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Maintenance of cellular homeostasis through regulated degradation of proteins and organelles is a defining feature of autophagy. This process itself is tightly regulated in a series of well-defined biochemical reactions governed largely by the highly conserved ATG protein family. Given its crucial role in regulating protein levels under both basal and stress conditions such as starvation and infection, genetic or pharmacological perturbation of autophagy results in massive changes in the cellular proteome and impacts nearly every biological process. Therefore, studying autophagy perturbations at a global scale assumes prime importance. In recent years, quantitative mass spectrometry (MS)-based proteomics has emerged as a powerful approach to explore biological processes through global proteome quantification analysis. Tandem mass tag (TMT)-based MS proteomics is one such robust quantitative technique that can examine relative protein abundances in multiple samples (parallel multiplexing). Investigating autophagy through TMT-based MS approach can give great insights into autophagy-regulated biological processes, protein-protein interaction networks, spatiotemporal protein dynamics, and identification of new autophagy substrates. This chapter provides a detailed protocol for studying the impact of a dysfunctional autophagy pathway on the cellular proteome and pathways in a healthy vs. disease (virus infection) condition using a 16-plex TMT-based quantitative proteomics approach. We also provide a pipeline on data processing and analysis using available web-based tools., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

The biological complexity cannot be captured by genes or proteins alone. The protein posttranslational modifications (PTMs) impart functional diversity to the proteome and regulate protein structure, activity, localization and interactions. Their dynamics drive cellular signaling, growth and development while their dysregulation causes many diseases. Mass spectrometry based quantitative profiling of PTMs and bioinformatics analysis tools allow systems level insights into their network architecture. High-resolution profiling of PTM networks will advance disease understanding and precision medicine. It can accelerate the discovery of biomarkers and drug targets. This requires better tools for unbiased, high-throughput and accurate PTM identification, site localization and automated annotation on a systems level., Competing Interests: Conflict of interest The authors declare no competing interests., (Copyright © 2021 Elsevier Inc. All rights reserved.)

A cell integrates various signals through a network of biomolecules that crosstalk to synergistically regulate the replication, transcription, translation and other metabolic activities of a cell. These networks regulate signal perception and processing that drives biological functions. The biological complexity cannot be fully captured by a single -omics discipline. The holistic study of an organism-in health, perturbation, exposure to environment and disease, is studied under systems biology. The bottom-up molecular approaches (genes, mRNA, protein, metabolite, etc.) have laid the foundation of current biological knowledge covering the horizon from viruses, bacteria, fungi, plants and animals. Yet, these techniques provide a rather myopic view of biology at the molecular level. To understand how the interconnected molecular components are formed and rewired in disease or exposure to environmental stimuli is the holy grail of modern biology. The omics era was heralded by the genomics revolution but advanced sequencing techniques are now also ubiquitous in transcriptomics, proteomics, metabolomics and lipidomics. Multi-omics data analysis and integration techniques are driving the quest for deeper insights into how the different layers of biomolecules talk to each other in diverse contexts., Competing Interests: Conflict of interest The authors declare no competing interests., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Proteomic analysis identifies post-translational functions of proteins, which remains obscure in transcriptomics. Given the important functions of Th9 cells in anti-tumor immunity, we performed proteome analysis of Th9 cells to understand the involvement of proteins that might be crucial for the anti-tumor functions of Th9 cells. Here we performed a comprehensive proteomic analysis of murine Th0 and Th9 cells, and identified proteins that are enriched in Th9 cells. Pathway analysis identified an abundance of phosphoproteins in the proteome of Th9 cells as compared to Th0 cells. Among upregulated phosphoproteins, Ppp2ca (catalytic subunit of protein phosphatase, PP2A) was found to be highly enriched in Th9 cells. Although the role of PP2A has been shown to regulate the differentiation and functions of Th1, Th2, Th17 and Tregs, its role in the differentiation and functions of Th9 cells is not identified yet. Here we found that PP2A is required for the induction of Th9 cells, as PP2A inhibition leads to the suppression of IL-9 and expression of key transcription factors of Th9 cells. PP2A inhibition abrogates Th9 cell-mediated anti-tumor immune response in B16-OVA melanoma tumor model. Thus, we report that PP2A is essential for the differentiation and anti-tumor functions of Th9 cells.

