This vignette contains a case study of the effects of environmental contamination on gilt-head bream (Sparus aurata) [@ziarrusta2018bream]. Fish were exposed over 14 days to oxybenzone and changes were sought in their brain, liver and plasma using untargeted metabolomics. Samples were processed using Ultra-performance liquid chromatography mass-spectrometry (UHPLC-qOrbitrap MS) in positive and negative modes with both C18 and HILIC separation.
The mortality of exposed fish was not altered, as well as the brain-related metabolites. However, liver and plasma showed perturbations, proving that adverse effects beyond the well-studied hormonal activity were present.
The enrichment procedure implemented in
was used in the
study for a deeper understanding of the dysregulated metabolites
in both tissues.
At the time of publication, the KEGG database [@kanehisa2016kegg]
FELLA is based-- did not have pathway annotations for
the Sparus aurata organism.
It is common, however, to use the zebrafish (Danio rerio) pathways as
a good approximation.
KEGG provides pathway annotations for it under the organismal code
which will be used to build the
library(FELLA) library(igraph) library(magrittr) set.seed(1) # Filter the dre01100 overview pathway, as in the article graph <- buildGraphFromKEGGREST( organism = "dre", filter.path = c("01100")) tmpdir <- paste0(tempdir(), "/my_database") # Make sure the database does not exist from a former vignette build # Otherwise the vignette will rise an error # because FELLA will not overwrite an existing database unlink(tmpdir, recursive = TRUE) buildDataFromGraph( keggdata.graph = graph, databaseDir = tmpdir, internalDir = FALSE, matrices = "none", normality = "diffusion", niter = 100)
We load the
FELLA.DATA object to run both analyses:
fella.data <- loadKEGGdata( databaseDir = tmpdir, internalDir = FALSE, loadMatrix = "none" )
Given the 11-month temporal gap between the study and this vignette, small changes to the amount of nodes in each category are expected (see section 2.4 Data handling and statistical analyses from the study). Please see the Note on reproducibility to understand why.
We want to emphasise that each time this vignette is built,
FELLA constructs its
using the most recent version of the KEGG database.
KEGG is frequently updated and therefore small changes can
take place in the knowledge graph between different releases.
The discussion on our findings was written at the date specified
in the vignette header and using the KEGG release in the
Table 1 from the main body in [@ziarrusta2018bream]
contains 5 KEGG identifiers associated to metabolic changes
in liver tissue and 12 in plasma.
Our first enrichment analysis with
FELLA will be based on the
Also note that we use the faster
approx = "normality" approach,
whereas the original article uses
approx = "simulation" with
niter = 15000
This is not only intended to keep the bulding time
of this vignette as low as possible,
but also to demonstrate that the findings
using both statistical approaches are consistent.
cpd.liver <- c( "C12623", "C01179", "C05350", "C05598", "C01586" ) analysis.liver <- enrich( compounds = cpd.liver, data = fella.data, method = "diffusion", approx = "normality")
All the metabolites are successfully mapped:
analysis.liver %>% getInput %>% getName(data = fella.data)
Below is a plot of the reported sub-network using the default parameters. The five metabolites are present and lie within the same connected component.
plot( analysis.liver, method = "diffusion", data = fella.data, nlimit = 250, plotLegend = FALSE)
We will examine the igraph object with the reported sub-network and some of its reported entities in tabular format:
g.liver <- generateResultsGraph( object = analysis.liver, data = fella.data, method = "diffusion") tab.liver <- generateResultsTable( object = analysis.liver, data = fella.data, method = "diffusion")
The reported sub-network contains around 100 nodes and can be manually inquired:
Figure 2 from the original study frames the five metabolites in the
input around Phenylalanine metabolism.
We can verify that
FELLA finds such pathway and two closely related
suggestions: Tyrosine metabolism and
Phenylalanine, tyrosine and tryptophan biosynthesis.
path.fig2 <- "dre00360" # Phenylalanine metabolism path.fig2 %in% V(g.liver)$name
These are the reported pathways:
tab.liver[tab.liver$Entry.type == "pathway", ]
Figure 2 also gathers two types of metabolites: metabolites in the input (inside shaded frames) and other contextual metabolites (no frames) that link the input metabolites.
