International Scholarly Research Notices

International Scholarly Research Notices / 2014 / Article

Research Article | Open Access

Volume 2014 |Article ID 391836 |

Maria Chiara Mimmi, Maurizio Ballico, Ghassan Nakib, Valeria Calcaterra, Jose Louis Peiro, Mario Marotta, Gloria Pelizzo, "Altered Metabolic Profile in Congenital Lung Lesions Revealed by 1H Nuclear Magnetic Resonance Spectroscopy", International Scholarly Research Notices, vol. 2014, Article ID 391836, 8 pages, 2014.

Altered Metabolic Profile in Congenital Lung Lesions Revealed by 1H Nuclear Magnetic Resonance Spectroscopy

Academic Editor: T. Fossen
Received11 Nov 2013
Accepted17 Dec 2013
Published29 Jan 2014


Congenital lung lesions are highly complex with respect to pathogenesis and treatment. Large-scale analytical methods, like metabolomics, are now available to identify biomarkers of pathological phenotypes and to facilitate clinical management. Nuclear magnetic resonance (NMR) is a unique tool for translational research, as in vitro results can be potentially translated into in vivo magnetic resonance protocols. Three surgical biopsies, from congenital lung malformations, were analyzed in comparison with one control sample. Extracted hydrophilic metabolites were submitted to high resolution 1H NMR spectroscopy and the relative concentration of 12 metabolites was estimated. In addition, two-dimensional NMR measurements were performed to complement the results obtained from standard monodimensional experiments. This is one of the first reports of in vitro metabolic profiling of congenital lung malformation. Preliminary data on a small set of samples highlights some altered metabolic ratios, dealing with the glucose conversion to lactate, to the relative concentration of phosphatidylcholine precursors, and to the presence of myoinositol. Interestingly some relations between congenital lung lesions and cancer metabolic alterations are found.

1. Introduction

Lung development is a highly regulated and coordinated process, typified by stage specific changes in structure and function, which includes branching morphogenesis, angiogenesis, sacculation, alveologenesis, and cytodifferentiation [1]. Congenital pulmonary disease is characterized by a wide variety of abnormalities arising at different stages of the process [2].

Metabolomics is an emerging approach that uses analytical techniques, such as nuclear magnetic resonance (NMR) [3] spectroscopy, or mass spectrometry (MS) [4], to achieve a comprehensive global monitoring of metabolites and their fluctuations in response to various stimuli.

Although gene/protein expressions schemes provide useful clues to organs development and function, many factors like posttranslational modifications, alternative gene functions, or compartmentalization also raise important biochemical changes. Therefore, metabolic profiling is essential to completely describe the physiopathological state of a biological system.

Considerable efforts in this sense were devoted to human lung cancer [5, 6] and to human lung injury [7], but very limited information is available about metabolic perturbations arising in situ from congenital lung malformations.

The purpose of this study was to determine the feasibility of tissue extraction and analysis of the main hydrosoluble metabolites of lung lesions biopsies from human. Three types of congenital malformation were evaluated in comparison to a control lung tissue.

Particular attention was paid to the lactate/glucose (Lac/Glc) ratio and to choline (Cho) compounds, which are involved in the phosphatidylcholine (PtdCho) synthesis and catabolism.

Many reasons lead us to focus on these particular metabolic pathways; first of all a hyperactive glycolysis as well as the following increment of the Lac/Glc ratio is associated with lung inflammatory state [7] and represents, for instance, a diagnostic tool related to lung cancers [5, 6].

The principal argument to investigate PtdCho metabolism, which actually include choline and glucose, is the crucial role of this phospholipid as the main component of alveolar surfactant [8].

Many data prove that a disturbed surfactant metabolism plays a role in several neonatal lung diseases, such as congenital diaphragmatic hernia (CDH) [9]. A further prompt to focus on glucose and choline pathways comes from the results of Raman mapping and FTIR imaging studies on congenital pulmonary airways malformations (CPAM), in which an accumulation of PtdCho and glycogen was noted [10].

