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MALDI-TOF-MS serum protein profiling for developing diagnostic models and identifying serum markers for discogenic low back pain.

Zhang YG, Jiang RQ, Guo TM, Wu SX, Ma WJ - BMC Musculoskelet Disord (2014)

Bottom Line: The discriminative ability of two most significantly differential peaks was poor in classifying DLBP vs.Our findings benefit not only the diagnosis of CLBP but also the understanding of the differences between different forms of DLBP.The ability to distinguish between different causes of CLBP and the identification of serum biomarkers may be of great value to diagnose different causes of DLBP and predict treatment efficacy.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Orthopedics, First Affiliated Hospital of Medical College of Xi'an Jiaotong University, Xi'an 710061, China. zyingang@mail.xjtu.edu.cn.

ABSTRACT

Background: The identification of the cause of chronic low back pain (CLBP) represents a great challenge to orthopedists due to the controversy over the diagnosis of discogenic low back pain (DLBP) and the existence of a number of cases of CLBP of unknown origin. This study aimed to develop diagnostic models to distinguish DLBP from other forms of CLBP and to identify serum biomarkers for DLBP.

Methods: Serum samples were collected from patients with DLBP, chronic lumbar disc herniation (LDH), or CLBP of unknown origin, and healthy controls (N), and randomly divided into a training set (n = 30) and a blind test set (n = 30). Matrix-assisted laser desorption ionization time-of-flight mass spectrometry was performed for protein profiling of these samples. After the discriminative ability of two most significantly differential peaks from each two groups was assessed using scatter plots, classification models were developed using differential peptide peaks to evaluate their diagnostic accuracy. The identity of peptides corresponding to three representative differential peaks was analyzed.

Results: The fewest statistically significant differential peaks were identified between DLBP and CLBP (3), followed by CLBP vs. N (5), DLBP vs. N (9), LDH vs. CLBP (20), DLBP vs. LDH (23), and LDH vs. N (43). The discriminative ability of two most significantly differential peaks was poor in classifying DLBP vs. CLBP but good in classifying DLBP vs. LDH. The accuracy of models for classification of DLBP vs. CLBP was not very high in the blind test (forecasting ability, 67.24%; sensitivity, 70%), although a higher accuracy was observed for classification of DLBP vs. LDH and LDH vs. N (forecasting abilities, ~90%; sensitivities, >90%). A further investigation of three representative differential peaks led to the identification of two peaks as peptides of complement C3, and one peak as a human fibrinogen peptide.

Conclusions: Our findings benefit not only the diagnosis of CLBP but also the understanding of the differences between different forms of DLBP. The ability to distinguish between different causes of CLBP and the identification of serum biomarkers may be of great value to diagnose different causes of DLBP and predict treatment efficacy.

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MS/MS identification of selected serum peptides as fragments of complement C3 (A) and (B), and isoform 1 of fibrinogen alpha chain precursor (C). The fragment ion spectra shown were taken for a MS/MS ion search of Protein Knowledgebase (UniProKB) (http://www.uniprot.org). b and y fragment ion series are indicated together with the limited sequences.
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Figure 3: MS/MS identification of selected serum peptides as fragments of complement C3 (A) and (B), and isoform 1 of fibrinogen alpha chain precursor (C). The fragment ion spectra shown were taken for a MS/MS ion search of Protein Knowledgebase (UniProKB) (http://www.uniprot.org). b and y fragment ion series are indicated together with the limited sequences.

Mentions: Protein/peptide markers were chosen based on the following considerations: (i) significantly differentially expressed peptides, especially those exhibiting a time- or dose-dependent pattern; and (ii) peptides that were able to accurately classify the control group and disease groups. Taking these into account, three differential peptides (1692.71 m/z, 1886.86 m/z and 1779.28 m/z) were selected for peptide identification. By comparing areas of the selected peptide peaks, we found that the peptides to which 1779 m/z and 1692.71 m/z corresponded showed similar expression patterns (Figure 2), with the lowest expression level in DLBP, followed by CLBP, N and LDH. Their expression levels differed significantly different between DLBP and the other three groups (Ps < 0.05 for all), but not between CLBP of unknown origin, LDH and N. The peptide peak at 1886 m/z peptide had the largest area in DLBP, followed by CLBP of unknown origin, LDH and N. Its expression level also differed significantly between DLBP and the other three groups (P < 0.05), but not between CLBP of unknown origin, LDH and N. MS/MS analysis of the peaks at 1779 and 1692 m/z detected most b and y ions (Figure 3A and B) and the sequences of these two peptides were S.SKITHRIHWESASLL.R and S.KITHRIHWESASLL.R, which corresponded to complement C3 and complement C3 precursor, respectively. The peak at 1886 m/z was analyzed by MS/MS and the sequence of this peptide was identified as R.HRHPDEAAFFDTASTGK.T, which is unique to fibrinogen alpha chain precursor (Figure 3C).


