Quantitative and qualitative proteome characteristics extracted from in-depth integrated genomics and proteomics analysis

Proteogenomics offers a novel avenue in neoantigen identification for cancer immunotherapy

Cancer neoantigens are tumor-specific non-synonymous mutant peptides that activate the immune system to produce an anti-tumor response. Personalized cancer vaccines based on neoantigens are currently one of the most promising therapeutic approaches for cancer treatment. By utilizing the unique mutations within each patient's tumor, these vaccines aim to elicit a strong and specific immune response against cancer cells. However, the identification of neoantigens remains challenging due to the low accuracy of current prediction tools and the high false-positive rate of candidate neoantigens. Since the concept of "proteogenomics" emerged in 2004, it has evolved rapidly with the increased sequencing depth of next-generation sequencing technologies and the maturation of mass spectrometry-based proteomics technologies to become a more comprehensive approach to neoantigen identification, allowing the discovery of high-confidence candidate neoantigens. In this review, we summarize the reason why cancer neoantigens have become attractive targets for immunotherapy, the mechanism of cancer vaccines and the advances in cancer immunotherapy. Considerations relevant to the application emerging of proteogenomics technologies for neoantigen identification and challenges in this field are described.

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.

Overview and considerations in bottom-up proteomics

Proteins are the key biological actors within cells, driving many biological processes integral to both healthy and diseased states. Understanding the depth of complexity represented within the proteome is crucial to our scientific understanding of cellular biology and to provide disease specific insights for clinical applications. Mass spectrometry-based proteomics is the premier method for proteome analysis, with the ability to both identify and quantify proteins. Although proteomics continues to grow as a robust field of bioanalytical chemistry, advances are still necessary to enable a more comprehensive view of the proteome. In this review, we provide a broad overview of mass spectrometry-based proteomics in general, and highlight four developing areas of bottom-up proteomics: (1) protein inference, (2) alternative proteases, (3) sample-specific databases and (4) post-translational modification discovery.

Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly

Monoclonal antibodies are biotechnologically produced proteins with various applications in research, therapeutics and diagnostics. Their ability to recognize and bind to specific molecule structures makes them essential research tools and therapeutic agents. Sequence information of antibodies is helpful for understanding antibody-antigen interactions and ensuring their affinity and specificity. De novo protein sequencing based on mass spectrometry is a valuable method to obtain the amino acid sequence of peptides and proteins without a priori knowledge. In this study, we evaluated six recently developed de novo peptide sequencing algorithms (Novor, pNovo 3, DeepNovo, SMSNet, PointNovo and Casanovo), which were not specifically designed for antibody data. We validated their ability to identify and assemble antibody sequences on three multi-enzymatic data sets. The deep learning-based tools Casanovo and PointNovo showed an increased peptide recall across different enzymes and data sets compared with spectrum-graph-based approaches. We evaluated different error types of de novo peptide sequencing tools and their performance for different numbers of missing cleavage sites, noisy spectra and peptides of various lengths. We achieved a sequence coverage of 97.69-99.53% on the light chains of three different antibody data sets using the de Bruijn assembler ALPS and the predictions from Casanovo. However, low sequence coverage and accuracy on the heavy chains demonstrate that complete de novo protein sequencing remains a challenging issue in proteomics that requires improved de novo error correction, alternative digestion strategies and hybrid approaches such as homology search to achieve high accuracy on long protein sequences.

Short open reading frames (sORFs) and microproteins: an update on their identification and validation measures