In addition, it is generally accepted that the cycle of PtdCho, the major phospholipid component of eukaryotic cells, is affected by malignant proliferations; free choline and its derivatives phosphocholine (PCho) and glycerophosphocholine GPCho represent relevant biomarkers for diagnostic and therapeutic tools [11].

Finally, NMR spectroscopy is a unique tool for translational research. NMR-based metabolomics can provide a comprehensive information on the degree of organ dysfunction and the metabolic biomarkers discovered in vitro can be translated into in vivo magnetic resonance spectroscopy protocols.

2. Materials and Methods

2.1. Instrumentation and Reagents

1H NMR experiments were acquired on a Bruker Avance spectrometer operating at 500 MHz (Bruker BioSpin, Germany).

Tissue homogenization was performed by Ultra Turrax T25 Homogenizer (IKA, Germany). Water was ultrapure (mQ H2O) and solvents were of HPLC grade (Sigma Aldrich, United States).

2.2. Tissue Extraction

Three biopsies were obtained from pathological lung tissue of two infants who underwent surgery for congenital malformations. The control biopsy was from a newborn deceased on the 7th day of life. The samples were immediately frozen in liquid nitrogen after collection and stored at −80°C. In Table 1 the characteristics of the tissue sections are reported.

SpecimenPatientType of malformationSex of patientAge at surgerySurgical procedure

N. 1ABronchogenic cystM4 monthsLobectomy
N. 2BCongenital lobar emphysemaM6 monthsLobectomy
N. 3 BBronchopulmonary sequestrationM6 monthsLobectomy
N. 4CControl lungM7 daysLung biopsy

The frozen biopsies were put in plastic test tube and kept dipped into liquid nitrogen during the homogenization process. While samples N. 2, N. 3, and N. 4 were completely pulverized in ca 5 minutes, sample N. 1 proved resistant to homogenization and a nucleus of tissue remained intact. The specimens weight after homogenization ranged roughly between 30 and 110 mg. Immediately after weighting, a biphasic extraction of metabolites was performed, following a previously reported protocol [12]. The obtained dry extracts were stored at –80°C until NMR analysis.

2.3. NMR Spectroscopy
2.3.1. Data Acquisition

An aliquot of each aqueous extract (corresponding to 30–50 mg of frozen pulverized tissue) was redissolved in 0.65 mL of deuterated phosphate buffer at pH 7.4, containing Na2HPO4/NaH2PO4 50 mM, TSPNa+-d4 (sodium 3-trimethyl-silyl [2,2 3,3-d4] propionate) 0.1 mM as a frequency reference, and NaN3 0.5 mM to prevent bacterial growth. The working temperature was 298 K.

Standard monodimensional 1H spectra included flip-angle pulses of 30° and residual solvent suppression by presaturation applied during the 5 s relaxation delay.

Two-dimensional TOCSY [13] spectra were also acquired and the acquisition scheme mlevphpr [14] included solvent suppression by presaturation [15], applied along the 2 s relaxation delay, and mixing time of 80 ms.

Spectra acquisition and processing were performed with the Bruker software TOPSPIN.

2.3.2. Data Treatment

After FID apodization with a line broadening window function, all 1D spectra were phased, Fourier-transformed, and chemical-shift referenced. The signals overlapping made the direct integration of peaks area impossible; therefore, a spectrum modeling (peak fitting) was performed with ACD/Labs 10. The experimental spectrum was divided in regions containing not more than 100 peaks prior to modeling with a combination of Gauss and Lorentz functions.

For the relative quantification of the metabolites of interest each fitted integral was normalized versus the total area of the spectrum and corrected for the numbers of protons associated to the assigned peak. The normalization permitted overcoming the differences in biopsies dimensions and homogenization and allowed for a comparison of metabolites levels among pathological and control tissues.

The error associated with each modeled peak integral was calculated by the following formula: where Total RSS stands for residual sum of squared differences between the calculated spectrum and original experimental data, while Total Integral stands for the absolute integral of the spectral region used for fitting and RSS determination.