MALDI-TOF-MS serum protein profiling for developing diagnostic models and identifying serum markers for discogenic low back pain.

Zhang YG, Jiang RQ, Guo TM, Wu SX, Ma WJ - BMC Musculoskelet Disord (2014)

MS/MS identification of selected serum peptides as fragments of complement C3 (A) and (B), and isoform 1 of fibrinogen alpha chain precursor (C). The fragment ion spectra shown were taken for a MS/MS ion search of Protein Knowledgebase (UniProKB) (http://www.uniprot.org). b and y fragment ion series are indicated together with the limited sequences.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4061098&req=5

Figure 3: MS/MS identification of selected serum peptides as fragments of complement C3 (A) and (B), and isoform 1 of fibrinogen alpha chain precursor (C). The fragment ion spectra shown were taken for a MS/MS ion search of Protein Knowledgebase (UniProKB) (http://www.uniprot.org). b and y fragment ion series are indicated together with the limited sequences.
Mentions: Protein/peptide markers were chosen based on the following considerations: (i) significantly differentially expressed peptides, especially those exhibiting a time- or dose-dependent pattern; and (ii) peptides that were able to accurately classify the control group and disease groups. Taking these into account, three differential peptides (1692.71 m/z, 1886.86 m/z and 1779.28 m/z) were selected for peptide identification. By comparing areas of the selected peptide peaks, we found that the peptides to which 1779 m/z and 1692.71 m/z corresponded showed similar expression patterns (Figure 2), with the lowest expression level in DLBP, followed by CLBP, N and LDH. Their expression levels differed significantly different between DLBP and the other three groups (Ps < 0.05 for all), but not between CLBP of unknown origin, LDH and N. The peptide peak at 1886 m/z peptide had the largest area in DLBP, followed by CLBP of unknown origin, LDH and N. Its expression level also differed significantly between DLBP and the other three groups (P < 0.05), but not between CLBP of unknown origin, LDH and N. MS/MS analysis of the peaks at 1779 and 1692 m/z detected most b and y ions (Figure 3A and B) and the sequences of these two peptides were S.SKITHRIHWESASLL.R and S.KITHRIHWESASLL.R, which corresponded to complement C3 and complement C3 precursor, respectively. The peak at 1886 m/z was analyzed by MS/MS and the sequence of this peptide was identified as R.HRHPDEAAFFDTASTGK.T, which is unique to fibrinogen alpha chain precursor (Figure 3C).

Bottom Line: The discriminative ability of two most significantly differential peaks was poor in classifying DLBP vs.Our findings benefit not only the diagnosis of CLBP but also the understanding of the differences between different forms of DLBP.The ability to distinguish between different causes of CLBP and the identification of serum biomarkers may be of great value to diagnose different causes of DLBP and predict treatment efficacy.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Orthopedics, First Affiliated Hospital of Medical College of Xi'an Jiaotong University, Xi'an 710061, China. zyingang@mail.xjtu.edu.cn.

ABSTRACT

Background: The identification of the cause of chronic low back pain (CLBP) represents a great challenge to orthopedists due to the controversy over the diagnosis of discogenic low back pain (DLBP) and the existence of a number of cases of CLBP of unknown origin. This study aimed to develop diagnostic models to distinguish DLBP from other forms of CLBP and to identify serum biomarkers for DLBP.

Methods: Serum samples were collected from patients with DLBP, chronic lumbar disc herniation (LDH), or CLBP of unknown origin, and healthy controls (N), and randomly divided into a training set (n = 30) and a blind test set (n = 30). Matrix-assisted laser desorption ionization time-of-flight mass spectrometry was performed for protein profiling of these samples. After the discriminative ability of two most significantly differential peaks from each two groups was assessed using scatter plots, classification models were developed using differential peptide peaks to evaluate their diagnostic accuracy. The identity of peptides corresponding to three representative differential peaks was analyzed.

Results: The fewest statistically significant differential peaks were identified between DLBP and CLBP (3), followed by CLBP vs. N (5), DLBP vs. N (9), LDH vs. CLBP (20), DLBP vs. LDH (23), and LDH vs. N (43). The discriminative ability of two most significantly differential peaks was poor in classifying DLBP vs. CLBP but good in classifying DLBP vs. LDH. The accuracy of models for classification of DLBP vs. CLBP was not very high in the blind test (forecasting ability, 67.24%; sensitivity, 70%), although a higher accuracy was observed for classification of DLBP vs. LDH and LDH vs. N (forecasting abilities, ~90%; sensitivities, >90%). A further investigation of three representative differential peaks led to the identification of two peaks as peptides of complement C3, and one peak as a human fibrinogen peptide.

Conclusions: Our findings benefit not only the diagnosis of CLBP but also the understanding of the differences between different forms of DLBP. The ability to distinguish between different causes of CLBP and the identification of serum biomarkers may be of great value to diagnose different causes of DLBP and predict treatment efficacy.

Show MeSH
Related in: MedlinePlus