamisole solution, and observe the green fluorescence intensity of nematodes in each group under an Inverted and Upright Integrated Fluorescence Microscope (Revolve, ECHO Company).
    D.Use ImageJ software to quantitatively analyze the fluorescence intensity.
44. August 13, 2024

    Participants: Xu Yuesi, Bai Zikuan, Huang Yijia, Wan Hamo, Wang Chang, Chen Junqiao, Li Yanrong, Wu Jianing, Jin Chenming, Peng Huier, Ma Yueyan

    A.Calculate the RMSF (Root Mean Square Fluctuation) values for the wgx-50 protein complex system and create corresponding graphs.
    B.Learn and understand popular free energy calculation methods in MD simulations, including the MM/GBSA method. This includes preparing topology files (e.g., complex.prmtop) for the protein-ligand complex, understanding the factors that influence binding energy, and learning about residue-based energy decomposition methods.
    C.Calculate the binding free energy of the wgx-50 protein complex.
45. August 14, 2024

    Participants: Xu Yuesi, Huang Yijia, Wan Hamo

    A.Learn cluster analysis and PCA (Principal Component Analysis), and apply these methods to the wgx-50 protein complex system to identify representative structures during MD simulations. Utilize software such as Pymol for structure visualization.
    B.Calculate the radius of gyration values for the wgx-50 protein complex system and create corresponding graphs.
    C.Continue learning and familiarizing with Pymol software.
46. August 15, 2024

    Participants: Xu Yuesi, Huang Yijia, Wan Hamo, Cui Jinming, Li Xiaoman, Xiao Yanzhi, Jin Ziqing, Wang Chang, Chen Junqiao

    A.Learn how to use Multiwfn for IGMH (Independent Gradient Model based on Hirshfeld partitioning) analysis to clearly and intuitively visualize interactions in chemical systems. Study quantitative analysis of the contributions of atoms and atom pairs to these interactions.
    B.Analyze which residues of wgx-50 and the protein form interactions, and use the IGMH method to analyze the formation of non-covalent interactions.
    C.Continue learning data analysis and plotting using GraphPad Prism software for creating figures and tables for research papers.
47. August 16, 2024

    Participants: Xu Yuesi, Huang Yijia, Wan Hamo

    A.Learn the RDG (Reduced Density Gradient) method for visualizing and quantifying non-covalent interactions. Study how to use RDG isosurfaces to display weak interaction regions and distinguish the strength and type of weak interactions.
    B.Apply the RDG method to the wgx-50 protein system.
48. August 17, 2024

    Participants: Xu Yuesi, Huang Yijia, Wan Hamo

    A.Learn to create filled RDG (Reduced Density Gradient) scatter plots for weak interaction analysis. Study the gnuplot software required for plotting and understand its basic operation procedures.
    B.Continue learning data analysis and plotting using GraphPad Prism software for creating figures and tables for research papers.
    C.Continue learning the use of the plotting software Origin.
49. August 18, 2024

    Participants: Yijia Huang, Jinming Cui, Yuesi Xu, Jingchen Shi, Ziqing Jin, Yufan Dong, Andi Hu, Hamo Wan, Haonan Deng, Yihao Tao, Jeffrey (Jiarui) Shen, Haonan Deng

    A.Further study how to analyze the state of ligands within proteins and investigate the role and function of water molecules in molecular recognition. Certain water molecules at binding sites may play crucial roles in the binding between ligands and receptors, so it is necessary to consider whether the ligand interacts appropriately with these water molecules.
    B.Apply this type of analysis to the wgx-50 system.
50. August 19, 2024

    Participants: Jinming Cui, Yuesi Xu, Ziqing Jin, Yufan Dong, Andi Hu, Hamo Wan, Haonan Deng, Yihao Tao, Jeffrey (Jiarui) Shen, Chenming Jin

    A.Learn the key points of writing an introduction, study how to read and summarize previous literature, and clarify the research background and its significance.
    B.Learn to write a brief literature review: summarize the main research progress in the related field, study how to cite several representative or recent research achievements, and present the development process and current knowledge level in the field.
51. August 20, 2024

    Participants: Xu Yuesi, Cui Jinming, Li Xiaoman, Xiao Yanzhi, Jin Ziqing, Huang Yijia, Wang Chang, Chen Junqiao

    A.Investigate the skeletal motion characteristics during the simulation process, the stability of conformations in different regions, and the hydrogen bonding situation in the system.
    B.Continue learning data analysis and plotting using GraphPad Prism software for creating figures and tables for research papers.
    C.Continue learning the use of the plotting software Origin.
52. August 21, 2024

    Participants: Yijia Huang, Jinming Cui, Yuesi Xu, Ziqing Jin, Yufan Dong, Andi Hu, Hamo Wan, Haonan Deng, Yihao Tao, Jeffrey (Jiarui) Shen, Chenming Jin

    A.Read typical literature on MD simulations to understand the key points of writing about MD simulation and interaction analysis techniques, especially the generation of topology files, selection of force fields, and the process of establishing simulation systems.
    B.Continue learning and practicing the use of Adobe Illustrator software for creating figures and tables for research papers.
53. August 22, 2024

    Participants: Xu Yuesi, Bai Zikuan, Huang Yijia, Wan Hamo, Wang Chang, Chen Junjiao, Li Yanrong, Wu Jianing, Jin Chenming, Peng Huier, Ma Yueyan

    A.Learn scientific paper writing, specifically how to propose research innovations and elaborate on the innovative aspects of your research compared to existing work.
    B.Reflect on the research methods, theoretical models, additions to existing theories, or new solutions to practical problems used in this project.
54. August 23, 2024

    Participants: Yijia Huang, Jinming Cui,Yuesi Xu, Ziqing Jin, Yufan Dong, Andi Hu, J Hamo Wan, Haonan Deng

    Summarize and organize experimental results, contemplate the potential contributions or application values of this study, and emphasize its importance and potential impact on the field.
55. August 24, 2024

    Participants: Zikuan Bai, Yijia Huang, Yuesi Xu, Huier Peng, Jianing Wu, Hang Su, Chengjun Jiang, Hamo Wan, Jeffrey (Jiarui) Shen, Zhuxu Zhang, Junqiao Chen, Yanrong Li

    Write the abstract of the paper: Briefly summarize the research purpose, methods, results, and conclusions. It should be clear and concise, typically 150-250 words, highlighting the paper's innovation and main findings.
56. August 25, 2024

    Participants: Zikuan Bai, Yijia Huang, Yuesi Xu, Huier Peng, Jianing Wu, Hang Su, Chengjun Jiang, Hamo Wan, Jeffrey (Jiarui) Shen, Zhuxu Zhang, Junqiao Chen, Yanrong Li,

    Continue summarizing and organizing experimental results, contemplating the potential contributions or application values of this study, and emphasizing its importance and potential impact on the field.
57. August 26, 2024

    Participants: Zikuan Bai, Yijia Huang, Yuesi Xu, Huier Peng, Jianing Wu, Hang Su, Chengjun Jiang, Hamo Wan, Jeffrey (Jiarui) Shen, Zhuxu Zhang, Junqiao Chen, Yanrong Li, Chang Wang, Yueyan Ma

    Write the methods section of the paper: Describe the experimental or research methods in detail to ensure the reproducibility of the study. Include research design, experimental materials, data collection, and analysis methods.
58. August 27, 2024

    Participants: Xu Yuesi, Bai Zikuan, Huang Yijia, Wan Hamo, Wang Chang, Chen Junjiao, Li Yanrong, Wu Jianing, Jin Chenming, Peng Huier, Ma Yueyan

    A.Refine the scientific paper writing.
    B.Write the results section: Objectively present the main findings of the study, including data, charts, and statistical analysis.
59. August 28, 2024

    Participants: Zikuan Bai, Yijia Huang, Yuesi Xu, Huier Peng, Jianing Wu, Hang Su, Chengjun Jiang, Hamo Wan, Jeffrey (Jiarui) Shen, Zhuxu Zhang, Junqiao Chen, Yanrong Li, Chang Wang, Yueyan Ma

    A.Refine the scientific paper writing.
    B.Finalize the results section: Objectively present the main findings of the study, including data, charts, and statistical analysis.
    C.Conduct analysis of MD simulation results.
60. August 29, 2024

    Participants: Xu Yuesi, Cui Jinming, Li Xiaoman, Xiao Yanzhi, Jin Ziqing, Huang Yijia, Wang Chang, Chen Junqiao

    A.Refine the scientific paper writing.
    B.Write the discussion section: Interpret the research results, compare similarities and differences with other studies, discuss the limitations of the study, and propose directions for future research.
61. August 30, 2024

    Participants: Zikuan Bai, Yijia Huang, Yuesi Xu, Huier Peng, Jianing Wu, Hang Su, Chengjun Jiang, Hamo Wan, Jeffrey (Jiarui) Shen, Zhuxu Zhang, Junqiao Chen, Yanrong Li

    A.Refine the scientific paper writing.
    B.Supplement and complete the references section: List all cited literature, ensuring the format meets the requirements of the target journal.
    C.Polish the language of the paper.
62. August 31, 2024

    Participants: Zikuan Bai, Yijia Huang, Yuesi Xu, Huier Peng, Jianing Wu, Hang Su, Chengjun Jiang, Hamo Wan, Jeffrey (Jiarui) Shen, Zhuxu Zhang, Junqiao Chen, Yanrong Li, Chang Wang, Yueyan Ma

    A.Refine the scientific paper writing, such as avoiding excessive citations: While it is necessary to cite relevant literature, the introduction is not a literature review. Citations should revolve around the research question, avoiding the accumulation of references.
    B.Polish the language of the paper.