Only a small region of 2D TOCSY spectra (in the range 3.0–4.6 ppm) was used to evaluate the relative concentration of free Cho and its derivatives PCho and GPCho; the diagnostic cross peaks of the three compounds are generated by the two methylene groups adjacent to the quaternary ammonium (see Figure 1). The height of significant cross peaks was measured with the software Sparky (NMR Assignment and Integration software) and the noise was taken as confidence limit of the measure. The noise was estimated as the median of 30 randomly sampled absolute value data heights.

3. Results

The 1H-1D NMR analysis of aqueous extracts from surgical biopsies provided an overview of the main hydrosoluble metabolites. The relative concentration of the assigned metabolites was obtained by normalizing each integral value, corrected for the proton number, versus the total area of the spectrum and is shown in Table 2 and Figure 2.

Metabolite Broncho pulmonary sequestrationBronchogenic cyst
Congenital lobar emphysema Control lung
(% fraction of total spectrum area)(% fraction of total spectrum area)(% fraction of total spectrum area)(% fraction of total spectrum area)

Lactic acid2.71 ± 0.023.09 ± 0.023.40 ± 0.024.42 ± 0.04
Alanine0.402 ± 0.0030.458 ± 0.0030.806 ± 0.0050.319 ± 0.003
Glutamic acid0.576 ± 0.0050.798 ± 0.0051.093 ± 0.0070.620 ± 0.006
Succinic acid0.0721 ± 0.00060.0504 ± 0.00030.0349 ± 0.00020.189 ± 0.002
Creatine0.1498 ± 0.00080.260 ± 0.0010.1788 ± 0.00090.182 ± 0.001
Choline0.0768 ± 0.00040.1079 ± 0.00050.279 ± 0.0010.0718 ± 0.0004
Phosphocholine0.0827 ± 0.00050.1063 ± 0.00050.1883 ± 0.0009ND
Glycerophosphocholine0.0744 ± 0.0004ND0.1145 ± 0.0006ND
Glucose beta0.0433 ± 0.00020.790 ± 0.0040.324 ± 0.0021.143 ± 0.007
Myoinositol1.019 ± 0.0061.137 ± 0.0051.364 ± 0.0070.522 ± 0.003
Creatine0.192 ± 0.0010.311 ± 0.0010.223 ± 0.0010.170 ± 0.001
Myoinositol_b0.847 ± 0.0051.371 ± 0.0071.932 ± 0.0090.472 ± 0.003
Formic acid1.5 ± 0.10.23 ± 0.020.10 ± 0.010.42 ± 0.02

The most important differences of malformation tissues versus control lung regarded the ratio lactic acid/glucose or Lac/Glc (here intended as Lac/β-D-glucose) (Table 3). This parameter, that reflects an enhanced glycolysis to produce lactic acid, was particularly high in bronchopulmonary sequestration and to a lesser extent in congenital lobar emphysema, compared to the control lung and bronchogenic cyst samples.

Metabolites ratioBroncho pulmonary sequestrationBronchogenic cystCongenital lobar emphysemaControl lung

Lac/Glc62.5 ± 0.53.91 ± 0.0210.48 ± 0.073.87 ± 0.04
Lac/Creat18.1 ± 0.211.9 ± 0.0719.0 ± 0.124.2 ± 0.2
Myo/Glc23.5 ± 0.11.44 ± 0.014.21 ± 0.020.457 ± 0.003

PCho/GPCho5.6 ± 0.50.96 ± 0.047.0 ± 0.20.41 ± 0.02

The other metabolite that discriminated pathological from healthy tissue was myoinositol whose level was lower in the control lung compared to the three malformation samples.

Among the assigned compounds, the phosphatidylcholine precursors were included (namely, Cho, PCho, and GPCho). In the adopted pH conditions the diagnostic peaks of choline compounds, arising from the methyl groups of the quaternary ammonium, were in the range 3.20–3.25 ppm; this resulted to be a very crowded region where also glucose and other compounds resonances were detected (Figure 3). In particular PCho and GPCho could not be measured in the control lung due to the overlapping of glucose signals that was particularly concentrated in this sample. For the same reason GPCho could not be determined in the bronchogenic cyst tissue.

