Two-dimensional gel proteome reference map of human small intestine

ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies

Label-free quantification (LFQ) with a specific and sequentially integrated workflow of acquisition technique, quantification tool and processing method has emerged as the popular technique employed in metaproteomic research to provide a comprehensive landscape of the adaptive response of microbes to external stimuli and their interactions with other organisms or host cells. The performance of a specific LFQ workflow is highly dependent on the studied data. Hence, it is essential to discover the most appropriate one for a specific data set. However, it is challenging to perform such discovery due to the large number of possible workflows and the multifaceted nature of the evaluation criteria. Herein, a web server ANPELA (https://idrblab.org/anpela/) was developed and validated as the first tool enabling performance assessment of whole LFQ workflow (collective assessment by five well-established criteria with distinct underlying theories), and it enabled the identification of the optimal LFQ workflow(s) by a comprehensive performance ranking. ANPELA not only automatically detects the diverse formats of data generated by all quantification tools but also provides the most complete set of processing methods among the available web servers and stand-alone tools. Systematic validation using metaproteomic benchmarks revealed ANPELA's capabilities in 1 discovering well-performing workflow(s), (2) enabling assessment from multiple perspectives and (3) validating LFQ accuracy using spiked proteins. ANPELA has a unique ability to evaluate the performance of whole LFQ workflow and enables the discovery of the optimal LFQs by the comprehensive performance ranking of all 560 workflows. Therefore, it has great potential for applications in metaproteomic and other studies requiring LFQ techniques, as many features are shared among proteomic studies.

A review of the current understanding of nanoparticles protein corona composition

Upon entering into the biological environments, the surface of the nanoparticles is immediately coated with proteins and form the so-called a protein corona due to which a nanoparticle changes its “synthetic” identity to a new “biological” identity. Different types of nanoparticles have different protein binding profiles, which is why they have different protein corona composition and therefore it cannot be said that there is a universal protein corona. The composition and amount of protein in the corona depends on the physical and chemical characteristics of the nanoparticles, the type of biological medium and the exposure time. Protein corona increases the diameter but also changes the composition of the surface of the nanoparticles and these changes affect biodistribution, efficacy, and toxicity of the nanoparticles.

Simultaneous Improvement in the Precision, Accuracy, and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains

The label-free proteome quantification (LFQ) is multistep workflow collectively defined by quantification tools and subsequent data manipulation methods that has been extensively applied in current biomedical, agricultural, and environmental studies. Despite recent advances, in-depth and high-quality quantification remains extremely challenging and requires the optimization of LFQs by comparatively evaluating their performance. However, the evaluation results using different criteria (precision, accuracy, and robustness) vary greatly, and the huge number of potential LFQs becomes one of the bottlenecks in comprehensively optimizing proteome quantification. In this study, a novel strategy, enabling the discovery of the LFQs of simultaneously enhanced performance from thousands of workflows (integrating 18 quantification tools with 3,128 manipulation chains), was therefore proposed. First, the feasibility of achieving simultaneous improvement in the precision, accuracy, and robustness of LFQ was systematically assessed by collectively optimizing its multistep manipulation chains. Second, based on a variety of benchmark datasets acquired by various quantification measurements of different modes of acquisition, this novel strategy successfully identified a number of manipulation chains that simultaneously improved the performance across multiple criteria. Finally, to further enhance proteome quantification and discover the LFQs of optimal performance, an online tool (https://idrblab.org/anpela/) enabling collective performance assessment (from multiple perspectives) of the entire LFQ workflow was developed. This study confirmed the feasibility of achieving simultaneous improvement in precision, accuracy, and robustness. The novel strategy proposed and validated in this study together with the online tool might provide useful guidance for the research field requiring the mass-spectrometry-based LFQ technique.

2-D gel electrophoresis: constructing 2D-gel proteome reference maps

Two-dimensional gel electrophoresis (2-DE) is the most popular and versatile method of protein separation among a rapidly growing array of proteomic technologies. Based on two independent biochemical characteristics of proteins, it combines isoelectric focusing, which separates proteins according to their isoelectric point (pI), and SDS-PAGE, which separates them further according to their molecular mass. An evolution of conventional 2-DE is represented by the 2D-Difference in Gel Electrophoresis (2D-DIGE) that allows sample multiplexing and achieving more accurate and sensitive quantitative proteomic determinations. The 2-DE separation permits the generation of protein maps of different cells or tissues and the study, by differential proteomics, of protein expression changes associated to the different states of a biological system. In order to identify the molecular bases of pathological processes, it is also useful to characterize the physiological protein homeostasis in healthy cells or tissues. On these grounds, the availability of detailed 2D reference maps could be very useful for proteomic studies. The protocol described in this chapter is based on the 2D-DIGE technology and has been applied to obtain the first 2-DE reference map of the human small intestine.

PPAR signaling pathway and cancer-related proteins are involved in celiac disease-associated tissue damage

Celiac disease (CD) is an immune-mediated disorder triggered by the ingestion of wheat gliadin and related proteins in genetically predisposed individuals. To find a proteomic CD diagnostic signature and to gain a better understanding of pathogenetic mechanisms associated with CD, we analyzed the intestinal mucosa proteome alterations using two dimensional difference gel electrophoresis (2D-DIGE) coupled with matrix assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF ms) of CD patients with varying degrees of histological abnormalities defined by Marsh criteria and controls. Our results clearly evidenced the presence of two groups of patients: Group A, including controls and Marsh 0-I CD patients; and Group B, consisting of CD subjects with grade II-III Oberhuber-Marsh classification. Differentially expressed proteins were involved mainly in lipid, protein and sugar metabolism. Interestingly, in Group B, several downregulated proteins (FABP1, FABP2, APOC3, HMGCS2, ACADM and PEPCK) were implicated directly in the peroxisome proliferator-activated receptor (PPAR) signaling pathway. Moreover, Group B patients presented a deregulation of some proteins involved in apoptosis/survival pathways: phosphatidylethanolamine-binding protein 1 (PEBP1), Ras-related nuclear protein (Ran) and peroxiredoxin 4 (PRDX4). PEBP1 downregulation and RAN and PRDX4 upregulation were associated with more severe tissue damage. Likewise, IgMs were found strongly upregulated in Group B. In conclusion, our results indicate that a downregulation of proteins involved in PPAR signaling and the modulation of several cancer-related proteins are associated with the highest CD histological score according to Oberhuber-Marsh classification.