Multiple-Enzyme-Digestion Strategy Improves Accuracy and Sensitivity of Label- and Standard-Free Absolute Quantification to a Level That Is Achievable by Analysis with Stable Isotope-Labeled Standard Spiking

Accounting for Digestion Enzyme Bias in Casanovo

A key parameter of any bottom-up proteomics mass spectrometry experiment is the identity of the enzyme that is used to digest proteins in the sample into peptides. The Casanovo de novo sequencing model was trained using data that was generated with trypsin digestion; consequently, the model prefers to predict peptides that end with the amino acids "K" or "R". This bias is desirable when Casanovo is used to analyze data that was also generated using trypsin but can be problematic if the data was generated using some other digestion enzyme. In this work, we modify Casanovo to take as input the identity of the digestion enzyme alongside each observed spectrum. We then train Casanovo with data generated by using several different enzymes, and we demonstrate that the resulting model successfully learns to capture enzyme-specific behavior. However, we find, surprisingly, that this new model does not yield a significant improvement in sequencing accuracy relative to a model trained without enzyme information but using the same training set. This observation may have important implications for future attempts to make use of experimental metadata in de novo sequencing models.

Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.

Complete genome assembly and methylome dissection of Methanococcus aeolicus PL15/H p

Although restriction-modification systems are found in both Eubacterial and Archaeal kingdoms, comparatively less is known about patterns of DNA methylation and genome defense systems in archaea. Here we report the complete closed genome sequence and methylome analysis of Methanococcus aeolicus PL15/H p , a strain of the CO2-reducing methanogenic archaeon and a commercial source for MaeI, MaeII, and MaeIII restriction endonucleases. The M. aeolicus PL15/H p genome consists of a 1.68 megabase circular chromosome predicted to contain 1,615 protein coding genes and 38 tRNAs. A combination of methylome sequencing, homology-based genome annotation, and recombinant gene expression identified five restriction-modification systems encoded by this organism, including the methyltransferase and site-specific endonuclease of MaeIII. The MaeIII restriction endonuclease was recombinantly expressed, purified and shown to have site-specific DNA cleavage activity in vitro.

Quantitative Characterization of Clinically Relevant Drug-Metabolizing Enzymes and Transporters in Rat Liver and Intestinal Segments for Applications in PBPK Modeling

Rats are extensively used as a preclinical model for assessing drug pharmacokinetics (PK) and tissue distribution; however, successful translation of the rat data requires information on the differences in drug metabolism and transport mechanisms between rats and humans. To partly fill this knowledge gap, we quantified clinically relevant drug-metabolizing enzymes and transporters (DMETs) in the liver and different intestinal segments of Sprague-Dawley rats. The levels of DMET proteins in rats were quantified using the global proteomics-based total protein approach (TPA) and targeted proteomics. The abundance of the major DMET proteins was largely comparable using quantitative global and targeted proteomics. However, global proteomics-based TPA was able to detect and quantify a comprehensive list of 66 DMET proteins in the liver and 37 DMET proteins in the intestinal segments of SD rats without the need for peptide standards. Cytochrome P450 (Cyp) and UDP-glycosyltransferase (Ugt) enzymes were mainly detected in the liver with the abundance ranging from 8 to 6502 and 74 to 2558 pmol/g tissue. P-gp abundance was higher in the intestine (124.1 pmol/g) as compared to that in the liver (26.6 pmol/g) using the targeted analysis. Breast cancer resistance protein (Bcrp) was most abundant in the intestinal segments, whereas organic anion transporting polypeptides (Oatp) 1a1, 1a4, 1b2, and 2a1 and multidrug resistance proteins (Mrp) 2 and 6 were predominantly detected in the liver. To demonstrate the utility of these data, we modeled digoxin PK by integrating protein abundance of P-gp and Cyp3a2 into a physiologically based PK (PBPK) model constructed using PK-Sim software. The model was able to reliably predict the systemic as well as tissue concentrations of digoxin in rats. These findings suggest that proteomics-informed PBPK models in preclinical species can allow mechanistic PK predictions in animal models including tissue drug concentrations.

Comprehensive proteomic analysis of murine terminal erythroid differentiation

Murine-based cellular models have provided and continue to provide many useful insights into the fundamental mechanisms of erythropoiesis, as well as insights into the pathophysiology of inherited and acquired red cell disorders. Although detailed information on many aspects of these cell models is available, comprehensive proteomic data are lacking. This is a critical knowledge gap, as proteins are effectors of most biologic processes. To address this critical unmet need, proteomes of the murine cell lines Friend erythroleukemia (MEL), GATA1 erythroid (G1ER), and embryonic stem cell-derived erythroid progenitor (MEDEP) and proteomes of cultured murine marrow-derived erythroblasts at different stages of terminal erythroid differentiation were analyzed. The proteomes of MEDEP cells and primary murine erythroid cells were most similar, whereas those of MEL and G1ER cells were more distantly related. We demonstrated that the overall cellular content of histones does not decrease during terminal differentiation, despite strong chromatin condensation. Comparison of murine and human proteomes throughout terminal erythroid differentiation revealed that many noted transcriptomic changes were significantly dampened at the proteome level, especially at the end of the terminal differentiation process. Analysis of the early events associated with induction of terminal differentiation in MEDEP cells revealed divergent alterations in associated transcriptomes and proteomes. These proteomic data are powerful and valuable tools for the study of fundamental mechanisms of normal and disordered erythropoiesis and will be of broad interest to a wide range of investigators for making the appropriate choice of various cell lines to study inherited and acquired diseases of the erythrocyte.