Apart from the mentioned inconvenience, the 1D NMR analysis showed some remarkable variations. The level of the free Cho decreased with the following order: congenital lobar emphysema, bronchogenic cyst, bronchopulmonary sequestration, control lung. The level of PCho decreased with the following order: congenital lobar emphysema, bronchogenic cyst, bronchopulmonary sequestration (control lung not determined). The relative concentration of GPCho was noted to be higher in congenital lobar emphysema than in bronchopulmonary sequestration.

The limits of 1D NMR analysis prompted us to exploit 2D NMR TOCSY experiments to estimate the Cho compounds level. Although the correlation of metabolite concentration with TOCSY peaks intensity is not linear, it has been proved that the variation of choline compounds signals reflects their concentration difference [16] (Figure 4).

To overcome the inhomogeneity among biopsies dimensions, and consequently of global spectra intensity, we focused on the relative peaks intensity instead of comparing their absolute values. In Table 3, we reported the ratio of PCho and GPCho cross peak height; this ratio decreased with the following order: congenital lobar emphysema, bronchopulmonary sequestration, bronchogenic cyst, control lung. Unfortunately, the cross peak of free Cho could not be taken into consideration because of overlapping with one of the myoinositol cross peaks (Figure 4).

We observed that GPCho is clearly more concentrated in the control lung than in malformations, while on the contrary PCho level is higher in malformations than in control lung, as it comes out from the qualitative observation of TOCSY maps.

4. Discussion

The lung is a complex organ consisting of more than 40 distinct cell types derived from ectodermal, mesenchymal, and endodermal compartments with a number of specialized functions related to gas exchange, host defense, and ion transport [17].

The spectrum of congenital lung lesions likely results from disordered embryologic interactions, occurring during the course of fetal lung development. Bronchopulmonary sequestrations are microscopic cystic masses of nonfunctioning pulmonary, which have a blood supply originating from the aorta rather than the pulmonary artery, and an absence of communication with the bronchial tree. BPS is likely to arise between the 4th and 8th week gestation during the pseudoglandular period. Also most bronchogenic cysts develop during this phase and it result from abnormal budding of the foregut. congenital lobar emphysema usually develops during the canalicular stage (16–26 weeks gestation) and it is an overinflation and distension of one or more pulmonary lobes, possibly secondary to a defect in the bronchial cartilage.

A detailed understanding of the biochemical processes governing stage specific changes during lung maturation and the eventual lesion onset is still lacking. Describing physiopathological lung development, at a molecular resolution, could lead to new perspectives in treatment of congenital malformations either in prenatal or in postnatal period. Tissue characterization of pulmonary malformation in infants and children could be useful with the perspective to give valuable information about lung maturation, timing, lung repair, and regeneration.

The aim of the this work was establishing a protocol for the extraction of hydrophilic metabolites from lung tissues, as well as for their quantification by NMR, prior to embarking on a large scale study to identify biomarkers of congenital lesions.

The first challenge we met was related to the intrinsic limitations of 1D NMR spectroscopy applied to a complex mixture of unfractionated compounds as in the case of any biological tissue extracts. The signals superposition was overcome by peak fitting, and in addition 2D NMR spectroscopy was exploited to get a semiquantitative estimate of Cho derivatives concentrations.

Though being aware that biomarkers identification must build on a statistically significant set of samples, we could make some interesting observations that regarded, first of all, the ratio Lac/Glc. An enhanced Glc utilization, a hallmark of malignant transformation, was observed for pulmonary sequestration and to a lesser extent for pulmonary emphysema, while the level of bronchogenic cyst was very similar to the control lung (Table 3).

It must be said that both Lac and Glc were higher in the bronchogenic cyst and in the control samples than in the remaining two samples. The investigation of a more numerous sample will clarify this variability.

