PILOT_PROTEIN: identification of unmodified and modified proteins via high-resolution mass spectrometry and mixed-integer linear optimization

High-throughput proteomic analysis of candidate biomarker changes in gingival crevicular fluid after treatment of chronic periodontitis

BACKGROUND AND OBJECTIVE

Untargeted, high-throughput proteomics methodologies have great potential to aid in identifying biomarkers for the diagnosis of periodontal disease. The application of such methods to the discovery of candidate biomarkers for the resolution of periodontal inflammation after periodontal therapy has been investigated.

MATERIAL AND METHODS

Gingival crevicular fluid samples were collected from 10 patients diagnosed with chronic periodontitis at baseline and 1, 5, 9 and 13 weeks after completion of mechanical periodontal treatment. Clinical indices of periodontal disease, including probing depth, recession, clinical attachment level and bleeding on probing, were recorded at baseline and 13 weeks. Samples were analyzed using an online liquid chromatography-nanoelectrospray-hybrid ion trap-Orbitrap mass spectrometer. Spectra were processed with the PILOT_PROTEIN proteomics software suite.

RESULTS

Clinical parameters were significantly improved 13 weeks after treatment (Wilcoxon signed ranks test, P < .05). From the substantial number of identified proteins, a small subset was extracted by filter methods that included temporal pattern matching, logistic function fitting and mixed-integer linear optimization. This subset includes azurocidin, lysozyme C and myosin-9 as candidate biomarkers prominent at baseline and alpha-smooth muscle actin as prominent 13 weeks after treatment. Cross-validation studies yielded average predictive accuracy and area under the curve of 0.900 and 0.930, respectively.

CONCLUSION

High-throughput proteomic analysis can contribute to identifying endpoints of periodontal therapy. These candidate biomarkers should be evaluated for clinical efficacy.

Proteomics for the discovery of biomarkers and diagnosis of periodontitis: a critical review

Periodontitis is a common chronic and destructive disease whose pathogenetic mechanisms remain unclear. Due to their sensitivity and global scale, proteomics studies offer the opportunity to uncover critical host and pathogen activity indicators and can elucidate clinically applicable biomarkers for improved diagnosis and treatment of the disease. This review summarizes the literature of proteomics studies on periodontitis and comprehensively discusses commonly found candidate biomarkers. Key considerations in the design of an experimental proteomics platform are also outlined. The applicability of protein biomarkers across the progression of periodontitis and unexplored areas of research are highlighted.

Initial characterization of histone H3 serine 10 O-acetylation

In eukaryotic organisms, histone posttranslational modifications (PTMs) are indispensable for their role in maintaining cellular physiology, often through their mediation of chromatin-related processes such as transcription. Targeted investigations of this ever expanding network of chemical moieties continue to reveal genetic, biochemical, and cellular nuances of this complex landscape. In this study, we present our findings on a novel class of histone PTMs: Serine, Threonine, and Tyrosine O-acetylation. We have combined highly sensitive nano-LC-MS/MS experiments and immunodetection assays to identify and validate these unique marks found only on histone H3. Mass spectrometry experiments have determined that several of these O-acetylation marks are conserved in many species, ranging from yeast to human. Additionally, our investigations reveal that histone H3 serine 10 acetylation (H3S10ac) is potentially linked to cell cycle progression and cellular pluripotency. Here, we provide a glimpse into the functional implications of this H3-specific histone mark, which may be of high value for further studies of chromatin.

Integral membrane proteins and bilayer proteomics

Integral membrane proteins reside within the bilayer membranes that surround cells and organelles, playing critical roles in movement of molecules across them and the transduction of energy and signals. While their extreme amphipathicity presents technical challenges, biological mass spectrometry has been applied to all aspects of membrane protein chemistry and biology, including analysis of primary, secondary, tertiary, and quaternary structures as well as the dynamics that accompany functional cycles and catalysis.

Discovery of biomarker combinations that predict periodontal health or disease with high accuracy from GCF samples based on high-throughput proteomic analysis and mixed-integer linear optimization

AIM

To identify optimal combination(s) of proteomic based biomarkers in gingival crevicular fluid (GCF) samples from chronic periodontitis (CP) and periodontally healthy individuals and validate the predictions through known and blind test sets.

MATERIALS AND METHODS

GCF samples were collected from 96 CP and periodontally healthy subjects and analysed using high-performance liquid chromatography, tandem mass spectrometry and the PILOT_PROTEIN algorithm. A mixed-integer linear optimization (MILP) model was then developed to identify the optimal combination of biomarkers which could clearly distinguish a blind subject sample as healthy or diseased.

RESULTS

A thorough cross-validation of the MILP model capability was performed on a training set of 55 samples and greater than 99% accuracy was consistently achieved when annotating the testing set samples as healthy or diseased. The model was then trained on all 55 samples and tested on two different blind test sets, and using an optimal combination of 7 human proteins and 3 bacterial proteins, the model was able to correctly predict 40 out of 41 healthy and diseased samples.

CONCLUSIONS

The proposed large-scale proteomic analysis and MILP model led to the identification of novel combinations of biomarkers for consistent diagnosis of periodontal status with greater than 95% predictive accuracy.