A short open reading frame (sORFs) constitutes ≤ 300 bases, encoding a microprotein or sORF-encoded protein (SEP) which comprises ≤ 100 amino acids. Traditionally dismissed by genome annotation pipelines as meaningless noise, sORFs were found to possess coding potential with ribosome profiling (RIBO-Seq), which unveiled sORF-based transcripts at various genome locations. Nonetheless, the existence of corresponding microproteins that are stable and functional was little substantiated by experimental evidence initially. With recent advancements in multi-omics, the identification, validation, and functional characterisation of sORFs and microproteins have become feasible. In this review, we discuss the history and development of an emerging research field of sORFs and microproteins. In particular, we focus on an array of bioinformatics and OMICS approaches used for predicting, sequencing, validating, and characterizing these recently discovered entities. These strategies include RIBO-Seq which detects sORF transcripts via ribosome footprints, and mass spectrometry (MS)-based proteomics for sequencing the resultant microproteins. Subsequently, our discussion extends to the functional characterisation of microproteins by incorporating CRISPR/Cas9 screen and protein–protein interaction (PPI) studies. Our review discusses not only detection methodologies, but we also highlight on the challenges and potential solutions in identifying and validating sORFs and their microproteins. The novelty of this review lies within its validation for the functional role of microproteins, which could contribute towards the future landscape of microproteomics.

Data-Independent Acquisition Protease-Multiplexing Enables Increased Proteome Sequence Coverage Across Multiple Fragmentation Modes

The use of multiple proteases has been shown to increase protein sequence coverage in proteomics experiments; however, due to the additional analysis time required, it has not been widely adopted in routine data-dependent acquisition (DDA) proteomic workflows. Alternatively, data-independent acquisition (DIA) has the potential to analyze multiplexed samples from different protease digests, but has been primarily optimized for fragmenting tryptic peptides. Here we evaluate a DIA multiplexing approach that combines three proteolytic digests (Trypsin, AspN, and GluC) into a single sample. We first optimize data acquisition conditions for each protease individually with both the canonical DIA fragmentation mode (beam type CID), as well as resonance excitation CID, to determine optimal consensus conditions across proteases. Next, we demonstrate that application of these conditions to a protease-multiplexed sample of human peptides results in similar protein identifications and quantitative performance as compared to trypsin alone, but enables up to a 63% increase in peptide detections, and a 45% increase in nonredundant amino acid detections. Nontryptic peptides enabled noncanonical protein isoform determination and resulted in 100% sequence coverage for numerous proteins, suggesting the utility of this approach in applications where sequence coverage is critical, such as protein isoform analysis.

Renal tubular peroxisomes are dispensable for normal kidney function

Peroxisomes are specialized cellular organelles involved in a variety of metabolic processes. In humans, mutations leading to complete loss of peroxisomes cause multiorgan failure (Zellweger’s spectrum disorders, ZSD), including renal impairment. However, the (patho)physiological role of peroxisomes in the kidney remains unknown. We addressed the role of peroxisomes in renal function in mice with conditional ablation of peroxisomal biogenesis in the renal tubule (cKO mice). Functional analyses did not reveal any overt kidney phenotype in cKO mice. However, infant male cKO mice had lower body and kidney weights, and adult male cKO mice exhibited substantial reductions in kidney weight and kidney weight/body weight ratio. Stereological analysis showed an increase in mitochondria density in proximal tubule cells of cKO mice. Integrated transcriptome and metabolome analyses revealed profound reprogramming of a number of metabolic pathways, including metabolism of glutathione and biosynthesis/biotransformation of several major classes of lipids. Although this analysis suggested compensated oxidative stress, challenge with high-fat feeding did not induce significant renal impairments in cKO mice. We demonstrate that renal tubular peroxisomes are dispensable for normal renal function. Our data also suggest that renal impairments in patients with ZSD are of extrarenal origin.

Capturing the third dimension in drug discovery: Spatially-resolved tools for interrogation of complex 3D cell models

Advanced three-dimensional (3D) cell models have proven to be capable of depicting architectural and microenvironmental features of several tissues. By providing data of higher physiological and pathophysiological relevance, 3D cell models have been contributing to a better understanding of human development, pathology onset and progression mechanisms, as well as for 3D cell-based assays for drug discovery. Nonetheless, the characterization and interrogation of these tissue-like structures pose major challenges on the conventional analytical methods, pushing the development of spatially-resolved technologies. Herein, we review recent advances and pioneering technologies suitable for the interrogation of multicellular 3D models, while capable of retaining biological spatial information. We focused on imaging technologies and omics tools, namely transcriptomics, proteomics and metabolomics. The advantages and shortcomings of these novel methodologies are discussed, alongside the opportunities to intertwine data from the different tools.