From the analysis of 1D NMR data it resulted as well that myoinositol (Myo) accumulated in malformation tissues compared to the control lung; interestingly the Myo level decreased in the order pulmonary sequestration, pulmonary emphysema, bronchogenic cyst, with the latter very similar to the control lung. This evidence could be worthy of further investigation, if it was considered that myoinositol is involved in fetal lung maturation as reported by Fanos et al. [18].

The last remarkable finding, which seemed to discriminate the normal lung from malformations, was based on 2D NMR analysis and regarded the ratio of PCho/GPCho cross peak height; this ratio decreased with the following order: pulmonary emphysema, pulmonary sequestration, bronchogenic cyst, control lung. Again, the last two samples were more homogeneous than the first two conditions. Interestingly, there seems to be a link between the reported observations and what is already known in the field of cancer research. It is generally accepted, in fact, that the cycle of PtdCho, the major phospholipid component of eukaryotic cells, is affected by malignant proliferations [11]. This causes an alteration of PCho and GPCho levels which can be detected by high resolution NMR; in particular an elevated size of PCho generally reflects a high rate of cell proliferation, while the spectral parameter PCho/GPCho has the role of malignancy indicator in cancer [19].

5. Conclusions

The comprehension of biological mechanisms and timing related to congenital lung malformations is still incomplete. Numerous studies and clinical trials have focused on the role of surfactant in lung maturation and function; much attention has been devoted also to the effect of surfactant deficiency in preterm infants and in neonatal lung diseases. In spite of that, a more detailed picture of the biochemical pathways involved in lung lesions onset during pregnancy could have positive outcomes both on diagnostic and therapeutic strategies of pediatric management.

To the best of our knowledge this is one of the first applications of metabolomics to human tissues derived from congenital lung lesions.

Although limited to a small set of samples this study highlights some altered metabolic ratios, dealing with the glucose conversion to lactate, to the relative concentration of PtdCho hydrosoluble precursors and to the presence of myoinositol. Interestingly, some unexpected relations between congenital lung lesions and cancer metabolic alterations are found.

Further investigation of the regulation of these metabolic traits can have a manifold outcome: first of all, the translation of in vitro NMR findings to in vivo MRS (Magnetic Resonance Spectroscopy) diagnosis of fetal lung maturity and development.

List of Abbreviations

NMR:Nuclear magnetic resonance
MS:Mass spectrometry
CDH:Congenital diaphragmatic hernia
CPAM:Congenital pulmonary airways malformations
TOCSY:Total correlation spectroscopy.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.


This work was financially supported by the Italian Ministry for University and Research (FIRB 2010 RBFR109EOS). The authors wish to thank Dr. F. Combi for collaborating in collecting research samples.