Proteomics by mass spectrometry (MS) allows the large-scale identification and quantitation of the cellular proteins in a given biological context. Systems biology studies from proteomics data are largely limited by the accuracy and coverage of quantitative proteomics along with missing values. Toward this end, statistically robust biological observations are required, comprising multiple replicates, preferably with little technical variations. Multiplexed labeling techniques in proteomics allow quantitative comparisons of several biological samples or conditions. In this focused Review, we discuss an emerging technique called higher order multiplexing or enhanced multiplexing, a unique combination of traditional MS 1 - and MS 2 -based quantitative proteomics methods that allows for expanding the multiplexing capability of MS methods to save valuable instrument time, achieve statistical robustness, enhance coverage and quantitation accuracy, and reduce the run-to-run variability. We discuss the various innovative studies and experimental designs that exploit the power of this technique and its variants to provide an overview of a rapidly growing area and also to highlight the advantages and challenges that lie ahead in the widespread adoption of this technique.

Database searching is the preferred method for protein identification from digital spectra of mass to charge ratios (m/z) detected for protein samples through mass spectrometers. The search database is one of the major influencing factors in discovering proteins present in the sample and thus in deriving biological conclusions. In most cases the choice of search database is arbitrary. Here we describe common search databases used in proteomic studies and their impact on final list of identified proteins. We also elaborate upon factors like composition and size of the search database that can influence the protein identification process. In conclusion, we suggest that choice of the database depends on the type of inferences to be derived from proteomics data. However, making additional efforts to build a compact and concise database for a targeted question should generally be rewarding in achieving confident protein identifications.

In the era of large-scale quantitative biology, mass spectrometry-based quantitative proteomics is progressively becoming indispensable for gaining insights into the biological systems at molecular level. Various quantitative study designs rely on chemical tagging approaches to study disease, stress, or drug response and temporal studies aiming at disease/developmental progression in a biological system. Isobaric tags for relative and absolute quantitation (iTRAQ) is one of the most popular chemical labeling techniques which allows four, six, or eight samples to be multiplexed in a single run. As the iTRAQ tag has a balancer group to equalize all states of a labeled peptide to same mass, the differentially labeled iTRAQ peptides are mixed before chromatography and elute as a single combined peak in MS. This enhances the peptide signal and quantitation is performed during MS/MS along with sequencing, where reporter ions of different masses are released to give relative quantitation. Known amount of a spiked-in protein can also help in absolute quantitation of the proteins in a sample.

With the advancement in proteomics separation techniques and improvements in mass analyzers, the data generated in a mass-spectrometry based proteomics experiment is rising exponentially. Such voluminous datasets necessitate automated computational tools for high-throughput data analysis and appropriate statistical control. The data is searched using one or more of the several popular database search algorithms. The matches assigned by these tools can have false positives and statistical validation of these false matches is necessary before making any biological interpretations. Without such procedures, the biological inferences do not hold true and may be outright misleading. There is a considerable overlap between true and false positives. To control the false positives amongst a set of accepted matches, there is a need for some statistical estimate that can reflect the amount of false positives present in the data processed. False discovery rate (FDR) is the metric for global confidence assessment of a large-scale proteomics dataset. This chapter covers the basics of FDR, its application in proteomics, and methods to estimate FDR.

Proteogenomic re-annotation and mRNA splicing information can lead to the discovery of various protein forms for eukaryotic model organisms like rat. However, detection of novel proteoforms using mass spectrometry proteomics data remains a formidable challenge. We developed EuGenoSuite, an open source multiple algorithmic proteomic search tool and utilized it in our in-house integrated transcriptomic-proteomic pipeline to facilitate automated proteogenomic analysis. Using four proteogenomic pipelines (integrated transcriptomic-proteomic, Peppy, Enosi, and ProteoAnnotator) on publicly available RNA-sequence and MS proteomics data, we discovered 363 novel peptides in rat brain microglia representing novel proteoforms for 249 gene loci in the rat genome. These novel peptides aided in the discovery of novel exons, translation of annotated untranslated regions, pseudogenes, and splice variants for various loci; many of which have known disease associations, including neurological disorders like schizophrenia, amyotrophic lateral sclerosis, etc. Novel isoforms were also discovered for genes implicated in cardiovascular diseases and breast cancer for which rats are considered model organisms. Our integrative multi-omics data analysis not only enables the discovery of new proteoforms but also generates an improved reference for human disease studies in the rat model., (© 2016 by The American Society for Biochemistry and Molecular Biology, Inc.)

