Metaprotein expression modeling for label-free quantitative proteomics

Quantitative label-free proteomic analysis of human follicle fluid to identify novel candidate protein biomarker for endometriosis-associated infertility

BACKGROUND

Endometriosis (EM) leads to a decline in fertility, which is characterized by a decrease in the number and quality of follicles, and thus has a negative impact on in vitro fertilization (IVF) outcomes. However, the mechanism of how EM affects oocytes and leads to infertility remains unclear. As a potentially available sample directly related to oocyte growth, follicular fluid (FF) has important research value. Evaluating the association of FF content and EM-associated infertility through proteomics may helpful to explore the possible pathogenesis of EM-associated infertility.

METHODS

In the present experimental study, from August 2019 to June 2020, FF samples were obtained as control group (CON-G; n = 10) from women with no one female factor of infertility and were undergoing IVF due to other reasons, 20 women with EM-associated infertility undergoing IVF with no other female factors were distributed into the EM group according to the time for IVF: (i) EM-group 1 (EM-G1, Stage I to Stage III, n = 10); (ii) EM-group 2 (EM-G2, Stage I to Stage III, n = 10). label-free quantitative proteomics (LFQP) technology and parallel reaction monitoring (PRM) approach were combined to aid in identifying and validating FF protein biomarkers for EM-associated infertility. In PRM analysis, another 20 subjects were enrolled as EM-associated infertility group (EM,Stage I to Stage III, n = 10) and controls (CON, n = 10) within the same time and inclusion criteria are the same as previously described. Finally, a potential protein biomarker panel of FF differential expressed proteins to EM-associated infertility was also evaluated by t-test and receiver operating characteristic (ROC) curve and binary Logistic regression models.

RESULTS

7 significant differential expressed proteins which closely related to EM-associated infertility were found by LFQP technology, among which immunoglobulin lambda variable 7-46 (IGLV7-46), Immunoglobulin heavy constant gamma 2 (IGHG2), glia-derived nexin (GDN) and Inter-alpha-trypsin inhibitor heavy chain H3 (ITIH3) were significantly up-regulated (p < 0.05), while corticosteroid-binding globulin (CBG), angiotensinogen (AGT) and Fetuin-B (FETUB) were significantly down regulated (p < 0.05). Additionally, GDN and AGT was identified as a potential protein biomarker by further PRM analysis for EM-associated infertility according to ROC curve analysis and t-test (p < 0.05), the area under the curve (AUC) for GDN and AGT was 0.78 and 0.69 with optimum sensitivity of 50%, 70% and specificity of 100%, 90%, respectively. According to binary logistic regression and evaluated ROC analysis, the AUC for the combination of GDN and AGT was 0.80.

CONCLUSIONS

To the best of our knowledge, this is the first time that elevated GDN protein levels have been found in the FF of patients with EM-associated infertility. Combining LFQP technology and PRM method we found the abnormal of GDN and AGT in FF may be the potential cause of EM-associated infertility which may help to better understand the physiological and pathological mechanism of EM-associated infertility. Further experimental studies are required to confirm their mechanism in EM-associated infertility. The results of this study are also consistent with the previous conclusion that EM is a chronic inflammatory disease.

SIGNIFICANCE

To the best of our knowledge, this is the first time that elevated GDN protein levels have been found in the follicular fluid of patients with EM-associated infertility. Combining LFQP technology and PRM methods we found the abnormal of GDN and AGT protein in FF may be the potential cause of EM-associated infertility which may help to better understand the physiological and pathological mechanism of EM-associated infertility. Clinically, it has been recognized that EM is related to infertility, but the mechanism remains unclear. Our study combines label-free quantitative proteomics technology and parallel reaction monitoring methods to identify and verify the FF protein biomarkers of EM-associated infertility, which provides a good research method for follow-up research.

LC-MSE, Multiplex MS/MS, Ion Mobility, and Label-Free Quantitation in Clinical Proteomics

Proteomic tools can only be implemented in clinical settings if high-throughput, automated, sensitive, and accurate methods are developed. This has driven researchers to the edge of mass spectrometry (MS)-based proteomics capacity. Here we provide an overview of recent achievements in mass spectrometric technologies and instruments. This includes development of high and ultra definition-MS^E (HDMS^E and UDMS^E) through implementation of ion mobility (IM) MS towards sensitive and accurate label-free proteomics using ultra performance liquid chromatography (UPLC). Label free UPLC-HDMS^E is less expensive than labeled-based quantitative proteomics and has no limits regarding the number of samples that can be analyzed and compared, which is an important requirement for supporting clinical applications.

Clinical proteomics in cancer: Where we are

Proteomics has emerged as a promising field in the post-genomic era. Notwithstanding the great advances provided by gene expression analysis in cancer, the lack of a correlation between gene expression and protein levels has highlighted the need for a proteomic focus on cancer. Although the increasing knowledge regarding cancer biology, a reliable marker to improve diagnosis, prognosis and treatment for cancer patients is not a reality at present. In this review, we address the main considerations regarding proteomics-based studies and their clinical applications on cancer research, highlighting some considerations related to strengths and limitations of proteomics-based studies and its application to clinical practice.