Proteomics: Concepts and applications in human medicine

Proteomics is the complete evaluation of the function and structure of proteins to understand an organism’s nature. Mass spectrometry is an essential tool that is used for profiling proteins in the cell. However, biomarker discovery remains the major challenge of proteomics because of their complexity and dynamicity. Therefore, combining the proteomics approach with genomics and bioinformatics will provide an understanding of the information of biological systems and their disease alteration. However, most studies have investigated a small part of the proteins in the blood. This review highlights the types of proteomics, the available proteomic techniques, and their applications in different research fields.

Transcriptome and proteome profiling reveal complementary scavenger and immune features of rat liver sinusoidal endothelial cells and liver macrophages

BACKGROUND

Liver sinusoidal endothelial cells (LSECs) and Kupffer cells (KCs; liver resident macrophages) form the body's most effective scavenger cell system for the removal of harmful blood-borne substances, ranging from modified self-proteins to pathogens and xenobiotics. Controversies in the literature regarding the LSEC phenotype pose a challenge when determining distinct functionalities of KCs and LSECs. This may be due to overlapping functions of the two cells, insufficient purification and/or identification of the cells, rapid dedifferentiation of LSECs in vitro, or species differences. We therefore characterized and quantitatively compared expressed gene products of freshly isolated, highly pure LSECs (fenestrated SE-1/FcγRIIb2⁺) and KCs (CD11b/c⁺) from Sprague Dawley, Crl:CD (SD), male rats using high throughput mRNA-sequencing and label-free proteomics.

RESULTS

We observed a robust correlation between the proteomes and transcriptomes of the two cell types. Integrative analysis of the global molecular profile demonstrated the immunological aspects of LSECs. The constitutive expression of several immune genes and corresponding proteins of LSECs bore some resemblance with the expression in macrophages. LSECs and KCs both expressed high levels of scavenger receptors (SR) and C-type lectins. Equivalent expression of SR-A1 (Msr1), mannose receptor (Mrc1), SR-B1 (Scarb1), and SR-B3 (Scarb2) suggested functional similarity between the two cell types, while functional distinction between the cells was evidenced by LSEC-specific expression of the SRs stabilin-1 (Stab1) and stabilin-2 (Stab2), and the C-type lectins LSECtin (Clec4g) and DC-SIGNR (Clec4m). Many immune regulatory factors were differentially expressed in LSECs and KCs, with one cell predominantly expressing a specific cytokine/chemokine and the other cell the cognate receptor, illustrating the complex cytokine milieu of the sinusoids. Both cells expressed genes and proteins involved in antigen processing and presentation, and lymphocyte co-stimulation.

CONCLUSIONS

Our findings support complementary and partly overlapping scavenging and immune functions of LSECs and KCs. This highlights the importance of including LSECs in studies of liver immunity, and liver clearance and toxicity of large molecule drugs and nano-formulations.

Rat models of human diseases and related phenotypes: a systematic inventory of the causative genes

The laboratory rat has been used for a long time as the model of choice in several biomedical disciplines. Numerous inbred strains have been isolated, displaying a wide range of phenotypes and providing many models of human traits and diseases. Rat genome mapping and genomics was considerably developed in the last decades. The availability of these resources has stimulated numerous studies aimed at discovering causal disease genes by positional identification. Numerous rat genes have now been identified that underlie monogenic or complex diseases and remarkably, these results have been translated to the human in a significant proportion of cases, leading to the identification of novel human disease susceptibility genes, helping in studying the mechanisms underlying the pathological abnormalities and also suggesting new therapeutic approaches. In addition, reverse genetic tools have been developed. Several genome-editing methods were introduced to generate targeted mutations in genes the function of which could be clarified in this manner [generally these are knockout mutations]. Furthermore, even when the human gene causing a disease had been identified without resorting to a rat model, mutated rat strains (in particular KO strains) were created to analyze the gene function and the disease pathogenesis. Today, over 350 rat genes have been identified as underlying diseases or playing a key role in critical biological processes that are altered in diseases, thereby providing a rich resource of disease models. This article is an update of the progress made in this research and provides the reader with an inventory of these disease genes, a significant number of which have similar effects in rat and humans.