{% endblock %}

{% extends "layout.html" %}
{% block title %}Engineering Success{% endblock %}
{% block lead %}{% endblock %}
{% block page\_content %}

* Overview
* Iteration 1
* Iteration 2
* Iteration 3
* Iteration 4
* Conclusion
* Reference

Overview

Global aging poses threats to nations, families, public health, and economies. Alzheimer’s Disease (AD) is one of the age-related diseases that is a major cause of death among the elderly.[1][2] Healthy aging is a genuine need of people as outlined in the Universal Declaration of Human Rights.[3] Our team tested WGX50 using *C. elegans* as a model organism to examine its unexplored anti-aging effect, finding that it enables HSPs longevity pathway, and therefore increased lifespan of w.t worms.

WGX50 is a small molecule entity extracted from Sichuan pepper. WGX50 inhibits amyloid β-protein (Aβ)-induced neuroinflammation in microglia by activating the JAK2/STAT3 and PI3K/AKT pathways through 7nAChR. It shows promise as a potential molecular compound for treating AD. As a novel candidate drug for AD treatment, WGX50 can directly reduce the accumulation of Aβ oligomers in the cerebral cortex and protect neurons in primary microglia.

Iteration 1

Design 1

To investigate whether WGX-50 could prolong lifespan, we conducted a longevity test in *C. elegans*. Strain Selection: use wild-type *C. elegans* (e.g., N2 strain) and, optionally, mutants with defects in antioxidant pathways (e.g., sod-1 mutants, daf-16 mutants). Age-synchronized populations are important for consistency. Perform synchronization by isolating eggs through hypochlorite treatment or using timed egg laying.

Antioxidant Selection: Choose the antioxidants of interest, such as ascorbic acid (vitamin C), vitamin E, N-acetylcysteine (NAC), or synthetic antioxidants like Trolox.

Prepare stock solutions of the antioxidants and ensure proper solubility in water or appropriate solvent. Use different concentrations of antioxidants for dose-response analysis.

Build 1

Nematode Growth and Maintenance: Grow nematodes on NGM (Nematode Growth Medium) plates seeded with Escherichia coli OP50 as a food source. We keep the nematodes at a standard temperature (typically 20°C for *C. elegans*) throughout the experiment to avoid temperature-related bias.

WGX-50 was offered by Prof. Dr. Dongqing Wei's laboratory at Shanghai Jiao Tong University. In *C. elegans* experiments, WGX-50 was dissolved in sterilized and purified water containing 0.1 % of ethanol (Sangon Biotech, China). The w.t Bristol N2 *C. elegans* and transgenic CL2070 strain (dvIs70 [hsp-16.2p::GFP + rol-6(su1006)], referred as hsp-16.2::GFP) were obtained from the Caenorhabditis Genetic Center (CGC, <https://cgc.umn.edu/>). Escherichia coli strain OP50 (CGC, USA) was aerobically cultured overnight at 37°C in 200 mL of Luria-Bertani (LB) media. Aliquots of concentrated OP50 (20 mg/mL, 250 L) for each 6 mm nematode growth media (NGM) petri plate were seeded, air dried, and used as food source. Unstarved at least three generations of populations were ramp up, synchronized (the sodium hypochlorite method), maintained on NGM plates containing 10 μM of antifungal nystatin (Sangon Biotech, China) with standard techniques at 15 or 20 °C until they developed to the fourth larval stage (L4). Afterwards, they were transferred to desired conditions for assays.

![](https://static.igem.wiki/teams/5496/images/engineering-01.png)

**Figure 1. The molecular structure of WGX-50**

Test 1

In the experimental setup for survival analysis, we divide nematodes into different treatment groups. As for control group: Nematodes grown under normal conditions without antioxidant treatment. Antioxidant-treated groups: Nematodes treated with different concentrations of the selected antioxidant. Positive control (optional): A group exposed to known pro-oxidative stress (e.g., paraquat) to confirm that the antioxidant protects from oxidative stress.

Statistical Analysis: Use Kaplan-Meier survival analysis to generate survival curves. Perform statistical tests (e.g., log-rank test) to compare the survival curves between different treatment groups. Additionally, perform dose-response analysis to determine the optimal concentration of the antioxidant. If studying stress resistance, calculate the LT50 and compare between groups. Statistical significance between two groups were determined by unpaired t test (two-tailed). Pre-requested normality and homoscedasticity were assumed to be fitted, considering the small sample size (n < 30). Statistical information such as sample size, p-values, and the detailed methods used for statistical inference can be found in the descriptions for figures and tables.

In longevity experiments, w.t N2 worms were allowed to grow to adulthood before exposure to any stimuli (i.e., drugs or RNAi) to eliminate the contribution of any developmental effects on lifespan. Embryos were synchronized and allowed to develop on NGM plates for about 3 days to L4 stage at 20 °C. Adult animals were then transferred to new plates that had been coated with WGX-50 or vehicle with overnight culture of fresh droplets of *E. coli* OP50 or RNAi feeding bacteria. Growing adult worms at 20°C were scored and well maintained every other day. Animals were judged as dead when they ceased pharyngeal pumping and did not respond to prodding with a platinum wire. Worms that crawled off the plate, exploded, or died from internal hatching were excluded from analysis.

Learn 1

Extended Lifespan: If the antioxidant is effective, treated nematodes are expected to exhibit an increased lifespan compared to the control group. A dose-response relationship may emerge, with an optimal antioxidant concentration leading to maximum lifespan extension, while too high concentrations might be toxic.

As a result, WGX-50 significantly prolonged the lifespan of *C. elegans* and the best lifespan extension effect was observed at a concentration of 50 μM (Figure 2b). Interestingly, in accordance with our study, WGX-50 extended lifespan in w.t *C. elegans* (dose range tested, 3.75 ~ 250 μM), in which the dose 15 μM increased the median survival to 45% (from 22 to 32 days, Figure 2b) at 20 °C. To this end, we concluded that WGX-50 prolongs lifespan in *C. elegans*.

![](https://static.igem.wiki/teams/5496/images/engineering-02.png)

**Figure 2: WGX-50 extends lifespan in N2 *C. elegans*.** (a): WGX-50 was previously characterized as a novel drug candidate for AD, which is a natural product derived from Zanthoxylum bungeanum Maxim. (Image credit: Royalty-free photo from <https://create.vista.com/>, slightly changed by the authors). (b): WGX-50 extends lifespan in N2 nematodes at 20 °C (b, n > 280 worms per condition).

Iteration 2

Design 2

Four novel genes take no roles in WGX-50 mediated longevity owing to a combinatorial effect enabled by the drug. To decipher the mechanistic pathways behind the detected phenotypes, we performed RNA sequencing (RNA-seq) for adult worms treated with WGX-50 or vehicle. Designing an RNA sequencing (RNA-seq) experiment to study antioxidant activity in Caenorhabditis elegans involves several key steps. To assess changes in gene expression in *C. elegans* exposed to oxidative stress and to identify antioxidant-related pathways. We set up two groups, as control Group: *C. elegans* maintained under standard conditions without oxidative stress. As treatment Group(s): *C. elegans* exposed to an oxidizing agent (e.g., hydrogen peroxide, paraquat) at various concentrations and time points. Replicates: Include at least three biological replicates for each group to ensure statistical validity. Time Points: Collect samples at multiple time points (e.g., 0, 6, 12, 24 hours) after exposure to the oxidative agent to capture dynamic gene expression changes.

Build 2

RNA sequencing strandedness allows researchers to determine which DNA strand (sense or antisense) a transcript came from. Compared to regular RNA sequencing methods, stranded RNA sequencing can find novel transcripts, distinguish transcripts from overlapping genes, find antisense sequences, and annotate genes.

Cultivation of *C. elegans*: Grow *C. elegans* on agar plates with standard growth media (Nematode Growth Medium). Use a synchronous population to ensure developmental stage uniformity (e.g., using eggs or L1 larvae).

Oxidative Stress Treatment: Expose *C. elegans* to selected oxidative agents at different concentrations and time points. Control environmental factors such as temperature and humidity during treatment.

Sample Collection: At designated time points, wash worms with M9 buffer to remove any residual oxidative agents. Snap-freeze worms in liquid nitrogen for RNA extraction.

RNA Extraction: Use a reliable RNA extraction kit (e.g., TRIzol or a column-based method) to isolate total RNA from the samples. Assess RNA quality and quantity using a spectrophotometer and gel electrophoresis.

