Bottlenecks in Proteomics

Characterization of natural peptides in Pheretima by integrating proteogenomics and label-free peptidomics

Pheretima, also called "earthworms", is a well-known animal-derived traditional Chinese medicine that is extensively used in over 50 Chinese patent medicines (CPMs) in Chinese Pharmacopoeia (2020 edition). However, its zoological origin is unclear, both in the herbal market and CPMs. In this study, a strategy for integrating in-house annotated protein databases constructed from close evolutionary relationship-sourced RNA sequencing data from public archival resources and various sequencing algorithms (restricted search, open search, and de novo) was developed to characterize the phenotype of natural peptides of three major commercial species of Pheretima, including Pheretima aspergillum (PA), Pheretima vulgaris (PV), and Metaphire magna (MM). We identified 10,477 natural peptides in the PA, 7,451 in PV, and 5,896 in MM samples. Five specific signature peptides were screened and then validated using synthetic peptides; these demonstrated robust specificity for the authentication of PA, PV, and MM. Finally, all marker peptides were successfully applied to identify the zoological origins of Brain Heart capsules and Xiaohuoluo pills, revealing the inconsistent Pheretima species used in these CPMs. In conclusion, our integrated strategy could be used for the in-depth characterization of natural peptides of other animal-derived traditional Chinese medicines, especially non-model species with poorly annotated protein databases.

Identification of Five Gelatins Based on Marker Peptides from Type I Collagen by Mass Spectrum in Multiple Reaction Monitoring Mode

In this study, a novel pseudo-targeted peptidomics strategy, integrating the transition list generated by an in-house software (Pep-MRMer) and the retention time transfer by high-abundance ion-based retention time calibration (HAI-RT-cal), was developed to screen marker peptides of gelatins from five closely related animal species, including porcine, bovine, horse, mule, and donkey. Five marker peptides were screened from the molecular phenotypic differences of type I collagen. Furthermore, a simple and robust 10 min multiple reaction monitoring (MRM) method was established and performed well in distinguishing different gelatins, particularly in discerning horse-hide gelatin (HHG) and mule-hide gelatin (MHG) from donkey-hide gelatin (DHG). The market investigation revealed the serious adulteration of DHG. Meantime, the pseudo-targeted peptidomics could be used to screen marker peptides of other gelatin foods.

CONSTANd: An Efficient Normalization Method for Relative Quantification in Small- and Large-Scale Omics Experiments in R BioConductor and Python

For differential expression studies in all omics disciplines, data normalization is a crucial step that is often subject to a balance between speed and effectiveness. To keep up with the data produced by high-throughput instruments, researchers require fast and easy-to-use yet effective methods that fit into automated analysis pipelines. The CONSTANd normalization method meets these criteria, so we have made its source code available for R/BioConductor and Python. We briefly review the method and demonstrate how it can be used in different omics contexts for experiments of any scale. Widespread adoption across omics disciplines would ease data integration in multiomics experiments.

The intricacy of biomarker complexity-the identification of a genuine proteomic biomarker is more complicated than believed

Several reasons have been put forward to explain the irreproducibility of proteomic biomarker search. However, these reasons pertain to almost every part of biomarker search across the entire analytical workflow but are entirely experimental or methodological. However, in this article we point out that there is a further cause of such irreproducibility. This is not an additional methodological or experimental cause but arises directly from the biology of protein expression. It arises from the fact that disease changes the diversity within protein families. This cause of irreproducibility has been very little studied in relation to proteomic biomarker search. Gene expression is highly variable even in healthy people. Therefore, multiple proteoforms are also to be expected when gene expression is disrupted by disease, proteoforms that may be differently altered by pathology. In consequence, it is illogical to expect that the whole protein family produces a reliably usable biomarker. It is more reasonable to expect that a specific proteoform fulfills this role. Appropriate sample pre-fractionation methods and data analyses could help to identify this version, carrying the modification or the epitope required.