First of all, we can check that all the input metabolites appear in the suggested sub-network. While it's expected that most of the input metabolites appear as relevant, it is an important property of our method, in order to elaborate a sensible biological justification of the experimental differences.
cpd.liver %in% V(g.liver)$name
On the other hand, one of the two contextual metabolites
is also suggested by
FELLA, proving its usefulness to fill the
gaps between the input metabolites.
cpd.fig2 <- c( "C00079", # Phenylalanine "C00082" # Tyrosine ) cpd.fig2 %in% V(g.liver)$name
As shown in section Defining the input and running the enrichment, 12 KEGG identifiers (one ID is repeated) are related to the experimental changes observed in plasma, which are the starting point of the enrichment:
cpd.plasma <- c( "C16323", "C00740", "C08323", "C00623", "C00093", "C06429", "C16533", "C00740", "C06426", "C06427", "C07289", "C01879" ) %>% unique analysis.plasma <- enrich( compounds = cpd.plasma, data = fella.data, method = "diffusion", approx = "normality")
The totality of the 11 unique metabolites
map to the
analysis.plasma %>% getInput %>% getName(data = fella.data)
Again, the reported sub-network consists of a large connected component encompassing most input metabolites:
plot( analysis.plasma, method = "diffusion", data = fella.data, nlimit = 250, plotLegend = FALSE)
We will export the results as a network and as a table:
g.plasma <- generateResultsGraph( object = analysis.plasma, data = fella.data, method = "diffusion") tab.plasma <- generateResultsTable( object = analysis.plasma, data = fella.data, method = "diffusion")
The reported sub-network is a bit larger than the one from liver, containing roughly 120 nodes:
Figure 3 from the original study is a holistic view of the
affected metabolites found in plasma, based on literature and
on an analysis with
The 11 metabolites are depicted within their core metabolic pathways.
We will check whether
FELLA is able to highlight them,
by first showing the reported metabolic pathways:
tab.plasma[tab.plasma$Entry.type == "pathway", ]
And then comparing against the ones in Figure 3:
path.fig3 <- c( "dre00591", # Linoleic acid metabolism "dre01040", # Biosynthesis of unsaturated fatty acids "dre00592", # alpha-Linolenic acid metabolism "dre00564", # Glycerophospholipid metabolism "dre00480", # Glutathione metabolism "dre00260" # Glycine, serine and threonine metabolism ) path.fig3 %in% V(g.plasma)$name
All of them but Glutathione metabolism are recovered,
FELLA can help gaining perspective on
the input metabolites.
As in the analogous section for liver, we will quantify how many input metabolites, drawn within a shaded frame in Figure 3, are reported in the sub-network:
cpd.plasma %in% V(g.plasma)$name
From the 11 highlighted metabolites, only one is
not reported by
Conversely, two out of the three contextual metabolites from the same figure are reported:
cpd.fig3 <- c( "C01595", # Linoleic acid "C00157", # Phosphatidylcholine "C00037" # Glycine ) cpd.fig3 %in% V(g.plasma)$name
As Figure 3 shows, the addition of linoleic acid and
phosphatidylcholine, backed up by
FELLA, helps connecting
almost all the metabolites found in blood.
FELLA misses glycine and, in fact, stays consistent with the
pathway (Glutathione metabolism) and the input metabolite
(5-Oxo-L-proline) that it left out from Figure 3.
The fact that
FELLA does not suggest such pathway seems to happen
at several molecular levels and therefore none of its metabolites
Even if the glutathione pathway was not reported,
FELLA can greatly ease the creation of elaborated contextual figures,
such as Figure 3, by suggesting the intermediate metabolites and
the metabolic pathways that link the input compounds.
In this vignette, we apply
FELLA to an untargeted metabolic study
of gilt-head bream exposed to an environmental contaminat (oxybenzome).
This study is an example of how
FELLA can be useful for
(1) organisms not limited to Homo sapiens, and
(2) conditions not limited to a specific disease.
On one hand,
FELLA helps creating complex
contextual interpretations of the data, such as the comprehensive
Figure 3 from the original article [@ziarrusta2018bream].
This material would be challenging to build through regular
over-representation analysis of the input metabolites.
On the other hand, metabolites and pathways suggested by
were also mentioned in the literature and supported the
main findings in the study.
In particular, it helped identify key processes such as
alpha-linoleic acid metabolism and
which ultimately pointed to alterations in oxidative stress.
This is the result of running
Date of generation:
Image of the workspace (for submission):
tempfile(pattern = "vignette_dre_", fileext = ".RData") %T>% message("Saving workspace to ", .) %>% save.image(compress = "xz")
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