Library Preparation: Prepare RNA-seq libraries using a suitable library preparation kit (e.g., Illumina TruSeq). Follow the manufacturer's protocol for poly(A) enrichment or rRNA depletion based on the RNA type. Sequencing: Sequence the libraries using an Illumina platform (e.g., HiSeq, NovaSeq) to obtain paired-end reads.

Data Processing: Perform quality control on raw sequencing data (using tools like FastQC). Trim adapters and low-quality bases using tools like Trimmomatic or Cutadapt. Align reads to the *C. elegans* reference genome using a suitable aligner (e.g., STAR, HISAT2).

Differential Expression Analysis: Use software like DESeq2 or EdgeR to identify differentially expressed genes (DEGs) between control and treatment groups. Apply appropriate statistical methods to assess significance (e.g., false discovery rate correction). Functional Annotation: Annotate DEGs using gene ontology (GO) enrichment analysis and pathway analysis tools (e.g., DAVID, KEGG). Identify antioxidant-related pathways and genes upregulated or downregulated in response to oxidative stress.

![](https://static.igem.wiki/teams/5496/images/engineering-03.png)

**Figure 3: Adult N2 *C. elegans* were treated with 50 M WGX-50 or control (Ctrl) for 5 days at 25°C, collected worms were subjected to RNA-seq analysis.** Volcano plot of differentially expressed genes (DEGs) in w.t *C. elegans* (n = 3). DEGs were selected based on predefined cutoff as p-adj < 0.05 and fold change (FC) ≥ 1.5 (dash-dotted line, vertical). Four down-regulated genes and some up-regulated genes (FC < 2) are in black. Genes with more than 4-fold changes are labeled with gene symbols. Colored dots indicate genes that had more than 2-fold changes varying at significance levels (red, p-adj < 0.05; gold, p-adj < 0.01; cyan, p-adj < 0.001). \* p < 0.05, \*\* p < 0.01, \*\*\* p < 0.001 and \*\*\*\* p < 0.0001; ns or no indication, not significant.

Test 2

We analyzed the distribution of 434 transcriptional genes as illustrated in heatmap. Next, RNA interference (RNAi) was conducted to investigate the unexplored role of these genes in WGX-50 mediated longevity. RNAi is among the processes that use short RNAs as guides for sequence-specific silencing; these pathways are together referred to as RNA silencing pathways. Long double-stranded RNA causes sequence-specific mRNA destruction, which is known as RNAi. This phenomenon was first identified in *C. elegans*.

![](https://static.igem.wiki/teams/5496/images/engineering-04.png)

**Figure 4: Overview of RNA-seq data as illustrated in Figure 3.** (a): Heatmap of the expression pattern of 434 genes that are significantly induced over two folds (p-val < 0.05) in adult animals by WGX-50 (n = 3). Genes (in columns) are colored for replicates (in rows) based on their Z-scores of FPKM. b): Heatmap of Spearman’s correlation of whole-worm gene expression in 6 biological samples across two groups (WGX-50 vs. Ctrl, n = 3) showing strong correlations (> 0.8). c-d): The overall gene expression for all samples are similar as demonstrated in FPKM violin (c) and box plots (d). Gene density is shown by the violin’s width. (e): Principal component analysis of global gene expression displays discrete separation between the two groups.

Learn 2

As results show, WGX-50 medicated survival increase was not diminished by RNAi any of these genes (compared to OP50). Thus, we concluded that these novel genes are not causative for WGX-50 mediated longevity. To be noted, in terms of RNAi sri-40 and K09C6.9, although they were tested to be significant, it’s not eligible to be considered plausibly as causality since changes in median survival were slight.

Iteration 3

Design 3

Develop a research strategy, including selecting appropriate protein targets, initial binding sites, force field parameters, experimental conditions, etc. In this process, researchers need to make key decisions and set parameters for MD simulations.

3.1 Select protein targets

Determine the target protein that interacts with the small molecule WGX-50. The target protein can be determined by literature review or database screening.

3.2 Prepare the initial structure

Obtain the crystal structure of the protein from the PDB database to ensure its high resolution.

Small molecule structure: WGX-50 is geometrically optimized using quantitative chemistry software (such as Gaussian) to obtain its low-energy stable conformation. A preliminary charge distribution can be generated using methods such as AM1-BCC.

3.3 Preliminary binding mode prediction

Molecular Docking: Use docking tools (such as AutoDock Vina, Schrödinger's Glide, MOE, etc.) to dock WGX-50 to the active site of the protein to generate an initial binding conformation. The conformation with the lowest docking energy can be used as the starting point for MD simulations.

Binding site selection: If there is a known ligand or substrate in the protein structure, docking can be performed at the active site; otherwise, potential binding pockets are predicted by molecular docking software.

3.4 Select force field parameters

Load the AMBER ff14S force field on the protein. The force field parameters of the small molecule WGX-50 need to be generated additionally. You can use Antechamber (AMBER force field) to generate the adapted parameters.

Build 3

In this stage, the designed structure and parameters are integrated into the molecular dynamics simulation environment, and the input files required for MD simulation are prepared.

3.1 System construction

Complex modeling: Import the WGX-50 and protein complex obtained by molecular docking into the MD simulation software (such as GROMACS, AMBER, NAMD, etc.). Ensure that the binding conformation of the complex is consistent with the previous docking results.

Add ions: Neutralize the total charge of the system, usually adding Na+ or Cl- ions. If necessary, 0.15 M NaCl can be added to simulate physiological conditions.

Solvation: Place the complex in a water solvent box and use the TIP3P water model. The water box should be large enough, usually 10-15 Å away from the protein surface.

3.2 Energy Minimization

Energy minimization is performed on the entire system to eliminate geometric collisions and unreasonable atomic arrangements. This is a key pre-processing step for MD simulation. By optimizing the geometric structure of the system, the stability of MD operation is ensured.

3.3 Heating and Equilibrium

Heating: Gradually heat the system to the target temperature (usually 300 K) to avoid structural instability caused by rapid heating by restricting the movement of proteins and small molecules. Equilibrium: Run equilibrium simulations under isothermal and isobaric (NPT) or isothermal and isovolumetric (NVT) conditions to make the system reach a thermodynamically stable state. The equilibrium time is generally from a few hundred picoseconds (ps) to several nanoseconds (ns), depending on the complexity of the system.

Test 3

The test phase is the core simulation process. Through MD simulation, the interaction between the small molecule WGX-50 and the protein is observed and trajectory data is generated.

3.1 Production Phase MD Simulation

Production simulation: MD simulations are usually run on a time scale of nanoseconds to microseconds. During this process, the motion trajectories of all atoms are recorded to reveal the dynamic behavior of the small molecule WGX-50 in the protein binding pocket. The simulation conditions are usually NPT ensemble (isothermal and isobaric), 300 K temperature and 1 atm pressure. The commonly used time step is 2 femtoseconds (fs), and a constraint algorithm (such as LINCS) is required to handle bond length constraints, especially hydrogen bonds.

3.2 Data Analysis

Trajectory analysis: Use MD simulation software or related analysis tools (such as VMD, PyMOL, cpptraj) to analyze the simulation trajectory. Common analyses include:

RMSD (root mean square deviation): evaluate the changes in protein structure and complex relative to the initial structure to detect the stability of the system.
RMSF (root mean square fluctuation): analyze the flexibility of each residue of the protein to determine the effect of WGX-50 binding on the protein conformation.

Hydrogen bond analysis: study the hydrogen bond interaction between WGX-50 and protein to determine the key binding site.

Dynamic changes in binding pocket: observe the conformational changes of the binding site to understand the stability of the binding mode and possible pocket adjustments.

3.3 Binding free energy estimation

MM/PBSA or MM/GBSA method: Based on the MD trajectory, the binding free energy of the protein-small molecule complex can be estimated. This helps to evaluate the binding strength of WGX-50 to the protein and provide guidance for further optimization of the small molecule structure.

Free energy calculation not only provides quantitative binding energy information, but also reveals key energy contributions (such as electrostatics, van der Waals, and solvation energy) in the binding process.

![](https://static.igem.wiki/teams/5496/images/engineering-05.png)

**Figure: The conformation structure analysis of wgx50-enzyme system.** (a): Root-mean-square-deviation (RMSD) of the backbone carbon atoms of WGX-50-HSP90 complex. The MD simulations were run in parallel for 3 times. (b): The compactness of the complex was measured by the radius of gyration (Rg). (c): RMSF analysis.

Learn 3

In the learning stage, by analyzing the results of MD simulations, understand the interaction mode, use wave function analysis methods such as IGMH and QTAIM to visualize and quantify non-covalent interactions, and design new experiments or simulations to verify and improve the binding model.

3.1 Summarize the analysis results

Based on the trajectory analysis and free energy calculation results, summarize the binding mode of WGX-50 to the protein. Confirm the key amino acid residues and non-covalent interactions (such as hydrogen bonds, hydrophobic interactions, -π interactions, etc.) involved in the binding pocket. Confirm the stability of the small molecule in the binding pocket and evaluate the diversity and dynamic characteristics of the binding mode.

