Applied Pharmacotherapeutics I

Dataset description: Investigation of burn injuries in a porcine model using metabolomics

Burn injury initiates a hypermetabolic response that leads to muscle wasting and organ dysfunction in burn patients. The lab examined the transition between “ebb” and “flow” in post-burn metabolism using proton nuclear magnetic resonance (1H-NMR) spectroscopy and serum from a porcine model of severe burn injury. We hypothesized serum metabolomes of porcine subjects would be distinguishable by time point, and changes in individual metabolite concentrations would characterize the shift from ebb to flow after burns. Methods: Pigs received 40% total body surface area (TBSA) burns with additional pine bark smoke inhalation. Arterial blood was drawn at baseline (pre-burn, “control” samples) and 48h (“treated” samples) post injury. The aqueous portion of each serum sample was analyzed using 1H-NMR spectroscopy.

Task: please analyse together the dataset that you received via email by answering to the three parts, each of them described below and corresponding to a specific analysis in MetaboAnalyst. Be as specific as possible, and don't forget to put the figures whenever requested.

First Part: Statistical Analysis

  • Downlad the data, and chose « spectral bins » and « sample in row (unpaired) ». Submit and pass uurethe data filtering section: choose “none” in filtering.
  • Normalisation: find the best normalisation method. Which sample normalization and data scaling did you use? Put the figure showing the normalized result for both feature and sample normalization.
  • PCA analysis: do you see, by looking at the 2D score-plot, any clear outlier? If yes, put the 2D PCA figure here to show it, remove the outlier(s) and perform a new normalisation on the dataset without the outlier(s). Again, which sample normalization and data scaling did you use? Put the figure showing the normalized result for both feature and sample normalisation.
  • PCA analysis: show the 2D score-plot.
  • PLS-DA analysis: show the 2D score-plot and put the R2 and Q2 values: do you think these are good values? Explain.
    Name the more important variables (from ‘imp. Features’; remember: with a VIP value>0.7).
    Which significant variables are higher for the samples at at baseline and which of these are higher for samples at 48h post-burn?
    Show the permutation test: what is the p-value? What does that mean?

Second Part: Enrichment Analysis

  • Perform an enrichment analysis using the over representation analysis. Type the name of the metabolites (one metabolite per line) that you identified as significant in step 5 of the previous question. Click submit, make sure that each metabolite has a correct hit during the “Compound Name/ID Standardization”. Click submit again.
  • I know that some of you don’t have a human dataset but a dataset with animals/plants, and so you shouldn’t be using the enrichment analysis since it’s supposed to be usable only with human data. However, in this case, do it anyway so you will have done it yourself with your own dataset, and click on “Pathway-associated metabolite sets (SMPDB)”.
  • Insert the image of the metabolite set enrichment overview (use the barchart view, not the network view).
  • What are the most significant pathways and what are the p-values of those (note here only the pathways that are significant, based on the p-value).

Third Part: Pathway Analysis

  • Perform a pathway analysis using the “one-column compound list” panel. Type the name of the metabolites (one metabolite per line) that you identified as significant in step 5 of the statistical analysis (first part) question. Use “Compound Name” as Input Type. Click submit, make sure that each metabolite has the correct hit during the “Compound Name/ID Standardization”. Click submit again.
  • Keep the “Hypergeometric Test” and “Relative-betweeness Centrality” as the pathway algorithms. Select the correct organism (human/pig/plant…), click submit.
  1. Insert a figure of each significant (only the significant ones, not the others) pathway, that you identified as significant based on their p-value.
  2. What is the p-value for these pathways?
  3. How is MetaboAnalyst determining that a pathway is significant or not?
  4. What determines the size of the circle of each pathway?
  5. Take a closer look at each of your significant pathways, in these particular pathways which of your metabolites is at a bottlenecks or at a hub position?

Remember, at the center of any academic work, lies clarity and evidence. Should you need further assistance, do look up to our Pharmacology Assignment Help

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