Formaldehyde Derivatization: An Unexpected Side Reaction During Filter-Aided Sample Preparation

The filter-aided sample preparation (FASP) method has been commonly used for proteomic sample preparation due to its high efficiency in removing impurities. Herein, we report an overlooked +12 Da side modification during FASP method using Microcon spin filters. We confirmed that the side modification is caused by formaldehyde released from the spin filter and found that the side modification leads to 10.5% and 9.5% loss in proteome-level peptide and protein identification, respectively. We evaluated different pretreatment procedures to reduce the side reaction. Furthermore, on the basis of the evaluation results of different brands of spin filters, we recommend Nanosep spin filters for different proteomic studies, especially for amine-labeling proteomic studies. Our results would benefit researchers employing the spin filters to improve their results and also help spin filter manufacturers to improve the product quality. Data are available via ProteomeXchange with identifier PXD018737.

The proteome landscape of the kingdoms of life

Proteins carry out the vast majority of functions in all biological domains, but for technological reasons their large-scale investigation has lagged behind the study of genomes. Since the first essentially complete eukaryotic proteome was reported¹, advances in mass-spectrometry-based proteomics² have enabled increasingly comprehensive identification and quantification of the human proteome^3-6. However, there have been few comparisons across species^7,8, in stark contrast with genomics initiatives⁹. Here we use an advanced proteomics workflow-in which the peptide separation step is performed by a microstructured and extremely reproducible chromatographic system-for the in-depth study of 100 taxonomically diverse organisms. With two million peptide and 340,000 stringent protein identifications obtained in a standardized manner, we double the number of proteins with solid experimental evidence known to the scientific community. The data also provide a large-scale case study for sequence-based machine learning, as we demonstrate by experimentally confirming the predicted properties of peptides from Bacteroides uniformis. Our results offer a comparative view of the functional organization of organisms across the entire evolutionary range. A remarkably high fraction of the total proteome mass in all kingdoms is dedicated to protein homeostasis and folding, highlighting the biological challenge of maintaining protein structure in all branches of life. Likewise, a universally high fraction is involved in supplying energy resources, although these pathways range from photosynthesis through iron sulfur metabolism to carbohydrate metabolism. Generally, however, proteins and proteomes are remarkably diverse between organisms, and they can readily be explored and functionally compared at www.proteomesoflife.org.

Global variability analysis of mRNA and protein concentrations across and within human tissues

Genes and proteins show variable expression patterns throughout the human body. However, it is not clear whether relative differences in mRNA concentrations are retained on the protein level. Furthermore, inter-individual protein concentration variability within single tissue types has not been comprehensively explored. Here, we used the Gini index for in-depth concentration variability analysis of publicly available transcriptomics and proteomics data, and of an in-house proteomics dataset of human liver and jejunum from 38 donors. We found that the transfer of concentration variability from mRNA to protein is limited, that established 'reference genes' for data normalization vary markedly at the protein level, that protein concentrations cover a wide variability spectrum within single tissue types, and that concentration variability analysis can be a convenient starting point for identifying disease-associated proteins and novel biomarkers. Our results emphasize the importance of considering individual concentration levels, as opposed to population averages, for personalized systems biology analysis.

Datasets: Sensitivity and protein digestion course of proteomic Filter Aided Sample Preparation

Sensitivity of FASP was tested using SDS lysates from HeLa cells and mouse brain. Peptides were analyzed using a QExactive HF-X instrument. Whole cell lysates of Hela cells were processed with FASP using single or double, consecutive or successive, digestion with LysC or trypsin. The generated peptides were analyzed using a LTQ-Orbitrap mass spectrometer. These datasets accompany “Filter Aided Sample Preparation – A Tutorial” (Wiśniewski, 2019).

Toward a Consensus on Applying Quantitative Liquid Chromatography-Tandem Mass Spectrometry Proteomics in Translational Pharmacology Research: A White Paper

Quantitative translation of information on drug absorption, disposition, receptor engagement, and drug-drug interactions from bench to bedside requires models informed by physiological parameters that link in vitro studies to in vivo outcomes. To predict in vivo outcomes, biochemical data from experimental systems are routinely scaled using protein quantity in these systems and relevant tissues. Although several laboratories have generated useful quantitative proteomic data using state-of-the-art mass spectrometry, no harmonized guidelines exit for sample analysis and data integration to in vivo translation practices. To address this gap, a workshop was held on September 27 and 28, 2018, in Cambridge, MA, with 100 experts attending from academia, the pharmaceutical industry, and regulators. Various aspects of quantitative proteomics and its applications in translational pharmacology were debated. A summary of discussions and best practices identified by this expert panel are presented in this "White Paper" alongside unresolved issues that were outlined for future debates.