Changes in the Oligodendrocyte Progenitor Cell Proteome with Ageing

Following central nervous system (CNS) demyelination, adult oligodendrocyte progenitor cells (OPCs) can differentiate into new myelin-forming oligodendrocytes in a regenerative process called remyelination. Although remyelination is very efficient in young adults, its efficiency declines progressively with ageing. Here we performed proteomic analysis of OPCs freshly isolated from the brains of neonate, young and aged female rats. Approximately 50% of the proteins are expressed at different levels in OPCs from neonates compared with their adult counterparts. The amount of myelin-associated proteins, and proteins associated with oxidative phosphorylation, inflammatory responses and actin cytoskeletal organization increased with age, whereas cholesterol-biosynthesis, transcription factors and cell cycle proteins decreased. Our experiments provide the first ageing OPC proteome, revealing the distinct features of OPCs at different ages. These studies provide new insights into why remyelination efficiency declines with ageing and potential roles for aged OPCs in other neurodegenerative diseases.

A new approach to identifying hypertension-associated genes in the mesenteric artery of spontaneously hypertensive rats and stroke-prone spontaneously hypertensive rats

Supplemental Digital Content is available in the text

Objective:

Hypertension is one of the most prevalent diseases in humans who live a modern lifestyle. Alongside more effective care, clarification of the genetic background of hypertension is urgently required. Gene expression in mesenteric resistance arteries of spontaneously hypertensive rats (SHR), stroke-prone SHR (SHRSP) and two types of renal hypertensive Wistar Kyoto rats (WKY), two kidneys and one clip renal hypertensive rat (2K1C) and one kidney and one clip renal hypertensive rat (1K1C), was compared using DNA microarrays.

Methods:

We used a simultaneous equation and comparative selection method to identify genes associated with hypertension using the Reactome analysis tool and GenBank database.

Results:

The expression of 298 genes was altered between SHR and WKY (44 upregulated and 254 downregulated), while the expression of 290 genes was altered between SHRSP and WKY (83 upregulated and 207 downregulated). For SHRSP versus SHR, the expression of 60 genes was altered (36 upregulated and 24 downregulated). Several genes expressed in SHR and SHRSP were also expressed in the renovascular hypertensive 2K1C and 1K1C rats, indicative of the existence of hyper-renin and/or hypervolemic pathophysiological changes in SHR and SHRSP.

Conclusion:

The overexpression of Kcnq1, Crlf1, Alb and Xirp1 and the inhibition of Galr2, Kcnh1, Ache, Chrm2 and Slc5a7 expression may indicate that a relationship exists between these genes and the cause and/or worsening of hypertension in SHR and SHRSP.

20(S)-Protopanaxatriol promotes the binding of P53 and DNA to regulate the antitumor network via multiomic analysis

Although the tumor suppressor P53 is known to regulate a broad network of signaling pathways, it is still unclear how certain drugs influence these P53 signaling networks. Here, we used a comprehensive single-cell multiomics view of the effects of ginsenosides on cancer cells. Transcriptome and proteome profiling revealed that the antitumor activity of ginsenosides is closely associated with P53 protein. A miRNA–proteome interaction network revealed that P53 controlled the transcription of at least 38 proteins, and proteome-metabolome profiling analysis revealed that P53 regulated proteins involved in nucleotide metabolism, amino acid metabolism and “Warburg effect”. The results of integrative multiomics analysis revealed P53 protein as a potential key target that influences the anti-tumor activity of ginsenosides. Furthermore, by applying affinity mass spectrometry (MS) screening and surface plasmon resonance fragment library screening, we confirmed that 20(S)-protopanaxatriol directly targeted adjacent regions of the P53 DNA-binding pocket and promoted the stability of P53–DNA interactions, which further induced a series of omics changes.