  1. D. Warburton, M. Schwarz, D. Tefft, G. Flores-Delgado, K. D. Anderson, and W. V. Cardoso, “The molecular basis of lung morphogenesis,” Mechanisms of Development, vol. 92, no. 1, pp. 55–81, 2000. View at: Publisher Site | Google Scholar
  2. T. Berrocal, C. Madrid, S. Novo, J. Gutiérrez, A. Arjonilla, and N. Gómez-León, “Congenital anomalies of the tracheobronchial tree, lung, and mediastinum: embryology, radiology, and pathology,” Radiographics, vol. 24, no. 1, article e17, 2004. View at: Google Scholar
  3. O. Beckonert, H. C. Keun, T. M. D. Ebbels et al., “Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts,” Nature Protocols, vol. 2, no. 11, pp. 2692–2703, 2007. View at: Publisher Site | Google Scholar
  4. W. Lu, B. D. Bennett, and J. D. Rabinowitz, “Analytical strategies for LC-MS-based targeted metabolomics,” Journal of Chromatography B, vol. 871, no. 2, pp. 236–242, 2008. View at: Publisher Site | Google Scholar
  5. T. W. M. Fan, A. N. Lane, R. M. Higashi et al., “Altered regulation of metabolic pathways in human lung cancer discerned by 13C stable isotope-resolved metabolomics (SIRM),” Molecular Cancer, vol. 8, article 41, 2009. View at: Publisher Site | Google Scholar
  6. K. Kami, T. Fujimori, H. Sato et al., “Metabolomic profiling of lung and prostate tumor tissues by capillary electrophoresis time-of-flight mass spectrometry,” Metabolomics, vol. 9, no. 2, pp. 444–453, 2013. View at: Publisher Site | Google Scholar
  7. N. J. Serkova, Z. van Rheen, M. Tobias, J. E. Pitzer, J. E. Wilkinson, and K. A. Stringer, “Utility of magnetic resonance imaging and nuclear magnetic resonance-based metabolomics for quantification of inflammatory lung injury,” American Journal of Physiology, vol. 295, no. 1, pp. L152–L161, 2008. View at: Publisher Site | Google Scholar
  8. L. J. I. Zimmermann, D. J. M. T. Janssen, D. Tibboel, A. Hamvas, and V. P. Carnielli, “Surfactant metabolism in the neonate,” Biology of the Neonate, vol. 87, no. 4, pp. 296–307, 2005. View at: Publisher Site | Google Scholar
  9. A. Akella and S. B. Deshpande, “Pulmonary surfactants and their role in pathophysiology of lung disorders,” Indian Journal of Experimental Biology, vol. 51, no. 1, pp. 5–22, 2013. View at: Google Scholar
  10. C. Krafft, D. Codrich, G. Pelizzo, and V. Sergo, “Raman mapping and FTIR imaging of lung tissue: congenital cystic adenomatoid malformation,” Analyst, vol. 133, no. 3, pp. 361–371, 2008. View at: Publisher Site | Google Scholar
  11. K. Glunde, Z. M. Bhujwalla, and S. M. Ronen, “Choline metabolism in malignant transformation,” Nature Reviews Cancer, vol. 11, no. 12, pp. 835–848, 2011. View at: Publisher Site | Google Scholar
  12. M. C. Mimmi, P. Picotti, A. Corazza et al., “High-performance metabolic marker assessment in breast cancer tissue by mass spectrometry,” Clinical Chemistry and Laboratory Medicine, vol. 49, no. 2, pp. 317–324, 2011. View at: Publisher Site | Google Scholar
  13. L. Braunschweiler and R. R. Ernst, “Coherence transfer by isotropic mixing: application to proton correlation spectroscopy,” Journal of Magnetic Resonance, vol. 53, no. 3, pp. 521–528, 1983. View at: Google Scholar
  14. A. Bax and D. G. Davis, “MLEV-17-based two-dimensional homonuclear magnetization transfer spectroscopy,” Journal of Magnetic Resonance, vol. 65, no. 2, pp. 355–360, 1985. View at: Google Scholar
  15. D. I. Hoult, “Solvent peak saturation with single phase and quadrature fourier transformation,” Journal of Magnetic Resonance, vol. 21, no. 2, pp. 337–347, 1976. View at: Google Scholar
  16. D. Morvan, A. Demidem, J. Papon, and J. C. Madelmont, “Quantitative HRMAS proton total correlation spectroscopy applied to cultured melanoma cells treated by chloroethyl nitrosourea: demonstration of phospholipid metabolism alterations,” Magnetic Resonance in Medicine, vol. 49, no. 2, pp. 241–248, 2003. View at: Publisher Site | Google Scholar
  17. Y. Xu, Y. Wang, V. Besnard et al., “Transcriptional programs controlling perinatal lung maturation,” PLoS ONE, vol. 7, no. 8, Article ID e37046, 2012. View at: Publisher Site | Google Scholar
  18. V. Fanos, L. Atzori, K. Makarenko, G. B. Melis, and E. Ferrazzi, “Metabolomics application in maternal-fetal medicine,” BioMed Research International, vol. 2013, Article ID 720514, 9 pages, 2013. View at: Publisher Site | Google Scholar
  19. F. Podo, “Tumour phospholipid metabolism,” NMR in Biomedicine, vol. 12, no. 7, pp. 413–439, 1999. View at: Google Scholar

Copyright © 2014 Maria Chiara Mimmi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder

Related articles

Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.