3.2 non-covalent interaction analysis

Across three-dimensional coordinates, using quantum chemistry and wave function analysis, visualize the interaction area and find the energy of the interaction

3.3 Feedback to the next cycle

Based on the results of MD simulation, propose new small molecule designs or protein modification strategies. These new designs will re-enter the design stage of the DBTL process and continue to optimize the binding model and reaction mechanism through iterative cycles.

![](https://static.igem.wiki/teams/5496/images/engineering-06.png)

**Figure Noncovalent interaction analysis of WGX-50-HSP90 complex.** (a) The key residues of HSP90 that interacted with WGX-50 were shown as sticks. Hirshfeld partition of molecular density (IGMH) analysis (b), and quantum theory of atoms in molecules (AIM) analysis of the complex(c). The blue region represents attractive van del Waals. Orange spheres show the BCPs. (d) Color-filled RDG scatter plot in the above model calculations.

Iteration 4

The DBTL process can effectively help us understand the mechanism of chemical reactions from the beginning, including the determination of reaction sites, the search for transition states, and the calculation of reaction energy barriers. The following describes in detail how to use DFT and Fukui functions to explore the reaction between WGX-50 and hydroxyl radicals and clarify its antioxidant mechanism

Design 4

This stage mainly uses theoretical methods to determine the sites where WGX-50 may react with hydroxyl radicals, and determine the calculation strategy, functional, basis set and related models.

4.1 Structural optimization of compound WGX-50

Select DFT functional and basis set: Generally speaking, B3LYP or ωB97X-D are commonly used functionals, which are suitable for the calculation of organic molecules and free radical reactions. The optimized basis set selects 6-311G(d,p), and def2tzvp is used for single point energy calculation.

4.2 Calculation of Fukui function and prediction of reaction site

The Fukui function uses frontier molecular orbital theory to help determine the active sites in molecules that are prone to electrophilic, nucleophilic or free radical reactions. By calculating the frontier molecular orbital (FMO), the HOMO (highest occupied molecular orbital) and LUMO (lowest unoccupied molecular orbital) on WGX-50 can be determined, and the corresponding reaction sites can be determined.

4.3 Preliminary prediction of free radical reaction sites

Through frontier molecular orbital and Fukui function analysis, the sites in WGX-50 that are most likely to react with hydroxyl radicals are determined. This may be the area with the highest electron density or the largest HOMO contribution, such as hydroxyl groups with active hydrogen or carbon atoms with dense electron clouds on aromatic rings.

At this stage, combined with empirical chemical knowledge and Fukui function results, the sites that hydroxyl radicals may attack can be determined.

Build 4

This stage constructs the preliminary results of the design stage into a specific reaction model, and prepares transition state search and reaction path calculation.

4.1 Build a complex of WGX-50 and hydroxyl radicals

Place WGX-50 and •OH radicals near the corresponding reaction sites and adjust the geometric configuration reasonably. At this point, it may be necessary to try different initial reactant geometries multiple times. For electrophilic reactions, •OH can be placed close to high electron density sites on WGX-50 (such as aromatic rings or near hydroxyl groups).

4.2 Preliminary transition state (TS) configuration guessing

Based on the preliminary configurations of reactants and products, guess the transition state of the reaction. We take the radical close to the reaction site and create a reasonable transition configuration as a starting point, and manually adjust the distance on the reaction path (such as the distance between the radical and the reaction site) to form the initial guess structure of the transition state.

4.3 Force constant calculation and transition state optimization

Use the opt=(ts, calcfc) command in Gaussian to optimize the transition state. ts means transition state search, and calcfc means optimization using the initial force constant matrix. This step helps us find the transition state of the reaction and determine its energy extreme value.

4.4 Intrinsic reaction coordinate (IRC) calculation, in order to confirm that the found transition state correctly connects the reactants and products, an IRC calculation can be performed. This can help understand whether the reaction path is correct and the rationality of the transition state.

Test 4

Perform calculations and analysis to obtain energy information, reaction mechanism and related kinetic parameters of free radical reactions.

Analyze the energy barrier (Activation Energy, ΔE‡) and reaction enthalpy change (ΔH) of the reaction by calculating the electronic energy of reactants, transition states and products. The energy barrier is determined by the energy difference between the transition state and the reactants.

Analyze the thermodynamic potential of the reaction by Gibbs free energy. If a frequency calculation is performed, Gaussian will automatically give the free energy G, which can be used to evaluate the reversibility and kinetic rate of the reaction.

We can further analysis of the reaction mechanism. Based on the IRC trajectory, the various stages of the reaction path can be analyzed to confirm the reaction mechanism. The reaction mechanism usually includes details such as structural changes between reactants, transition states and products, electron transfer, bond formation and breaking. The Fukui function can also continue to help explain which atoms contribute to the main reaction activity.

![](https://static.igem.wiki/teams/5496/images/engineering-07.png)

**Figure:** DFT calculation of WGX-50 reacted with •OH radicals. (a) Fukui function analysis results of isosurfaces value of ±0.006 (blue, negative; green, positive). The TS structure of HAT reaction (b, c) and RAF reaction (d, e) were shown. (f) The relative free energy profile was calculated. Energies are in kcal/mol.

Learn 4

In the learning stage, through the analysis of test results, new theoretical understanding is obtained, the calculation model is optimized and the next round of calculations or experimental verification is planned.

4.1 Summarize the reaction active sites and energy changes

Summarize the Fukui function, reaction site and transition state information to confirm which site of WGX-50 is most likely to react with hydroxyl radicals. Analyze whether it is consistent with the predictions in the design stage. If the reaction energy barrier is too high, it can be inferred that the reaction is difficult to occur, and then consider different reaction sites or free radical attack paths.

4.2 Propose new hypotheses and optimize the design

If the transition state or energy barrier is not ideal, it may be necessary to return to the design stage to modify the reaction model and try other possible reaction sites. For example, test the reactivity of other small molecule regions, or consider other reaction conditions (such as different solvents or pH values). Through the summary of the learning stage, propose new hypotheses and feedback to the next round of DBTL cycle.

4.3 Propose further experimental verification

Based on the DFT calculation results, design corresponding experiments to verify the reaction of the reaction site of WGX-50 with free radicals. The reaction products can be detected by experimental techniques such as mass spectrometry and nuclear magnetic resonance (NMR) to verify the predictions of theoretical calculations.

Conclusion

In vitro computational chemistry research is closely combined with in vivo biological experiments to clarify the antioxidant capacity and biological mechanism of WGX-50 through a multi-level research framework. This multidisciplinary DBTL process can help us guide the design of biological experiments through DFT calculation results, and the results of biological experiments can in turn verify or correct theoretical predictions.

Reference

[1]. Hernandez-Segura, A., Nehme, J. & Demaria, M. Hallmarks of cellular senescence. Trends Cell Biol. 28, 436–453 (2018).

[2]. Cox, L. S. Therapeutic approaches to treat and prevent age-related diseases through understanding the underlying biological drivers of ageing. J. Econ. Ageing 23, 100423 (2022).

[3]. The Lancet Healthy Longevity. Human rights for healthy longevity. Lancet Heal. Longev. 4, e517 (2023).

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* 1.Chemical compound
* 2.Animals
* 3.Survival analysis
* 4.RNA-seq
* 5.RNA interference in *C. elegans*
* 6.RT-qPCR (Real-time polymerase chain reaction)
* 7.Fluorescence microscopy
* 8.Target protein modelling
* 9.Molecular dynamics simulations
* 10.Noncovalent interaction analysis
* 11.Statistical analysis
* Reference

1.Chemical compound

WGX-50 was offered by Prof. Dr. Dongqing Wei's laboratory at Shanghai Jiao Tong University (SJTU, Shanghai, China). In *C. elegans* experiments, WGX-50 was dissolved in sterilized and purified water containing 0.1% of ethanol (Sangon Biotech, China). Other chemicals with source records were clearly described elsewhere.

2.Animals

Although experiment with a mammalian model is compelling, it is time-consuming and limited by the presence of ethical concerns. *C. elegans* has been proved as a reasonable model organism for biological research on aging because of its advantageous features.[1] Although its anatomical structure is simple, the tissues and organs, such as muscles, nervous system, gastrointestinal tract, and gonads of *C. elegans*, are similar to that of higher animals.[2] In addition, its complete genome sequence is available, and about 50% of human protein-coding sequences have identifiable homologous genes in nematodes. Similar to humans and other higher mammals, its behavior changes and descending physiological indexes are accompanied by aging. Moreover, there are highly evolutionary conserved mechanisms controlling physiological phenomena, such as development, aging, and disease. Homologous or functionally similar forms of the main enzymes, genes, and transcription factors involved in metabolism have been found in higher animals and *C. elegans*.[3] No ethics required for the nematodes. The w.t Bristol N2 *C. elegans* and transgenic CL2070 strain (dvIs70 [hsp-16.2p::GFP + rol-6(su1006)], referred as hsp-16.2::GFP) were obtained from the Caenorhabditis Genetic Center (CGC, <https://cgc.umn.edu/>). Escherichia coli strain OP50 (CGC, USA) was aerobically cultured overnight at 37°C in 200 mL of Luria-Bertani (LB) media. Aliquots of concentrated OP50 (20 mg/mL, 250 L) for each 6 mm nematode growth media (NGM) petri plate were seeded, air dried, and used as food source. Unstarved at least three generations of populations were ramp up, synchronized (the sodium hypochlorite method), maintained on NGM plates containing 10 µM of antifungal nystatin (Sangon Biotech, China) with standard techniques at 15 or 20 °C until they developed to the fourth larval stage (L4). Afterwards, they were transferred to desired conditions for assays.

