New Insights into the Disease Progression Control Mechanisms by Comparing Long-Term-Nonprogressors versus Normal-Progressors among HIV-1-Positive Patients Using an Ion Current-Based MS1 Proteomic Profiling

Human peripheral blood mononuclear cells as a valuable source of disease-related biomarkers: Evidence from comparative proteomics studies

PURPOSE

The discovery of specific and sensitive disease-associated biomarkers for early diagnostic purposes of many diseases is still highly challenging due to various complex molecular mechanisms triggered, high variability of disease-related interactions, and an overlap of manifestations among diseases. Human peripheral blood mononuclear cells (PBMCs) contain protein signatures corresponding to essential immunological interplay. Certain diseases stimulate PBMCs and contribute towards modulation of their proteome which can be effectively identified and evaluated via the comparative proteomics approach.

EXPERIMENTAL DESIGN

In this review, we made a detailed survey of the PBMCS-derived protein biomarker candidates for a variety of diseases, published in the last 15 years. Articles were preselected to include only comparative proteomics studies.

RESULTS

PBMC-derived biomarkers were investigated for cancer, glomerular, neurodegenerative/neurodevelopmental, psychiatric, chronic inflammatory, autoimmune, endocrinal, infectious, and other diseases. A detailed review of these studies encompassed the proteomics platforms, proposed candidate biomarkers, their immune cell type specificity, and potential clinical application.

CONCLUSIONS

Overall, PBMCs have shown a solid potential in giving early diagnostic and prognostic biomarkers for many diseases. The future of PBMC biomarker research should reveal its full potential through well-designed comparative studies and extensive testing of the most promising protein biomarkers identified so far.

High-quality and robust protein quantification in large clinical/pharmaceutical cohorts with IonStar proteomics investigation

Robust, reliable quantification of large sample cohorts is often essential for meaningful clinical or pharmaceutical proteomics investigations, but it is technically challenging. When analyzing very large numbers of samples, isotope labeling approaches may suffer from substantial batch effects, and even with label-free methods, it becomes evident that low-abundance proteins are not reliably measured owing to unsufficient reproducibility for quantification. The MS1-based quantitative proteomics pipeline IonStar was designed to address these challenges. IonStar is a label-free approach that takes advantage of the high sensitivity/selectivity attainable by ultrahigh-resolution (UHR)-MS1 acquisition (e.g., 120-240k full width at half maximum at m/z = 200) which is now widely available on ultrahigh-field Orbitrap instruments. By selectively and accurately procuring quantitative features of peptides within precisely defined, very narrow m/z windows corresponding to the UHR-MS1 resolution, the method minimizes co-eluted interferences and substantially enhances signal-to-noise ratio of low-abundance species by decreasing noise level. This feature results in high sensitivity, selectivity, accuracy and precision for quantification of low-abundance proteins, as well as fewer missing data and fewer false positives. This protocol also emphasizes the importance of well-controlled, robust experimental procedures to achieve high-quality quantification across a large cohort. It includes a surfactant cocktail-aided sample preparation procedure that achieves high/reproducible protein/peptide recoveries among many samples, and a trapping nano-liquid chromatography-mass spectrometry strategy for sensitive and reproducible acquisition of UHR-MS1 peptide signal robustly across a large cohort. Data processing and quality evaluation are illustrated using an example dataset ( http://proteomecentral.proteomexchange.org ), and example results from pharmaceutical project and one clinical project (patients with acute respiratory distress syndrome) are shown. The complete IonStar pipeline takes ~1-2 weeks for a sample cohort containing ~50-100 samples.

MS1 ion current-based quantitative proteomics: A promising solution for reliable analysis of large biological cohorts

The rapidly-advancing field of pharmaceutical and clinical research calls for systematic, molecular-level characterization of complex biological systems. To this end, quantitative proteomics represents a powerful tool but an optimal solution for reliable large-cohort proteomics analysis, as frequently involved in pharmaceutical/clinical investigations, is urgently needed. Large-cohort analysis remains challenging owing to the deteriorating quantitative quality and snowballing missing data and false-positive discovery of altered proteins when sample size increases. MS1 ion current-based methods, which have become an important class of label-free quantification techniques during the past decade, show considerable potential to achieve reproducible protein measurements in large cohorts with high quantitative accuracy/precision. Nonetheless, in order to fully unleash this potential, several critical prerequisites should be met. Here we provide an overview of the rationale of MS1-based strategies and then important considerations for experimental and data processing techniques, with the emphasis on (i) efficient and reproducible sample preparation and LC separation; (ii) sensitive, selective and high-resolution MS detection; iii)accurate chromatographic alignment; (iv) sensitive and selective generation of quantitative features; and (v) optimal post-feature-generation data quality control. Prominent technical developments in these aspects are discussed. Finally, we reviewed applications of MS1-based strategy in disease mechanism studies, biomarker discovery, and pharmaceutical investigations.

