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Research Article
Open Access
With NMR Towards New Diagnostic Methods For Dengue
Camilla do Nascimento Bernardo1, Marcela Cristina Oliveira Nogueira1, Érika Pereira de Aquino1, Sina Schmidtke1, Elizandes Leal de
Azeredo2, Claire Fernandes Kubelka2 and Jochen Junker1*
1Fundação Oswaldo Cruz - CDTS, Rio de Janeiro - RJ, Brazil
2Fundação Oswaldo Cruz - IOC, Rio de Janeiro - RJ, Brazil
*Corresponding author:
Dr. Jochen Junker
Fundação Oswaldo Cruz - CDTS, Rio de
Janeiro - RJ, Brazil E-mail: junker@cdts.fiocruz.br
Received February 07, 2012; Accepted February 28, 2012; Published March
12, 2012
Citation: Bernardo CdN, Nogueira MCO, Aquino ÉPd, Schmidtke S, Azeredo ELd,
et al. (2012) With NMR Towards New Diagnostic Methods For Dengue. J Anal Bioanal Tech 3:140.
doi:10.4172/2155-9872.1000140
Dengue is a very common viral disease in tropical countries. From time to time it has become epidemic,
hindering economics and affecting the social system. Early starting treatment reduces recovery time and suffering
of the patients. But, the usual diagnostics is done by detection of the antibodies, which can only be done after five
days. Other parts of the metabolomics might change much faster and therefore the small molecule content in the
blood might change much faster than that. By using NMR spectroscopy we attempted to differentiate blood plasma
from infected patients from healthy subjects and afterwards identify the consistent changes. Several of these were
found and are discussed.
Introduction
According to WHO records, 50-100 million people are infected
with Dengue every year. Within 100 countries, about 550 thousand
patients are hospitalized and about 20 thousand die every year. The
early diagnosis of Dengue is done by eliminating other possible causes
of the observed symptoms, a direct antibody diagnosis becomes
possible only after several days into the disease. Hence, for the first
days, a patient is either not treated (not strong enough symptoms) or
treated “on suspicion”. A method of early identification for Dengue is
need [1].
The blood is the main means of transport inside the human body,
not only for proteins (like antibodies), but also for small molecules.
Due to changes in their metabolism, infected cells could be producing
different secondary metabolites, which will show up in the blood
plasma. This changes should appear quickly, much faster than
antibodies. Blood plasma has been extensively studied by NMR [2-4]
and many small molecules can be associated to certain NMR signals. A
lot of work has been done in order to characterize the lipids in plasma
samples [5-8], but hardly have other molecules been looked at [9-11].
The quantitative nature of the proton NMR experiment allows the
observation of relative changes in the concentration of the different
metabolites, as changes in the NMR signals. With the help of Principal
Component Analysis (PCA) the characterization of consistent changes
becomes possible [4,9,11-17].
A series of consistent changes were assigned and the metabolic
function of the identified molecules were investigated. Of course many
changes are in general characteristic for infections, but there were a few
exceptions, specially when looking the combination of different factors.
Hence, not only might we be able to differentiate the plasma samples of
healthy and infected subjects, but also subjects with different infections.
Materials and Methods
In our study we have analyzed blood plasma from healthy and
infected subjects using proton NMR. The samples originate from the
collection from the “Instituto Oswaldo Cruz” , were collected under
non-controlled conditions over several years and kept deep frozen. A
volume of 0.5 ml of the samples was filtered with 3kDa filters (Millipore
Amicon Ultra-4 Centrifugal Filter Units 3kDa, centrifuged at 4000g),
freeze-dried and re-suspended in 0.5 ml D2O. 1-D NOE spectra [9,16]
were acquired on a Bruker Avance 500 MHz with BBO probe, using a
D1 of 2s, 64 scans and pre–saturation on the water signal. The spectra
were calibrated on the glucose methyl at 1.33 ppm and then subjected
to an extensive Principal Components Analysis (PCA) [11,16-18] using
the software Amix version 3.8.6, in order to obtain a differentiation.
Signal ranges that showed significant changes within the same group
were excluded for the PCA, as well as the glucose signals.
All signals identified by the PCA were assigned to a metabolite
via [3] and the observed change was noted. After a literature survey
regarding the concentration change in the metabolite, a further analysis
of the combination of different factors was attempted.
Results
In total 63 plasma samples were prepared as descried and the
obtained NMR spectra were evaluated. As it turns out, the width of
the glucose peaks (3.5 ppm - 4.3 ppm) varies dramatically, and decided
if a spectrum could be used in the PCA or not, as shown in Figure 1.
After this evaluation only 51 plasma sample spectra could be used for
further analysis. A total of 19 spectra, 5 being of healthy subjects, were
randomly chosen and used to create the PCA model. The remaining 32
spectra, including 4 spectra of healthy subjects, were used as test-set.
Figure 1:Comparison of different proton spectra of 5 selected infected
samples. A) The overview of the superimposed spectra shows several regions
in which the samples differ a lot. Those regions were excluded in the following
PCA analysis. B) The glucose region of the spectra shows the dramatic effect
of this signal on the overall aspect of the spectra. Based on this signals the
spectra were classified, and the results are shown color-coded: red and yellow
are good spectra, green is the limit for classification. Blue and violet can not be
used, and later the PCA models classify them as out of the model.
