Multidimensional protein identification technology

Growing of fungi on the stored low denatured defatted soybean meals and the hydrolysis of proteins and isoflavone glycosides by fungal enzymes

Recently, more and more attention has been paid to the effects of fungal contamination and fungal enzymes secreted in raw grain on product quality. As the starting material of protein and active components, the quality of low denatured defatted soybean meals (LDSM) directly determines the qualities of subsequent products. In previous studies, we have revealed that infection with Aspergillus ochraceus protease causes significant hydrolysis of proteins. In this study, growing of fungi on the stored low denatured defatted soybean meals (LDSM) was analyzed by high-throughput sequencing and real-time PCR, which revealed that the abundance of Aspergillus increased significantly after storage. Twenty fungal proteases and 9 fungal glucosidases were found in stored LDSM and zymography showed that the proteases were of serine-type with some cysteine and aspartic activities. Proteolysis of the soybean storage proteins mainly occurred after the hydration of LDSM and the average molecular weight of soy proteins decreased from 57.9 kDa to 30.7 kDa after 60 min's of hydrolysis. Two-dimensional electrophoresis (2-DE) analysis found the polypeptide fragments from soybean 7S and 11S proteins with molecular weight around 10-25 kDa in the hydrated LDSM. Glycosylated isoflavones were hydrolyzed in both dry and hydrated stored LDSM which resulted in significant (p < 0.05) increase in the contents of isoflavone aglycones. This study suggested that fungi contamination be a new factor affecting the properties of LDSM derived soy protein products.

Proteomic analysis of germinal vesicles in the domestic cat model reveals candidate nuclear proteins involved in oocyte competence acquisition

STUDY QUESTION

Do nuclear proteins in the germinal vesicle (GV) contribute to oocyte competence acquisition during folliculogenesis?

SUMMARY ANSWER

Proteomic analysis of GVs identified candidate proteins for oocyte competence acquisition, including a key RNA processing protein-heterogeneous nuclear ribonucleoprotein A2/B1 (hnRNPA2B1).

WHAT IS KNOWN ALREADY

The domestic cat GV, which is physiologically similar to the human GV, gains the intrinsic ability to resume meiosis and support early embryo development during the pre-antral-to-antral follicle transition. However, little is known about nuclear proteins that contribute to this developmental process.

STUDY DESIGN SIZE, DURATION

GVs were enriched from pre-antral (incompetent) and antral (competent) follicles from 802 cat ovaries. Protein lysates were subjected to quantitative proteomic analysis to identify differentially expressed proteins in GVs from the two follicular categories.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Two biological replicates (from independent pools of ovaries) of pre-antral versus antral samples were labeled by tandem mass tags and then assessed by liquid chromatography-tandem mass spectrometry. Proteomic data were analyzed according to gene ontology and a protein-protein interaction network. Immunofluorescent staining and protein inhibition assays were used for validation.

MAIN RESULTS AND THE ROLE OF CHANCE

A total of 174 nuclear proteins was identified, with 54 being up-regulated and 22 down-regulated (≥1.5-fold) after antrum formation. Functional protein analysis through gene ontology over-representation tests revealed that changes in molecular network within the GVs during this transitional phase were related to chromatin reorganization, gene transcription, and maternal RNA processing and storage. Protein inhibition assays verified that hnRNPA2B1, a key nuclear protein identified, was required for oocyte meiotic maturation and subsequent blastocyst formation.

LARGE SCALE DATA

Data are available via ProteomeXchange with identifier PXD007211.

LIMITATIONS REASONS FOR CAUTION

Proteins identified by proteomic comparison may (i) be involved in processes other than competence acquisition during the pre-antral-to-antral transition or (ii) be co-expressed in other macrostructures besides the GV. Expressional and functional validations should be performed for candidate proteins before downstream application.

WIDER IMPLICATIONS OF THE FINDINGS

Collective results generated a blueprint to better understand the molecular mechanisms involved in GV competence acquisition and identified potential nuclear competence markers for human fertility preservation.

STUDY FUNDING AND COMPETING INTEREST(S)

Funded by the National Center for Research Resources (R01 RR026064), a component of the National Institutes of Health (NIH) and currently by the Office of Research Infrastructure Programs/Office of the Director (R01 OD010948). The authors declare that there is no conflict of interest.