Surfactant Cocktail-Aided Extraction/Precipitation/On-Pellet Digestion Strategy Enables Efficient and Reproducible Sample Preparation for Large-Scale Quantitative Proteomics

For quantitative proteomics, efficient, robust, and reproducible sample preparation with high throughput is critical yet challenging, especially when large cohorts are involved, as is often required by clinical/pharmaceutical studies. We describe a rapid and straightforward surfactant cocktail-aided extraction/precipitation/on-pellet digestion (SEPOD) strategy to address this need. Prior to organic solvent precipitation and on-pellet digestion, SEPOD treats samples with a surfactant cocktail (SC) containing multiple nonionic/anionic surfactants, which achieves (i) exhaustive/reproducible protein extraction, including membrane-bound proteins; (ii) effective removal of detrimental nonprotein matrix components (e.g., >94% of phospholipids); (iii) rapid/efficient proteolytic digestion owing to dual (surfactants + precipitation) denaturation. The optimal SC composition and concentrations were determined by Orthogonal-Array-Design investigation of their collective/individuals effects on protein extraction/denaturation. Key parameters for cleanup and digestion were experimentally identified as well. The optimized SEPOD procedures allowed a rapid 6 h digestion providing a clean digest with high peptide yields and excellent quantitative reproducibility (especially low-abundance proteins). Compared with filter-assisted sample preparation (FASP) and in-solution digestion, SEPOD showed superior performance by recovering substantially more peptide/proteins (including integral membrane proteins), yielding significantly higher peptide intensities and improving quantification for peptides with extreme physicochemical properties. SEPOD was further applied in a large-cohort temporal investigation of 44 IAV-infected mouse lungs, providing efficient and reproducible peptide yields (77.9 ± 4.6%) across all samples. With the IonStar pipeline, >6 400 unique protein groups were quantified (≥2 peptide/protein, peptide-FDR < 0.05%), ∼99% without missing data in any sample with <7% technical median-intragroup CV. Altered proteome patterns revealed interesting novel insights into pathophysiological changes by IAV infection. In summary, SEPOD offers a feasible solution for rapid, efficient, and reproducible preparation of biological samples, facilitating high-quality proteomic quantification of large sample cohorts.

Proteomic Analysis of Combined Gemcitabine and Birinapant in Pancreatic Cancer Cells

Pancreatic cancer is characterized by mutated signaling pathways and a high incidence of drug resistance. Comprehensive, large-scale proteomic analysis can provide a system-wide view of signaling networks, assist in understanding drug mechanisms of action and interactions, and serve as a useful tool for pancreatic cancer research. In this study, liquid chromatography-mass spectrometry-based proteomic analysis was applied to characterize the combination of gemcitabine and birinapant in pancreatic cancer cells, which was shown previously to be synergistic. A total of 4069 drug-responsive proteins were identified and quantified in a time-series proteome analysis. This rich dataset provides broad views and accurate quantification of signaling pathways. Pathways relating to DNA damage response regulations, DNA repair, anti-apoptosis, pro-migration/invasion were implicated as underlying mechanisms for gemcitabine resistance and for the beneficial effects of the drug combination. Promising drug targets were identified for future investigation. This study also provides a database for systems mathematical modeling to relate drug effects and interactions in various signaling pathways in pancreatic cancer cells.

Identification of miRNA-mRNA crosstalk in CD4+ T cells during HIV-1 infection by integrating transcriptome analyses

Background

HIV-1-infected long-term nonprogressors (LTNPs) are characterized by infection with HIV-1 more than 7–10 years, but keeping high CD4⁺ T cell counts and low viral load in the absence of antiretroviral treatment, while loss of CD4⁺ T cells and high viral load were observed in the most of HIV-1-infected individuals with chronic progressors (CPs) However, the mechanisms of different clinical outcomes in HIV-1 infection needs to be further resolved.

Methods

To identify microRNAs (miRNAs) and their target genes related to distinct clinical outcomes in HIV-1 infection, we performed the integrative transcriptome analyses in two series GSE24022 and GSE6740 by GEO2R, R, TargetScan, miRDB, and Cytoscape softwares. The functional pathways of these differentially expressed miRNAs (DEMs) targeting genes were further analyzed with DAVID.

Results

We identified that 7 and 19 DEMs in CD4⁺ T cells of LTNPs and CPs, respectively, compared with uninfected controls (UCs), but only miR-630 was higher in CPs than that in LTNPs. Further, 478 and 799 differentially expressed genes (DEGs) were identified in the group of LTNPs and CPs, respectively, compared with UCs. Compared to CPs, four hundred and twenty-four DEGs were identified in LTNPs. Functional pathway analyses revealed that a close connection with miRNA-mRNA in HIV-1 infection that DEGs were involved in response to virus and immune system process, and RIG-I-like receptor signaling pathway, whose DEMs or DEGs will be novel biomarkers for prediction of clinical outcomes and therapeutic targets for HIV-1.

Conclusions

Integrative transcriptome analyses showed that distinct transcriptional profiles in CD4⁺ T cells are associated with different clinical outcomes during HIV-1 infection, and we identified a circulating miR-630 with potential to predict disease progression, which is necessary to further confirm our findings in the future.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-017-1130-y) contains supplementary material, which is available to authorized users.