We present GenoSuite, an integrated proteogenomic pipeline to validate, refine and discover protein coding genes using high-throughput mass spectrometry (MS) data from prokaryotes. To demonstrate the effectiveness of GenoSuite, we analyzed proteomics data of Bradyrhizobium japonicum (USDA110), a model organism to study agriculturally important rhizobium-legume symbiosis. Our analysis confirmed 31% of known genes, refined 49 gene models for their translation initiation site (TIS) and discovered 59 novel protein coding genes. Notably, a novel protein which redefined the boundary of a crucial cytochrome P450 system related operon was discovered, known to be highly expressed in the anaerobic symbiotic bacteroids. A focused analysis on N-terminally acetylated peptides indicated downstream TIS for gene blr0594. Finally, ortho-proteogenomic analysis revealed three novel genes in recently sequenced B. japonicum USDA6(T) genome. The discovery of large number of missing genes and correction of gene models have expanded the proteomic landscape of B. japonicum and presents an unparalleled utility of proteogenomic analyses and versatility of GenoSuite for annotating prokaryotic genomes including pathogens.

The statistical validation of database search results is a complex issue in bottom-up proteomics. The correct and incorrect peptide spectrum match (PSM) scores overlap significantly, making an accurate assessment of true peptide matches challenging. Since the complete separation between the true and false hits is practically never achieved, there is need for better methods and rescoring algorithms to improve upon the primary database search results. Here we describe the calibration and False Discovery Rate (FDR) estimation of database search scores through a dynamic FDR calculation method, FlexiFDR, which increases both the sensitivity and specificity of search results. Modelling a simple linear regression on the decoy hits for different charge states, the method maximized the number of true positives and reduced the number of false negatives in several standard datasets of varying complexity (18-mix, 49-mix, 200-mix) and few complex datasets (E. coli and Yeast) obtained from a wide variety of MS platforms. The net positive gain for correct spectral and peptide identifications was up to 14.81% and 6.2% respectively. The approach is applicable to different search methodologies--separate as well as concatenated database search, high mass accuracy, and semi-tryptic and modification searches. FlexiFDR was also applied to Mascot results and showed better performance than before. We have shown that appropriate threshold learnt from decoys, can be very effective in improving the database search results. FlexiFDR adapts itself to different instruments, data types and MS platforms. It learns from the decoy hits and sets a flexible threshold that automatically aligns itself to the underlying variables of data quality and size.

Mass spectrometry has made rapid advances in the recent past and has become the preferred method for proteomics. Although many open source algorithms for peptide identification exist, such as X!Tandem and OMSSA, it has majorly been a domain of proprietary software. There is a need for better, freely available, and configurable algorithms that can help in identifying the correct peptides while keeping the false positives to a minimum. We have developed MassWiz, a novel empirical scoring function that gives appropriate weights to major ions, continuity of b-y ions, intensities, and the supporting neutral losses based on the instrument type. We tested MassWiz accuracy on 486,882 spectra from a standard mixture of 18 proteins generated on 6 different instruments downloaded from the Seattle Proteome Center public repository. We compared the MassWiz algorithm with Mascot, Sequest, OMSSA, and X!Tandem at 1% FDR. MassWiz outperformed all in the largest data set (AGILENT XCT) and was second only to Mascot in the other data sets. MassWiz showed good performance in the analysis of high confidence peptides, i.e., those identified by at least three algorithms. We also analyzed a yeast data set containing 106,133 spectra downloaded from the NCBI Peptidome repository and got similar results. The results demonstrate that MassWiz is an effective algorithm for high-confidence peptide identification without compromising on the number of assignments. MassWiz is open-source, versatile, and easily configurable.