3.Survival analysis

In longevity experiments, w.t N2 worms were allowed to grow to adulthood before exposure to any stimuli (i.e., drugs or RNAi) to eliminate the contribution of any developmental effects on lifespan. Lifespan assays were performed essentially as described previously.[4] Embryos were synchronized and allowed to develop on NGM plates for about 3 days to L4 stage at 20 °C. Adult animals were then transferred to new plates that had been coated with WGX-50 or vehicle with overnight culture of fresh droplets of E. coli OP50 or RNAi feeding bacteria. Growing adult worms at 20°C were scored and well maintained every other day. Animals were judged as dead when they ceased pharyngeal pumping and did not respond to prodding with a platinum wire. Worms that crawled off the plate, exploded, or died from internal hatching were excluded from analysis. Statistics for survival curves were made as described in statistical analysis part.

4.RNA-seq

Day 5 adult nematodes were collected from NGM plates by washing with fresh M9 three times after being treated with 50 µM of WGX-50 (the effective dose identified in Jia et al, manuscript in preparation) or control (n = 3 biological replicates per group). To avoid any phenotypic changes associated with germline ablation, we didn't use chemosterilant, but daily transferred the adults to fresh plates throughout the study. Supernatant of pelleted worm samples was aspirated away. Liquid nitrogen frozen animals were homogenized in TRIzol (Thermo Fisher Scientific, USA), and total RNA was extracted following the manufacturer's instructions. Nanodrop 2000 (Thermo Fisher Scientific, USA) was used to determine the concentration and purity of RNA samples. RNA-seq was conducted at TIANGEN Biotech (Beijing, China).

RNA-seq was conducted at TIANGEN Biotech (Beijing, China). Briefly, Agilent 2100 Bioanalyzer and RNA nano 6000 assay kit (Agilent Technologies, USA) were firstly used to evaluate RNA integrity. Poly-A mRNA was enriched by TIANSeq mRNA capture kit (TIANGEN, China) with designed Oligo-(dT) magnetic beads. Transcriptome RNA-seq libraries were then generated by TIANSeq Fast RNA library kit (Illumina) (TIANGEN, China). Following, poly-A mRNA samples were routinely subjected to random fragmentation, cDNA strands synthesis, end repairing, adapter ligation as standard methods of the service center. The prepared library (activity > 2 nM, by qPCR) was diluted to 1 ng/µL using Qubit 2.0 fluorometer (Life Technologies, USA). Insert size was checked on an Agilent 2100 Bioanalyzer. The clonal clusters of the index-coded samples were bridge amplified on a cBot Cluster generation system (Illumina, USA) using TruSeq PE cluster kit v3-cBot-HS (Illumina, USA) according to the manufacturer's instructions. Paired-end sequencing (PE150) was lastly performed on a NovaSeq 6000 sequencing system (Illumina, USA).

Raw data in fastq format were firstly processed through in-house perl scripts (TIANGEN Biotech, China) to acquire clean reads by trimming adaptor/low-quality sequences with Trimmomatic (v0.39). In the meanwhile, sequence quality was assessed using FastQC (v0.12.0). Thereafter, clean reads were aligned to the *C. elegans* reference genome (GCA\_000002985.3, Ensembl) using HISAT2 (v2.1.0). HTSeq (v0.6.0) was used to count the reads numbers mapped to genes. Fragments per kilobase of transcript per million mapped reads (FPKM) for each gene was calculated to determine mRNA expression levels. Differentially expressed gene (DEG) analysis for the two groups was filtered by DESeq2 R package (v1.30.0). The false discovery rate (FDR) was set at adjusted p-values (p-adj, or q-values) < 0.05. DEGs were also defined by more than 1.5 of absolute fold change. Gene Set Enrichment Analysis (GSEA) was performed as previously published method.[5][6]

5.RNA interference in *C. elegans*

Bacterial-feeding RNAi was performed as described previously.[7] The target-L4440 constructs were produced as following steps: (1) RT-PCR cloning of targets cDNA sequences (Table 1) from extracted total RNA of *C. elegans*; (2) ligation of cDNAs into multiple-enzyme (XhoI, HindIII, Fermentas, Canada) digested plasmid L4440 (Fenghui Biotech, China) using Hieff clone® plus one step cloning kit (Yeasen Biotech, China) according to the vendors' instructions. Before transforming the resulting constructs or L4440 into Escherichia coli strain HT115 (Maokang Biotech, China) using standard methods, they were sequenced at Tsingke Biotech (Beijing, China) for correct insertion verification. Individual colonies were inoculated to LB broth containing ampicillin (50 μg/mL, Yeasen Biotech, China) and grown overnight at 37°C. RNAi cultures were concentrated to 20 mg/mL, and 250 μL of aliquots for each 6 mm NGM plate were seeded, air dried, and grown at 25°C overnight. Isopropyl β-d-thiogalactoside (IPTG, Biosharp life sciences, China) was added into NGM plates to 10 mM for initiation of dsRNA synthesis. Experimental worm population was prepared by RNAi feeding prior to hatching for one generation.[8] HT115-L4440 bacteria was used as control. Afterwards, survival analyses were conducted as described in 'Survival analysis'. Knockdown efficiency was measured by RT-qPCR in day 3 adult *C. elegans* for three biological replicates per condition.

Table 1: Primer sequences for RNAi and RT-qPCR.

| Genes | Species | Assay | Primer sequences (5' -> 3') | Comments |
| --- | --- | --- | --- | --- |
| F44E5.4/.5 | *C. elegans* | RNAi | F: gtaccgggccccccctcgagCTATTGGTATCGACCTCGGTAC R: aggaattcgatatcaagcttCCGAGATGGGTATCACCAGCAG | PCR cloning; mRNA\_CDS:17-689 |
| sri-40 | *C. elegans* | RNAi | F: gtaccgggccccccctcgagGATATCACATTCGAATTACCCG R: aggaattcgatatcaagcttCAAAACCGTAGAAATATTGAGA | PCR cloning; mRNA\_CDS:4-960 |
| F59B2.12 | *C. elegans* | RNAi | F: gtaccgggccccccctcgagGTTTAGTTCTTCATCGAGTGGA R: aggaattcgatatcaagcttCGTTATGTGAGTCCCCAACTGA | PCR cloning; mRNA\_CDS:87-739 |
| K09C6.9 | *C. elegans* | RNAi | F: gtaccgggccccccctcgagCCACGTTTATTGGTACTCACTA R: aggaattcgatatcaagcttCTTCTCAATACTGTAGCACTTA | PCR cloning; mRNA\_CDS:4-585 |
| F44E5.4/.5 | *C. elegans* | qPCR | F: TTATCAATGAACCAACTGCTGCTGC R: CCGAGATGGGTATCACCAGCAG | Knockdown efficiency, etc. |
| sri-40 | *C. elegans* | qPCR | F: GATATCACATTCGAATTACCCG R: CGTTAGAGTACATAGAAGCTGG | Knockdown efficiency, etc. |
| F59B2.12 | *C. elegans* | qPCR | F: ATGAGTTTGTCAACCAACAAAACGC R: CGTTATGTGAGTCCCCAACTGA | Knockdown efficiency, etc. |
| K09C6.9 | *C. elegans* | qPCR | F: CCACGTTTATTGGTACTCACTA R: TGGGTCATTGTAGAAATGAGTATCG | Knockdown efficiency, etc. |
| gpd-1 | *C. elegans* | qPCR | F: GGCTTCTGCTCATCTTCAAGG R: TGGTGCACGATGCGTTAGAA | Reference gene |
| hsf-1 | *C. elegans* | qPCR | F: TTTGCATTTTCTCGTCTCTGTC R: TCTATTTCCAGCACACCTCGT |  |
| hsp-1 | *C. elegans* | qPCR | F: GTCCAAGCCGCTATCCTCT R: AGGTTGTGAAGGTCTGAGCG |  |
| hsp-4 | *C. elegans* | qPCR | F: TCTCTACCGCTGCCGATAGT R: CAATTTGTGGAACACCGCGT |  |
| hsp-6 | *C. elegans* | qPCR | F: AACCATTGAGCCATGCCGTA R: CTTGAACAGTGGCTTGCACC |  |
| hsp-12.1 | *C. elegans* | qPCR | F: CGATGGTGTCGTGAAGGTGA R: CGGTTGGACGATTCTGCAAT |  |
| hsp-12.6 | *C. elegans* | qPCR | F: TGGGCGGAGTCTGGATATGA R: GGTTCCTTCGTCAGCCATCA |  |
| hsp-16.1 | *C. elegans* | qPCR | F: CGTCCAGCTCAACGTTCTGT R: TGGCTTGAACTGCGAGACAT |  |
| hsp-16.2 | *C. elegans* | qPCR | F: CTGCAGAATCTCTCCATCTGAGTC R: AGATTCGAAGCAACTGCACC |  |
| hsp-17 | *C. elegans* | qPCR | F: GCTCACTGGACACCGAGTAG R: CCGGACAAAGTGACGCTCTA |  |
| hsp-60 | *C. elegans* | qPCR | F: TCTCCAAGAAGGTCACCATCAC R: TCCAATCTTGAGCACAGCGA |  |
| hsp-70 | *C. elegans* | qPCR | F: CCGGTTGAAAAGGCACTTCG R: GAGCAGTTGAGGTCCTTCCC |  |
| hsp-12.2 | *C. elegans* | qPCR | F: GGCCACTTCAACACAACGAC R: GTGACGGCAGTGGATGAGAA |  |
| hsp-90 | *C. elegans* | qPCR | F: TCAGTTCGGAGTCGGATTCT R: GGGTGACCTCTGGGTCATTG |  |