Off-Line Multidimensional Liquid Chromatography and Auto Sampling Result in Sample Loss in LC/LC-MS/MS

Large-scale proteomics often employs two orthogonal separation methods to fractionate complex peptide mixtures. Fractionation can involve ion exchange separation coupled to reversed-phase separation or, more recently, two reversed-phase separations performed at different pH values. When multidimensional separations are combined with tandem mass spectrometry for protein identification, the strategy is often referred to as multidimensional protein identification technology (MudPIT). MudPIT has been used in either an automated (online) or manual (offline) format. In this study, we evaluated the performance of different MudPIT strategies by both label-free and tandem mass tag (TMT) isobaric tagging. Our findings revealed that online MudPIT provided more peptide/protein identifications and higher sequence coverage than offline platforms. When employing an off-line fractionation method with direct loading of samples onto the column from an eppendorf tube via a high-pressure device, a 5.3% loss in protein identifications is observed. When off-line fractionated samples are loaded via an autosampler, a 44.5% loss in protein identifications is observed compared with direct loading of samples onto a triphasic capillary column. Moreover, peptide recovery was significantly lower after offline fractionation than in online fractionation. Signal-to-noise (S/N) ratio, however, was not significantly altered between experimental groups. It is likely that offline sample collection results in stochastic peptide loss due to noncovalent adsorption to solid surfaces. Therefore, the use of the offline approaches should be considered carefully when processing minute quantities of valuable samples.

Genomics and proteomics of pulmonary vascular disease

Study of RNA and proteins in cells of both normal and diseased tissues is providing researchers with new knowledge of disease pathologies. While still in its early stages, high-throughput expression analysis is improving our understanding of the pathogenesis of pulmonary arterial hypertension (PAH). While many studies have used microarray and proteomic analyses as "hypothesis-generating" tools, the technologies also have potential to identify and quantify biomarkers of disease. To date, many of the published studies have examined gene expression profiles of tissue biopsies, others have utilized cells from peripheral blood. Microarray technology has been employed successfully in the investigation of a diverse array of human diseases. The potential of high-throughput expression analysis to improve our understanding of the pathogenesis of PAH is highlighted in this review. Proteomic studies of PAH and pulmonary vascular diseases in general have been little utilized thus far. To date, such studies are few and no consistent biomarker has emerged from studies of either plasma or blood cells from idiopathic pulmonary arterial hypertension (IPAH) patients. The studies of both lung tissue and lymphocytes are perhaps more revealing and suggest that changes in the cytoskeletal machinery may play a role in the pathogenesis of idiopathic pulmonary arterial hypertension. The oncology literature has demonstrated the utility of gene microarray analysis to predict important outcomes such as response to therapy and survival. It is likely that in the near future, gene microarrays and proteomic analyses will also be employed in a pharmacogenomics approach in PAH, helping to identify the most appropriate therapies for individual patients.

Mass spectrometric analysis of mucin core proteins

Mucins are difficult to handle for their identification and characterization via proteomic applications due to their heavily glycosylated nature (up to 90%), high molecular weight (200 kDa-200 MDa), and size (Rg 10-300 nm). Their core proteins are extremely large and highly substituted with oligosaccharides, which only allow access to a highly restricted portion of their protein. For this reason, conventional 1D or 2D polyacrylamide gel-based proteomic approaches are not effective for identification and characterization of mucin molecules. In this chapter, we present our current protocol employing a modified shotgun proteomic approach to identify these complex glycoproteins.

Current technological challenges in biomarker discovery and validation

In this review we will give an overview of the issues related to biomarker discovery studies with a focus on liquid chromatography-mass spectrometry (LC-MS) methods. Biomarker discovery is based on a close collaboration between clinicians, analytical scientists and chemometritians/statisticians. It is critical to define the final purpose of a biomarker or biomarker pattern at the onset of the study and to select case and control samples accordingly. This is followed by designing the experiment, starting with the sampling strategy, sample collection, storage and separation protocols, choice and validation of the quantitative profiling platform followed by data processing, statistical analysis and validation workflows. Biomarker candidates that result after statistical validation should be submitted for further validation and, ideally, be connected to the disease mechanism after their identification. Since most discovery studies work with a relatively small number of samples, it is necessary to assess the specificity and sensitivity of a given biomarker-based assay in a larger set of independent samples, preferably analyzed at another clinical center. Targeted analytical methods of higher throughput than the original discovery method are needed at this point and LC-tandem mass spectrometry is gaining acceptance in this field. Throughout this review, we will focus on possible sources of variance and how they can be assessed and reduced in order to avoid false positives and to reduce the number of false negatives in biomarker discovery research.