A series of different PCA models were created, varying several
of the available parameters. The differentiation is very consistent
and survives even dramatic parameter changes. For three different
bucket widths (0.1 ppm, 0.05 ppm and 0.01 ppm) we obtained similar
results, with PC2 vs PC1, PC2 vs PC3 and PC2 vs PC4 showing nicely separated data points (see Figure 2). The plots for the different bucket
sizes are composed of several pieces: A) The confidence plot, showing if
all samples are within the model. The further the points are in the lower
left quadrant, the better the model describes them. B) The deviation
of each sample from the model. It shows how well the model really
represents an average over all samples. A sample with distance zero
from the model would be located in the middle of the plot, point
above the middle have a positive deviation, points below negative. C)
Number of PCs for the explanation of >95% of the data’s variation. D)
Plot of different PCs against each other, showing the separation of the
data. E) Plot of the buckets that are responsible for the positioning of
the samples in the previous plots. The most important buckets can be
identified by comparing the plots D and E for the same PCs.
Figure 2:PCA models obtained using different bucket widths, 0.1 ppm, 0.05
ppm and 0.01 ppm (top to bottom). All samples are color coded using black for
healthy and blue for infected. A) Confidence plot for the model, showing that
all samples used are well described with the model. B) Deviation plot, showing
the distribution of the distances of the samples from the model. For the model
with the bucket width 0.01 ppm a equal distribution for healthy and infected
is observed. C) Number of PCs explaining the data diversity, values shown
indicate variance explained using PC1 and >95% (blue). D) 2D projections
plotting the PCs 1×2, 1×3, 4×2 and 2×3. The plots 1×2, 4×2 and 2×3 show good
separation of the samples into healthy and infected. E) 2D projections plotting
the buckets influence on the localization of samples in the previous plot. Whilst
most buckets accumulate on one point, 8 can be identified that are responsible
for the separation seen in the previous plot.
The analysis of the plots reveals that the result is seemingly
independent of the bucket width, the separation is always maintained.
But the narrowest bucket width shows the best model, which can
be seen in the distance plot B. It also becomes very clear that only 8
buckets are relevant for the separation of the data, consistent for all
models. Therefore all models were used for the test-set analysis. Figure
3 shows the result of our PCA model applied to the classification of
the test-set samples. As before, the classification works better when the
buckets become narrower. The model with the bucket width 0.01 ppm
only has one sample classified erroneously, but it also was classified as
being out of the model’s confidence interval.
Figure 3:Application of the PCA models with different bucket widths for the
classification of the test-set samples. Healthy subject samples are colored blue,
dark blue indicates that the sample was classified within the confidence interval,
light blue outside. Infected subject samples are colored green, dark green
indicates that the sample was classified within the confidence interval, light
green outside. The different models improve in classification with decreasing
bucket width, 0.01 ppm performing best. Only one infected subjected sample
is classified wrongly, but it also is identified as out of the confidence interval for
the model.
Overall PC2 is the most important component for the differentiation
between healthy and infected subjects, whereas PC1, PC3 and PC4
contribute much less to the differentiation. From the PCA model we
can identify the most important regions responsible for that result,
mainly around 1.92 ppm (Figure 4) and 3.38 ppm (Figure 5). According
to assignments published in the literature for blood plasma [3] we can
associate the varying signals to a series of compounds: proline, lysine,
arginine, acetate, treonine, β-glucose, pyruvate, citroline, glutamine,
isoleucine and others. Table 1 shows a summary of all changes that
could be identified in this ranges.
Figure 4:Superposition of randomly chosen spectra (healthy subjects in red and
blue, infected subjects in green and violet) showing the difference as identified
by the PCA at 1.86 ppm (citrulline),1.92 ppm (lisine, arginine and acetate), 2.04
ppm (glycoproteins), 2.05 ppm (proline), 2.08/2.09 ppm (glutamine), 2.13 ppm
(metionine), 2.15 ppm (glutamate) and 2.24 ppm (valine).
Figure 5:Superposition of randomly chosen spectra (healthy subjects in red
and blue, infected subjects in green and violet) showing the difference as
identified by the PCA at 3.16 ppm (phenylalanine), 3.24 ppm (arginine), 3.35
ppm (proline), 3.95 ppm (glycerol), 3.97 ppm (phenylalanine) and 3.98 ppm
(histidine).
Table 1:Changes in signal intensities observed between plasma samples
of healthy and Dengue infected subjects and corresponding assignment to
metabolites.
Discussion and Conclusion
The increased level of phenylalanine can be correlated to the
immune activation marker neopterin. The oxidative stress due to
immune activation destroys the cofactor needed for phenylalanine
(4)–hydroxylase, hence the serum concentrations of phenylalanine
increases [19]. The observed decrease of citrulline level and increase
of proline level combined with unchanged arginine levels can be
associated to certain production paths of nitric oxide, common
response to viral infections. All three compounds are connected in the
metabolism. Arginase hydrolyses L–arginine to urea and L–ornithine which, in turn, is a precursor for the synthesis L–proline by the enzyme
ornithine aminotransferase. Alternatively arginine can be metabolized
by nitric oxide synthase (NOS) to produce nitric oxide (NO) and L–
citrulline. It has been shown that Dengue patients have increased NO
levels [20]. Frequently Dengue is compared to Sepsis, as one can lead
to the other [21]. It has been shown that Sepsis patients have higher
protein breakdown levels [22,23], which are the major source of L–
arginine in the metabolism, and therefore compensating the NOS
arginine use, whilst maintaining a low citrulline level.
Overall, not all differences found in the NMR spectra could be
associated to known metabolic effects. Further investigations will be
carried out on them. But, for the moment, the observed level decreases
of citrulline and proline combined with a constant arginine level seem
to be a possible way to identify Dengue.
Acknowledgements
The authors wish to acknowledge funding for this project from FAPERJ - Edital
10/2008 and CAPES.
OMICS Publishing Group is the member of / publishing partner of/source content provider to
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