6.RT-qPCR (Real-time polymerase chain reaction)

Nematodes were collected from plates by washing with M9 buffer three times. Total RNA from *C. elegans* or collected using RNAiso plus reagent kit (Takara Bio, Japan). Reverse transcription (RT) was performed by PrimeScript™ RT reagent kit with gDNA eraser (Takara Bio, Japan) using 1 μg of RNA. For quantitative PCR (qPCR), 2 μL of synthetic cDNA was added into 2× Hieff® qPCR SYBR® green master mix (Yeasen Biotech, China) with forward and reverse gene-specific primers. 3 ~4-fold diluted cDNA was amplified in 40 ~ 60-cycle runs according to the manufacturer's instructions. Data were acquired by a CFX Connect system (Bio-Rad, Singapore) controlled by CFX manager software v3.1. Percentage relative mRNA levels were measured by 2-∆∆Ct method using *C. elegans* gpd-1 (GAPDHase-l) as housekeeping gene. Results are representative of two or three independent experiments, showing in % normalized relative mRNA levels for three or 6 biological replicates per condition.

7.Fluorescence microscopy

All worm strains were maintained at 25°C unless clearly stated elsewhere. Prepare nematodes after being treated with 50 μM of WGX-50 or control (0.1 % ethanol) as described elsewhere. Afterwards, they were transferred on 1 % agarose pads (1 mm) over glass slides with 25 mM of levamisole (Aladdin, China) to anesthetize. Images of worms were captured at 20/40× magnification using an inverted fluorescence microscope (RVL-100-G, ECHO, USA) under constant exposure time. ImageJ (Fiji, NIH, USA) was used for the quantification of fluorescent images. Results were presented as the integrated density of gray values ± SEM. Assays were repeated three times with at least 10 animals per treatment in each independent trial. We visualized hsp-16.2::GFP with the microscope through GFP filter sets on day 5, and scored its levels for fluorescence intensity of the two groups of nematodes.

8.Target protein modelling

To predict potential targets of WGX-50, we used online machine learning (ML) platform SwissTargetPrediction (<http://swisstargetprediction.ch/>) both for human and mice molecules. Then we checked ZINC/ChEMBL hits ZINC1225873/CHEMBL252000 (WGX-50) via SEA in ChEMBL 20 library setting the affinity threshold at 5 nM. More deeply, both extended connectivity fingerprints (ECFP, by rdkit) and maximum tanimoto coefficient (Tc) parameters were used for target prediction that involved searching database ChEMBL (<https://www.ebi.ac.uk/chembl/>) for proteins of which the ligands share similar structural features as WGX-50. ECFP was utilized to represent chemical structure of the query. Tc was employed as similarity metric for comparing fingerprints. For identified targets, a lower p-value suggests more significant association between the compound and target.

9.Molecular dynamics simulations

A combination of molecular docking, MD simulations, binding free energy calculations were applied to investigate the binding mode of WGX-50 with HSP90 (PDB#2XJX) protein. in our previous work. Briefly, the lowest-energy conformations of ligand were obtained using CREST of the XTB program.[9] For conformation search of the ligand, the root mean square deviation (RMSD) threshold was set at 0.5 Å, and no constraint was used. It was found that all low-energy conformers at the DFT level are within 3 kcal/mol relative to the lowest energy conformers at the GFN2-xTB method. Molecular docking was performed using AutoDock Vina33. Docking poses of substrates to the enzyme were generated with the molecular docking method in the “Receptor-ligand Interaction” module. To obtain the force field parameters for the substrates, calculations of electrostatic potential surfaces were performed at the level of HF/6-31G(d), and then a two-step restrained electrostatic potential charge fitting method[10] was applied to generate the bonds, angles, dihedral angles and van der Waals radii parameters for the substrates by using the Antechamber package. MD simulations were performed using Amber 22 and AmberTools 22 packages.[7] Protonation states of the titratable residues such as His, Glu, and Asp were determined from the PDB2PQR server using PROPKA.[11] Through tleap, the systems were solvated in an octahedral box of TIP3P water with the thickness of the external water layer exceeding 10 Å, while sodium ions were added to achieve charge neutralization for the calculated system. To remove atomic collisions, the solvated systems were subjected to energy minimization of the water molecules and then of the overall system. The system was gradually heated up from 0 to 300 K in 50 ps using the Langevin thermostat and switched to constant pressure and temperature (NPT) using Langevin temperature regulation and the Berendsen barostat for constant pressure controlling and equilibrated for 50 ps with a collision frequency of 2 ps-1 to adjust to the correct density. Each system was first performed with 10 parallel experiments which include the above minimization, heating, equilibration and 1 ns production run. Three of the ten with stable hydrogen bond networks were chosen to run extension. The Particle Mesh Ewald (PME) method was employed to account for long-range electrostatic interactions. Residues with binding contributions over 1.0 kcal/mol then were selected for further hydrophobic interaction and hydrogen bonding analysis with the CPPTRAJ toolset.[12]

10.Noncovalent interaction analysis

The noncovalent interaction (NCI) analysis including IGMH (independent gradient model based on Hirshfeld partition of molecular density) analysis and AIM (atoms in molecules) was performed.[13][14] Briefly speaking, IGMH replaces all ρfree involved in the IGM (independent gradient model) equations with atomic densities derived by the popular Hirshfeld partition of the actual electron density. Another difference of IGMH compared to IGM is that the sign(λ2) r calculated based on the actual electron density is employed instead of the sign(λ2) r under the promolecular approximation. In Bader’s famous AIM theory[14], a critical point (CP) refers to the position where the magnitude of gradient of r is zero. The so-called (3, -1) type of CP commonly appears between two attracting atoms and hence is referred to as bond critical point (BCP). The (3, +1) type of CP often implies that electron is locally depleted at this position it generally occurs at the center of a ring. The standard for distinguishing (3, - 1) and (3,+1) types of CPs is the sign of λ2. In AIM theory, BCP is often recognized as the most representative position of a notable interaction, and values of various functions at a BCP are very useful in characterizing the interaction nature and strength. A bond path is generated by tracing the steepest ascent path from a BCP, and it may be viewed as the most representative path revealing an interatomic interaction. Maps were produced using VMD (v1.9.3) and Multiwfn (v3.7) software packages.[15]

11.Statistical analysis

No pilot studies were performed in advance to determine the minimum sample size at a given degree of confidence, but the biological replicates used in this study were similar with those published elsewhere. No data points in this study were excluded. Most findings are based on two or three independent experiments. Data are presented as mean ± SEM (standard error of mean). Graphs were plotted with GraphPad Prism v9.5 (GraphPad Software, USA) and produced afterwards by Adobe Illustrator CC (Adobe Systems Inc., USA).

Unless otherwise indicated, statistically significant differences for survival curves were determined by Log-rank (Mantel-Cox) test. Significance level was set at = 0.05. Censored animals are indicated with symbols for each curve. Statistical significance between two groups were determined by unpaired t test (two-tailed); otherwise, for multiple comparisons, one-way ANOVA analysis should be performed accordingly. Pre-requested normality and homoscedasticity were assumed to be fitted, considering the small sample size (n < 30). Analyses were blinded to the investigators who performed experiments. Statistical information such as sample size, p-values, and the detailed methods used for statistical inference can be found in the descriptions for figures and tables. \* p < 0.05, \*\* p < 0.01, \*\*\* p < 0.001 and \*\*\*\* p < 0.0001; ns or no indication, not significant.

Reference

[1] Guarente, L., and Kenyon, C. (2000). Genetic pathways that regulate ageing in model organisms. Nature 408, 255–262.

[2] Jorgensen, E. M., and Mango, S. E. (2002). The art and design of genetic screens: Caenorhabditis *elegans*. Nat. Rev. Genet. 3, 356-369.