Bioinformatics analysis of differentially expressed proteins in prostate cancer based on proteomics data

We mined the literature for proteomics data to examine the occurrence and metastasis of prostate cancer (PCa) through a bioinformatics analysis. We divided the differentially expressed proteins (DEPs) into two groups: the group consisting of PCa and benign tissues (P&b) and the group presenting both high and low PCa metastatic tendencies (H&L). In the P&b group, we found 320 DEPs, 20 of which were reported more than three times, and DES was the most commonly reported. Among these DEPs, the expression levels of FGG, GSN, SERPINC1, TPM1, and TUBB4B have not yet been correlated with PCa. In the H&L group, we identified 353 DEPs, 13 of which were reported more than three times. Among these DEPs, MDH2 and MYH9 have not yet been correlated with PCa metastasis. We further confirmed that DES was differentially expressed between 30 cancer and 30 benign tissues. In addition, DEPs associated with protein transport, regulation of actin cytoskeleton, and the extracellular matrix (ECM)–receptor interaction pathway were prevalent in the H&L group and have not yet been studied in detail in this context. Proteins related to homeostasis, the wound-healing response, focal adhesions, and the complement and coagulation pathways were overrepresented in both groups. Our findings suggest that the repeatedly reported DEPs in the two groups may function as potential biomarkers for detecting PCa and predicting its aggressiveness. Furthermore, the implicated biological processes and signaling pathways may help elucidate the molecular mechanisms of PCa carcinogenesis and metastasis and provide new targets for clinical treatment.

Comparative two-dimensional polyacrylamide gel electrophoresis of the salivary proteome of children with autism spectrum disorder

In the last decades, prevalence of autism spectrum disorder (ASD) has been on the rise. However, clear aetiology is still elusive and improvements in early diagnosis are needed. To uncover possible biomarkers present in ASD, we used two-dimensional polyacrylamide gel electrophoresis and nanoliquid chromatography-tandem mass spectrometry (nanoLC-MS/MS), to compare salivary proteome profiling of children with ASD and controls. A total of 889 spots were compared and only those spots with a fold change ≥1.7 and a P-value <0.05 or a fold change of ≥3.0 between ASD cases and controls were analysed by nanoLC-MS/MS. Alpha-amylase, CREB-binding protein, p532, Transferrin, Zn alpha2 glycoprotein, Zymogen granule protein 16, cystatin D and plasminogen were down-regulated in ASD. Increased expression of proto-oncogene Frequently rearranged in advanced T-cell lymphomas 1 (FRAT1), Kinesin family member 14, Integrin alpha6 subunit, growth hormone regulated TBC protein 1, parotid secretory protein, Prolactin-inducible protein precursor, Mucin-16, Ca binding protein migration inhibitory factor-related protein 14 (MRP14) was observed in individuals with ASD. Many of the identified proteins have previously been linked to ASD or were proposed as risk factors of ASD at the genetic level. Some others are involved in pathological pathways implicated in ASD causality such as oxidative stress, lipid and cholesterol metabolism, immune system disturbances and inflammation. These data could contribute to protein signatures for ASD presence, risk and subtypes, and advance understanding of ASD cause as well as provide novel treatment targets for ASD.

SuperQuant: A Data Processing Approach to Increase Quantitative Proteome Coverage

SuperQuant is a quantitative proteomics data processing approach that uses complementary fragment ions to identify multiple coisolated peptides in tandem mass spectra allowing for their quantification. This approach can be applied to any shotgun proteomics data set acquired with high mass accuracy for quantification at the MS(1) level. The SuperQuant approach was developed and implemented as a processing node within the Thermo Proteome Discoverer 2.x. The performance of the developed approach was tested using dimethyl-labeled HeLa lysate samples having a ratio between channels of 10(heavy):4(medium):1(light). Peptides were fragmented with collision-induced dissociation using isolation windows of 1, 2, and 4 Th while recording data both with high-resolution and low-resolution. The results obtained using SuperQuant were compared to those using the conventional ion trap-based approach (low mass accuracy MS(2) spectra), which is known to achieve high identification performance. Compared to the common high-resolution approach, the SuperQuant approach identifies up to 70% more peptide-spectrum matches (PSMs), 40% more peptides, and 20% more proteins at the 0.01 FDR level. It identifies more PSMs and peptides than the ion trap-based approach. Improvements in identifications resulted in up to 10% more PSMs, 15% more peptides, and 10% more proteins quantified on the same raw data. The developed approach does not affect the accuracy of the quantification and observed coefficients of variation between replicates of the same proteins were close to the values typical for other precursor ion-based quantification methods. The raw data is deposited to ProteomeXchange (PXD001907). The developed node is available for testing at https://github.com/caetera/SuperQuantNode.