QuantFusion: Novel Unified Methodology for Enhanced Coverage and Precision in Quantifying Global Proteomic Changes in Whole Tissues

Single quantitative platforms such as label-based or label-free quantitation (LFQ) present compromises in accuracy, precision, protein sequence coverage, and speed of quantifiable proteomic measurements. To maximize the quantitative precision and the number of quantifiable proteins or the quantifiable coverage of tissue proteomes, we have developed a unified approach, termed QuantFusion, that combines the quantitative ratios of all peptides measured by both LFQ and label-based methodologies. Here, we demonstrate the use of QuantFusion in determining the proteins differentially expressed in a pair of patient-derived tumor xenografts (PDXs) representing two major breast cancer (BC) subtypes, basal and luminal. Label-based in-spectra quantitative peptides derived from amino acid-coded tagging (AACT, also known as SILAC) of a non-malignant mammary cell line were uniformly added to each xenograft with a constant predefined ratio, from which Ratio-of-Ratio estimates were obtained for the label-free peptides paired with AACT peptides in each PDX tumor. A mixed model statistical analysis was used to determine global differential protein expression by combining complementary quantifiable peptide ratios measured by LFQ and Ratio-of-Ratios, respectively. With minimum number of replicates required for obtaining the statistically significant ratios, QuantFusion uses the distinct mechanisms to "rescue" the missing data inherent to both LFQ and label-based quantitation. Combined quantifiable peptide data from both quantitative schemes increased the overall number of peptide level measurements and protein level estimates. In our analysis of the PDX tumor proteomes, QuantFusion increased the number of distinct peptide ratios by 65%, representing differentially expressed proteins between the BC subtypes. This quantifiable coverage improvement, in turn, not only increased the number of measurable protein fold-changes by 8% but also increased the average precision of quantitative estimates by 181% so that some BC subtypically expressed proteins were rescued by QuantFusion. Thus, incorporating data from multiple quantitative approaches while accounting for measurement variability at both the peptide and global protein levels make QuantFusion unique for obtaining increased coverage and quantitative precision for tissue proteomes.

Proteomic analysis of human bronchoalveolar lavage fluid after subsgemental exposure

The analysis of airway fluid, as sampled by bronchoalveolar lavage (BAL), provides a minimally invasive route to interrogate lung biology in health and disease. Here, we used immunodepletion, coupled with gel- and label-free LC-MS/MS, for quantitation of the BAL fluid (BALF) proteome in samples recovered from human subjects following bronchoscopic instillation of saline, lipopolysaccharide (LPS) or house dust mite antigen into three distinct lung subsegments. Among more than 200 unique proteins quantified across nine samples, neutrophil granule-derived and acute phase proteins were most highly enriched in the LPS-exposed lobes. Of these, peptidoglycan response protein 1 was validated and confirmed as a novel marker of neutrophilic inflammation. Compared to a prior transcriptomic analysis of airway cells in this same cohort, the BALF proteome revealed a novel set of response factors. Independent of exposure, the enrichment of tracheal-expressed proteins in right lower lung lobes suggests a potential for constitutive intralobar variability in the BALF proteome; sampling of multiple lung subsegments also appears to aid in the identification of protein signatures that differentiate individuals at baseline. Collectively, this proof-of-concept study validates a robust workflow for BALF proteomics and demonstrates the complementary nature of proteomic and genomic techniques for investigating airway (patho)physiology.

Design and application of a data-independent precursor and product ion repository

The functional design and application of a data-independent LC-MS precursor and product ion repository for protein identification, quantification, and validation is conceptually described. The ion repository was constructed from the sequence search results of a broad range of discovery experiments investigating various tissue types of two closely related mammalian species. The relative high degree of similarity in protein complement, ion detection, and peptide and protein identification allows for the analysis of normalized precursor and product ion intensity values, as well as standardized retention times, creating a multidimensional/orthogonal queryable, qualitative, and quantitative space. Peptide ion map selection for identification and quantification is primarily based on replication and limited variation. The information is stored in a relational database and is used to create peptide- and protein-specific fragment ion maps that can be queried in a targeted fashion against the raw or time aligned ion detections. These queries can be conducted either individually or as groups, where the latter affords pathway and molecular machinery analysis of the protein complement. The presented results also suggest that peptide ionization and fragmentation efficiencies are highly conserved between experiments and practically independent of the analyzed biological sample when using similar instrumentation. Moreover, the data illustrate only minor variation in ionization efficiency with amino acid sequence substitutions occurring between species. Finally, the data and the presented results illustrate how LC-MS performance metrics can be extracted and utilized to ensure optimal performance of the employed analytical workflows.