[3] Chen, W., Muller, D., Richling, E., and Wink, M. (2013). Anthocyanin-rich purple wheat prolongs the life span of Caenorhabditis *elegans* probably by activating the DAF-16/FOXO transcription factor. J. Agric. Food Chem. 61, 3047–3053.

[4] Oleson, B. J. et al. Early life changes in histone landscape protect against age-associated amyloid toxicities through HSF-1-dependent regulation of lipid metabolism. Nat. Aging 4, 48–61 (2023).

[5] Mootha, V. K. et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267-273 (2003).

[6] Subramanian, A. et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. 102, 15545-15550 (2005).

[7] Case, D. A.; Belfon, K.; Ben-Shalom, I. Y.; Brozell, S. R.; Cerutti, D. S.; Cheatham, T. E.; Iii; Cruzeiro, V. W. D.; Darden, T. A.; Duke, R. E.; Giambasu, G.; Gilson, M. K.; Gohlke, H.; Goetz, A. W.; Harris, R.; Izadi, S.; Izmailov, S. A.; Kasavajhala, K.; Kovalenko, A.; Krasny, R.; Kurtzman, T.; Lee, T. S.; LeGrand, S.; Li, P.; Lin, C.; Liu, J.; Luchko, T.; Luo, R.; Man, V.; Merz, K. M.; Miao, Y.; Mikhailovskii, O.; Monard, G.; Nguyen, H.; Onufriev, A.; Pan, F.; Pantano, S.; Qi, R.; Roe, D. R.; Roitberg, A.; Sagui, C.; Schott-Verdugo, S.; Shen, J.; Simmerling, C. L.; Skrynnikov, N. R.; Smith, J.; Swails, J.; Walker, R. C.; Wang, J.; Wilson, L.; Wolf, R. M.; Wu, X.; Xiong, Y.; Xue, Y.; York, D. M.; Kollman, P. A. AMBER 2022; University of California: San Francisco, 2022.

[8] López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. Hallmarks of aging: An expanding universe. Cell 186, 243–278. (2023).

[9] Pracht, P., Bohle, F. & Grimme, S. Automated exploration of the low-energy chemical space with fast quantum chemical methods. Phys. Chem. Chem. Phys. 22, 7169–7192 (2020).

[10] Kirschner, K. N.; Yongye, A. B.; Tschampel, S. M.; GonzálezOuteiriño, J.; Daniels, C. R.; Foley, B. L.; Woods, R. J. GLYCAM06: A generalizable biomolecular force field. Carbohydrates. J. Comput. Chem. 2008, 29, 622−655.

[11] Olsson, M. H. M.; Søndergaard, C. R.; Rostkowski, M.; Jensen, J. H. PROPKA3: Consistent Treatment of Internal and Surface Residues in Empirical pKa Predictions. J. Chem. Theory Comput. 2011, 7, 525−537.

[12] Roe, D. R.; Cheatham, T. E., III PTRAJ and CPPTRAJ: Software for processing and analysis of molecular dynamics trajectory data. J. Chem. Theory Comput. 2013, 9, 3084−3095.

[13] Lu, T.; Chen, Q. Independent gradient model based on Hirshfeld partition: A new method for visual study of interactions in chemical systems. J. Comput. Chem. 2022, 43, 539−555.

[14] Spackman, M. A. The Quantum Theory of Atoms in Molecules. From Solid State to DNA and Drug Design. Edited by Cherif ́ F. Matta and Russell J. Boyd. Angew. Chem., Int. Ed. 2007, 46, 6766−6767.

[15] Lu, T. & Chen, F. Multiwfn: A multifunctional wavefunction analyzer. J. Comput. Chem. 33, 580–592 (2012).

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* Overview
* WGX-50 extends lifespan in *C. elegans*
* WGX-50 activates HSPs longevity pathway
* Reference

Overview

WGX-50 is an important molecule with anti-inflammatory and antioxidant effects similar to those of peppercorns, present in peppercorns, a common food additive and traditional herbal medicine. However, as of now, research on the anti-aging properties of the peppercorn WGX-50 is very limited. The results of experiments on WGX-50 for anti-aging, antioxidant and anti-Alzheimer's disease have been limited to be applied to clinical medicine or cosmeceutical products.

This year, SRE-China's goal is to use synthetic biology techniques, especially for combining our peppermint WGX-50 small molecules with proteins, in order to explore its pharmacological activities. Our experiments were conducted in two stages. Firstly, we tested the drug using the cryptic rod nematode *C. elegans* as a model organism to investigate its unexplored anti-aging effects and found that it activates the longevity pathway of heat shock proteins, thereby extending the lifespan of the nematode.To dissect the underling mechanisms, we performed RNA-seq which afterwards provides interesting hints (F44E5.4/.5, sri-40, F59B2.12, K09C6.9) that had been highly upregulated by WGX-50 for four folds. Therefore, we hypothesised that WGX-50 may extend nematode lifespan through these new genes.

In our project, we successfully explored the mechanism of action of WGX-50 to extend the healthy lifespan We hope that in the future, we can contribute to the prevention of diseases and improvement of the quality of life of the elderly through the anti-aging and antioxidant efficacy of WGX-50.

WGX-50 extends lifespan in *C. elegans*

In the view of the previously described beneficial effects of WGX-50 (Figure 1a) on Alzheimer's disease (AD)1–6, pain relief7, drug-induced heart injury8 and skin health9,10, we sought to investigate its impact on longevity of *C. elegans* because the underlying mechanism on longevity is uncertain. To investigate whether WGX-50 could prolong lifespan, we conducted a longevity test in *C. elegans*. Interestingly, WGX-50 significantly prolonged the lifespan of *C. elegans* and the best lifespan extension effect was observed at a concentration of 50 μM (Figure 1b). Interestingly, in accordance with our study, WGX-50 extended lifespan in w.t nematodes (unpublished data; dose range tested, 3.75 ~ 250 μM), in which the dose 15 M increased the median survival to 45% (from 22 to 32 days, Figure 1b) at 20 oC. To this end, we concluded that WGX-50 prolongs lifespan in *C. elegans*.

![](https://static.igem.wiki/teams/5496/images/result-1.jpg)

**Figure 1: WGX-50 extends lifespan in N2 *C. elegans*.** (a): WGX-50 was previously characterized as a novel drug candidate for AD, which is a natural product derived from *Zanthoxylum bungeanum Maxim*. (Image credit: Royalty-free photo from https://create.vista.com/, slightly changed by the authors). (b): WGX-50 extends lifespan in N2 nematodes at 20 °C (b, n > 280 worms per condition). \*\*\*\* p < 0.0001.
 **Four novel genes take no roles in WGX-50 mediated longevity owing to a combinatorial effect enabled by the drug**

To decipher the mechanistic pathways behind the detected phenotypes, we performed RNA sequencing (RNA-seq) for adult worms treated with WGX-50 or vehicle. RNA sequencing strandedness allows researchers to determine which DNA strand (sense or antisense) a transcript came from. Compared to regular RNA sequencing methods, stranded RNA sequencing can find novel transcripts, distinguish transcripts from overlapping genes, find antisense sequences, and annotate genes.

![](https://static.igem.wiki/teams/5496/images/result-2.jpg)

**Figure 2:** Adult N2 *C. elegans* were treated with 50 μM WGX-50 or control (Ctrl) for 5 days at 25°C, collected worms were subjected to RNA-seq analysis. Volcano plot of differentially expressed genes (DEGs) in w.t *C. elegans* (n = 3). DEGs were selected based on predefined cutoff as p-adj < 0.05 and fold change (FC) ≥ 1.5 (dash-dotted line, vertical). Four down-regulated genes and some up-regulated genes (FC < 2) are in black. Genes with more than 4-fold changes are labeled with gene symbols. Colored dots indicate genes that had more than 2-fold changes varying at significance levels (red, p-adj < 0.05; gold, p-adj < 0.01; cyan, p-adj < 0.001). \* p < 0.05, \*\* p < 0.01, \*\*\* p < 0.001 and \*\*\*\* p < 0.0001; ns or no indication, not significant.

Next, RNA interference (RNAi) was conducted to investigate the unexplored role of these genes in WGX-50 mediated longevity. RNAi is among the processes that use short RNAs as guides for sequence-specific silencing; these pathways are together referred to as RNA silencing pathways. Long double-stranded RNA causes sequence-specific mRNA destruction, which is known as RNAi. This phenomenon was first identified in *C. elegans*. As results show, WGX-50 medicated survival increase was not diminished by RNAi any of these genes.

