Engineering Success

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.

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.

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.

Figure 1. The molecular structure of WGX-50

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

[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).