Clinical protein science in translational medicine targeting malignant melanoma

Melanoma of the skin is the sixth most common type of cancer in Europe and accounts for 3.4% of all diagnosed cancers. More alarming is the degree of recurrence that occurs with approximately 20% of patients lethally relapsing following treatment. Malignant melanoma is a highly aggressive skin cancer and metastases rapidly extend to the regional lymph nodes (stage 3) and to distal organs (stage 4). Targeted oncotherapy is one of the standard treatment for progressive stage 4 melanoma, and BRAF inhibitors (e.g. vemurafenib, dabrafenib) combined with MEK inhibitor (e.g. trametinib) can effectively counter BRAFV600E-mutated melanomas. Compared to conventional chemotherapy, targeted BRAFV600E inhibition achieves a significantly higher response rate. After a period of cancer control, however, most responsive patients develop resistance to the therapy and lethal progression. The many underlying factors potentially causing resistance to BRAF inhibitors have been extensively studied. Nevertheless, the remaining unsolved clinical questions necessitate alternative research approaches to address the molecular mechanisms underlying metastatic and treatment-resistant melanoma. In broader terms, proteomics can address clinical questions far beyond the reach of genomics, by measuring, i.e. the relative abundance of protein products, post-translational modifications (PTMs), protein localisation, turnover, protein interactions and protein function. More specifically, proteomic analysis of body fluids and tissues in a given medical and clinical setting can aid in the identification of cancer biomarkers and novel therapeutic targets. Achieving this goal requires the development of a robust and reproducible clinical proteomic platform that encompasses automated biobanking of patient samples, tissue sectioning and histological examination, efficient protein extraction, enzymatic digestion, mass spectrometry-based quantitative protein analysis by label-free or labelling technologies and/or enrichment of peptides with specific PTMs. By combining data from, e.g. phosphoproteomics and acetylomics, the protein expression profiles of different melanoma stages can provide a solid framework for understanding the biology and progression of the disease. When complemented by proteogenomics, customised protein sequence databases generated from patient-specific genomic and transcriptomic data aid in interpreting clinical proteomic biomarker data to provide a deeper and more comprehensive molecular characterisation of cellular functions underlying disease progression. In parallel to a streamlined, patient-centric, clinical proteomic pipeline, mass spectrometry-based imaging can aid in interrogating the spatial distribution of drugs and drug metabolites within tissues at single-cell resolution. These developments are an important advancement in studying drug action and efficacy in vivo and will aid in the development of more effective and safer strategies for the treatment of melanoma. A collaborative effort of gargantuan proportions between academia and healthcare professionals has led to the initiation, establishment and development of a cutting-edge cancer research centre with a specialisation in melanoma and lung cancer. The primary research focus of the European Cancer Moonshot Lund Center is to understand the impact that drugs have on cancer at an individualised and personalised level. Simultaneously, the centre increases awareness of the relentless battle against cancer and attracts global interest in the exceptional research performed at the centre.

Integrated Transcriptomic and Proteomic Analysis of Primary Human Umbilical Vein Endothelial Cells

Understanding the molecular profile of every human cell type is essential for understanding its role in normal physiology and disease. Technological advancements in DNA sequencing, mass spectrometry, and computational methods allow us to carry out multiomics analyses although such approaches are not routine yet. Human umbilical vein endothelial cells (HUVECs) are a widely used model system to study pathological and physiological processes associated with the cardiovascular system. In this study, next-generation sequencing and high-resolution mass spectrometry to profile the transcriptome and proteome of primary HUVECs is employed. Analysis of 145 million paired-end reads from next-generation sequencing confirmed expression of 12 186 protein-coding genes (FPKM ≥0.1), 439 novel long non-coding RNAs, and revealed 6089 novel isoforms that were not annotated in GENCODE. Proteomics analysis identifies 6477 proteins including confirmation of N-termini for 1091 proteins, isoforms for 149 proteins, and 1034 phosphosites. A database search to specifically identify other post-translational modifications provide evidence for a number of modification sites on 117 proteins which include ubiquitylation, lysine acetylation, and mono-, di- and tri-methylation events. Evidence for 11 "missing proteins," which are proteins for which there was insufficient or no protein level evidence, is provided. Peptides supporting missing protein and novel events are validated by comparison of MS/MS fragmentation patterns with synthetic peptides. Finally, 245 variant peptides derived from 207 expressed proteins in addition to alternate translational start sites for seven proteins and evidence for novel proteoforms for five proteins resulting from alternative splicing are identified. Overall, it is believed that the integrated approach employed in this study is widely applicable to study any primary cell type for deeper molecular characterization.