Further measurement for these gene hits using RT-qPCR indicates that their expression levels were inconsistently changed upon WGX-50 in different treatment days (Figure 3), which is also incompatible with that in RNA-seq data. This may suggest their expression is dependent on the age of the worms, hence it differs for some hits. To address such inconsistency, we performed gene set enrichment analysis. As results show (Table 2), metabolisms in amino acids, vitamins, carbohydrates, and lipids were significantly enriched. In RNA-seq data, genes like ttr-1/6/17, drd-1, vit-1, far-1/2/6, lbp-3/6 whose expression had been significantly increased are predicted to be involved in lipid transport. Besides, T05E11.3 (enpl-1, human ortholog hsp90b1) including F44E5.4/.5, members of HSP family working as molecular chaperones, were increased in their transcripts (colored, un-texted, Figure 4). It was reported that T05E11.3 was activated even if at low levels in long lived worms11. Collectively, our data herein demonstrated that WGX-50 possibly enables various longevity pathways, and thus, knocking down any of RNA-seq revealed hits doesn't attenuate the action of WGX50.

![](https://static.igem.wiki/teams/5496/images/result-3.jpg)

**Figure 3:** RNA-seq revealed gene hits didn't involve in WGX-50 longevity, which possibly owes to an activated paradigm consists of multiple pathways by WGX-50. Transcriptional levels of F44E5.4/.5, sri-40, F59B2.12, K09C6.9 were determined by RT-qPCR in WGX-50 (50 μM) treated *C. elegans* on day 3 or 5. Results are representative of two independent experiments (n = 2), showing in % relative mRNA levels normalized to gpd-1 (GAPDHase-l) for six biological replicates per condition (n = 6).\* p < 0.05, \*\* p < 0.01, \*\*\* p < 0.001 and \*\*\*\* p < 0.0001; ns or no indication, not significant.

**Table 2: Metabolic pathways enriched by GSEA in WGX-50 treated *C. elegans* via RNA-seq.**

|
|  |
 Name | Metabolism type | Size | ES | NES | Nom p-val | FDR q-val | Core enrichment || Valine, leucine and isoleucine degradation | Amino acids | 42 | 0.5117 | 1.7188 | 0.0025 | 0.0476 | acdh-1, acdh-4, alh-12, acdh-9, ech-6, ech-7, Y44A6D.5, hach-1, hacd-1, B0250.5, alh-8, T09B4.8, T02G5.7, ard-1, alh-9 |
| Glutathione metabolism | Amino acids | 42 | 0.4965 | 1.7004 | 0.0025 | 0.045 | gsto-1, gst-36, gst-5, gpx-5, gst-11, gst-6, gst-38, C02D5.4, spds-1, gpx-7, C44B7.7, Y7A9A.1, gpx-1, gpx-3, T25B9.9, gst-44, gst-10, gst-9, E01A2.1, gsto-2, idh-2 |
| Cysteine and methionine metabolism | Amino acids | 35 | 0.5464 | 1.761 | 0.0013 | 0.0438 | C01G10.9, tatn-1, spds-1, cysl-4, cysl-1, Y44A6D.5, cth-1, ldh-1, cdo-1, sams-5, mpst-2, got-1.2, mpst-4, T09B4.8, ZC373.5, F42F12.4, cbs-2 |
| Glycine, serine and threonine metabolism | Amino acids | 25 | 0.5582 | 1.6836 | 0.0051 | 0.0496 | Y51H7C.9, gldc-1, R102.4, gcsh-2, T25B9.1, cth-1, gcsh-1, F08F3.4, T09B4.8, cbs-2, agxt-1, alh-9, cbs-1, C31C9.2 |
| Beta-alanine metabolism | Amino acids | 20 | 0.5208 | 1.4857 | 0.0431 | 0.2041 | upb-1, alh-12, ech-6, ech-7, spds-1, hach-1, alh-8, alh-9, alh-5, dhp-2 |
| Folate biosynthesis | N-acyl-amino acids; vitamin B | 11 | 0.6995 | 1.7402 | 0.0103 | 0.0454 | ptps-1, F49H6.5, cat-4, mocs-1, moc-5, dhfr-1, qdpr-1 |
| Pentose and glucuronate interconversions | Carbohydrates | 22 | 0.5751 | 1.7081 | 0.0079 | 0.0462 | ugt-55, ugt-62, ugt-48, ugt-46, ugt-6, Y71F9B.9, dhs-21, R04B5.5 |
| Ascorbate and aldarate metabolism | Carbohydrates | 20 | 0.6218 | 1.7942 | 0.0027 | 0.0381 | ugt-55, ugt-62, ugt-48, alh-12, ugt-46, ugt-6 |
| Retinol metabolism | Vitamin A | 19 | 0.5719 | 1.655 | 0.0055 | 0.0605 | ugt-55, ugt-62, ugt-48, ugt-46, ugt-6 |
| Riboflavin metabolism | Vitamin B | 13 | 0.6156 | 1.5851 | 0.0209 | 0.1043 | pho-14, pho-1, pho-13, pho-4, pho-10 |
| Metabolism of xenobiotics by cytochrome P450 | Na. | 31 | 0.624 | 1.971 | 0 | 0.0082 | gsto-1, gst-36, gst-5, ugt-55, ugt-62, ugt-48, gst-11, ugt-46, gst-6, ugt-6, gst-38, C02D5.4 |
| Steroid hormone biosynthesis | Lipids | 22 | 0.6326 | 1.8647 | 0.0014 | 0.0208 | stdh-2, dhs-25, ugt-55, ugt-62, ugt-48, ugt-46, ugt-6 |
| Biosynthesis of unsaturated fatty acids | Lipids | 18 | 0.557 | 1.5867 | 0.0307 | 0.1121 | stdh-2, fat-7, acox-1.3, acox-1.5, fat-5, let-767, acox-1.4 |
| Arachidonic acid metabolism | Lipids (PUFA) | 18 | 0.5312 | 1.5002 | 0.0374 | 0.1936 | C07E3.9, gpx-5, gst-11, gpx-7, gpx-1, gpx-3, daf-41, pges-2, ltah-1.2, gst-9 |
| Ether lipid metabolism | Lipids | 13 | 0.604 | 1.5421 | 0.0284 | 0.1422 | T05C3.6, C07E3.9, T03G6.3, paf-1, paf-2 |

WGX-50 activates HSPs longevity pathway

In RNA-seq data, HSP70 like F44E5.4/.5 genes were activated. Expectedly, the upstream hsf-1gene was tested to be transcriptionally elevated by WGX-50 in nematodes (Figure 5). Generally, hsf-1 responses to proteotoxic stress and thereby enables longevity in *C. elegans* via accumulating HSPs functioning as molecular chaperones12. As quantified, HSPs forms such as hsp12.2 and hsp-90 were also transcriptionally increased (Figure 5). HSP90 has been reported to be required for nematodes' longevity13. It was confirmed that DAF-21, without influencing DAF-16D/F localization or transcriptional activity, particularly enhances daf-2 and heat-shock induced nuclear translocation of DAF-16A as well as the induction of DAF-16A-specific mRNAs. On the contrary, transcriptional levels of other tested HSPs were all decreased (Figure 5).

![](https://static.igem.wiki/teams/5496/images/result-4.jpg)

**Figure 5:** Heat shock protein genes were transcriptionally quantified by RT-qPCR for groups (50 μM of WGX-50 vs. Ctrl, 5 days). Results are representative of three independent experiments (n = 3), showing in % relative mRNA levels normalized to gpd-1 (GAPDHase-l) for biological replicates (n = 6).

As shown in Figure 6a and b, it was obviously that WGX-50 failed to activate the fluorescent reporter in hsp-16.2::GFP animals. Overall, our data indicate that WGX-50 activated HSPs longevity pathway despite down-regulation of other HSPs.

![](https://static.igem.wiki/teams/5496/images/result-5.jpg)

**Figure 6:** WGX-50 regulates HSPs longevity pathway. (a): 50 μM of WGX-50 didn't affect the protein levels of hsp-16.2 in transgenic CL2070 worms upon 5 days treatment at 25°C. Whole-body fluorescence for hsp-16.2::GFP (b) was visualized under an ECHO inverted fluorescence microscope. Signal intensities (c) was presented as integrated density of gray values ± SEM of 10 animals in each group (n = 10). Scale bar = 320 μm. Results were normally selected from two independent experiments. \*\*\* p < 0.001, \*\*\*\* p < 0.0001; ns or no indication, not significant.

References

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10. Gao ping, Song Xiao fei, Dai Jing bo, Wang Zhao xia, Xu Wang jie, Q. Z. dong. Rotective Effects of GX-50 on Ultraviolet-B Exposed Skin Fibroblasts. J. Shanghai Jiaotong Univ. Sci. 33, 47–53 (2015).

11. Jorgensen, E. M., and Mango, S. E. (2002). The art and design of genetic screens: Caenorhabditis  *elegans*. Nat. Rev. Genet. 3, 356-369.

12. Walker, G. A. et al. Heat Shock Protein Accumulation Is Upregulated in a Long-Lived Mutant of Caenorhabditis  *elegans*. Journals Gerontol. Ser. A Biol. Sci. Med. Sci. 56, B281–B287 (2001).

13. Somogyvári, M., Gecse, E. & Sőti, C. DAF-21/Hsp90 is required for *C. elegans* longevity by ensuring DAF-16/FOXO isoform A function. Sci. Rep. 8, 12048 (2018).

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