Impact of Lipotoxicity on Tissue "Cross Talk" and Metabolic Regulation

Obesity-associated comorbidities include non-alcoholic fatty liver disease, Type 2 diabetes, and cardiovascular disease. These diseases are associated with accumulation of lipids in non-adipose tissues, which can impact many intracellular cellular signaling pathways and functions that have been broadly defined as "lipotoxic." This review moves beyond understanding intracellular lipotoxic outcomes and outlines the consequences of lipotoxicity on protein secretion and inter-tissue "cross talk," and the impact this exerts on systemic metabolism.

Gene expression variability: the other dimension in transcriptome analysis

Transcriptome sequencing is a powerful technique to study molecular changes that underlie the differences in physiological conditions and disease progression. A typical question that is posed in such studies is finding genes with significant changes between sample groups. In this respect expression variability is regarded as a nuisance factor that is primarily of technical origin and complicates the data analysis. However, it is becoming apparent that the biological variation in gene expression might be an important molecular phenotype that can affect physiological parameters. In this review we explore the recent literature on technical and biological variability in gene expression, sources of expression variability, (epi-)genetic hallmarks, and evolutionary constraints in genes with robust and variable gene expression. We provide an overview of recent findings on effects of external cues, such as diet and aging, on expression variability and on other biological phenomena that can be linked to it. We discuss metrics and tools that were developed for quantification of expression variability and highlight the importance of future studies in this direction. To assist the adoption of expression variability analysis, we also provide a detailed description and computer code, which can easily be utilized by other researchers. We also provide a reanalysis of recently published data to highlight the value of the analysis method.

Probing the endogenous peptidomes of cancer for biomarkers: A new endeavor

The life span of cancer patients can be prolonged with appropriate therapies if detected early. Mass screening for early detection of cancer, however, requires sensitive and specific biomarkers obtainable from body fluids such as blood or urine. To date, most biomarker discovery programs focus on the proteome rather than the endogenous peptidome. It has been long-established that tumor cells and stromal cells produce tumor resident proteases (TRPs) to remodel the surrounding tumor microenvironment in support of tumor progression. In fact, proteolytic products of TRPs have been shown to correlate with malignant behavior. Being of low molecular weight, these unique peptides can pass through the endothelial barrier of the vasculature into the bloodstream. As such, the cancer peptidome has increasingly become a focus for biomarker discovery. In this review, we discuss on the various aspects of the peptidome in cancer biomarker research.

Chaperonin CCT checkpoint function in basal transcription factor TFIID assembly

TFIID is a cornerstone of eukaryotic gene regulation. Distinct TFIID complexes with unique subunit composition exist and several TFIID subunits are shared with other complexes, conveying intricate cellular decision making to control subunit allocation and functional assembly of this essential transcription factor. However, the underlying molecular mechanisms remain poorly understood. Here, we used quantitative proteomics to examine TFIID submodules and assembly mechanisms in human cells. Structural and mutational analysis of the cytoplasmic TAF5-TAF6-TAF9 submodule identified novel interactions crucial for TFIID integrity, and for allocating TAF9 to TFIID or the SAGA co-activator complex. We discover a key checkpoint function for the chaperonin CCT, which specifically associates with nascent TAF5 for subsequent handover to TAF6-TAF9 and ultimate holo-TFIID formation. Our findings illustrate at the molecular level how multisubunit complexes are crafted in the cell, involving checkpoint decisions facilitated by a chaperone machine.

A System-wide Approach to Monitor Responses to Synergistic BRAF and EGFR Inhibition in Colorectal Cancer Cells

Intrinsic and/or acquired resistance represents one of the great challenges in targeted cancer therapy. A deeper understanding of the molecular biology of cancer has resulted in more efficient strategies, where one or multiple drugs are adopted in novel therapies to tackle resistance. This beneficial effect of using combination treatments has also been observed in colorectal cancer patients harboring the BRAF(V600E) mutation, whereby dual inhibition of BRAF(V600E) and EGFR increases antitumor activity. Notwithstanding this success, it is not clear whether this combination treatment is the only or most effective treatment to block intrinsic resistance to BRAF inhibitors. Here, we investigate molecular responses upon single and multi-target treatments, over time, using BRAF(V600E) mutant colorectal cancer cells as a model system. Through integration of transcriptomic, proteomic and phosphoproteomics data we obtain a comprehensive overview, revealing both known and novel responses. We primarily observe widespread up-regulation of receptor tyrosine kinases and metabolic pathways upon BRAF inhibition. These findings point to mechanisms by which the drug-treated cells switch energy sources and enter a quiescent-like state as a defensive response, while additionally compensating for the MAPK pathway inhibition.

Recapitulation of Human Neural Microenvironment Signatures in iPSC-Derived NPC 3D Differentiation

Brain microenvironment plays an important role in neurodevelopment and pathology, where the extracellular matrix (ECM) and soluble factors modulate multiple cellular processes. Neural cell culture typically relies on heterologous matrices poorly resembling brain ECM. Here, we employed neurospheroids to address microenvironment remodeling during neural differentiation of human stem cells, without the confounding effects of exogenous matrices. Proteome and transcriptome dynamics revealed significant changes at cell membrane and ECM during 3D differentiation, diverging significantly from the 2D differentiation. Structural proteoglycans typical of brain ECM were enriched during 3D differentiation, in contrast to basement membrane constituents in 2D. Moreover, higher expression of synaptic and ion transport machinery was observed in 3D cultures, suggesting higher neuronal maturation in neurospheroids. This work demonstrates that 3D neural differentiation as neurospheroids promotes the expression of cellular and extracellular features found in neural tissue, highlighting its value to address molecular defects in cell-ECM interactions associated with neurological disorders.

Utilizing proteomics to understand and define hypertension: where are we and where do we go?

INTRODUCTION

Hypertension is a complex and multifactorial cardiovascular disorder. With different mechanisms contributing to a different extent to an individual's blood pressure, the discovery of novel pathogenetic principles of hypertension is challenging. However, there is an urgent and unmet clinical need to improve prevention, detection, and therapy of hypertension in order to reduce the global burden associated with hypertension-related cardiovascular diseases. Areas covered: Proteomic techniques have been applied in reductionist experimental models including angiotensin II infusion models in rodents and the spontaneously hypertensive rat in order to unravel mechanisms involved in blood pressure control and end organ damage. In humans proteomic studies mainly focus on prediction and detection of organ damage, particularly of heart failure and renal disease. While there are only few proteomic studies specifically addressing human primary hypertension, there are more data available in hypertensive disorders in pregnancy, such as preeclampsia. We will review these studies and discuss implications of proteomics on precision medicine approaches. Expert commentary: Despite the potential of proteomic studies in hypertension there has been moderate progress in this area of research. Standardized large-scale studies are required in order to make best use of the potential that proteomics offers in hypertension and other cardiovascular diseases.

MicroRNA-guided prioritization of genome-wide association signals reveals the importance of microRNA-target gene networks for complex traits in cattle

MicroRNAs (miRNA) are key modulators of gene expression and so act as putative fine-tuners of complex phenotypes. Here, we hypothesized that causal variants of complex traits are enriched in miRNAs and miRNA-target networks. First, we conducted a genome-wide association study (GWAS) for seven functional and milk production traits using imputed sequence variants (13~15 million) and >10,000 animals from three dairy cattle breeds, i.e., Holstein (HOL), Nordic red cattle (RDC) and Jersey (JER). Second, we analyzed for enrichments of association signals in miRNAs and their miRNA-target networks. Our results demonstrated that genomic regions harboring miRNA genes were significantly (P < 0.05) enriched with GWAS signals for milk production traits and mastitis, and that enrichments within miRNA-target gene networks were significantly higher than in random gene-sets for the majority of traits. Furthermore, most between-trait and across-breed correlations of enrichments with miRNA-target networks were significantly greater than with random gene-sets, suggesting pleiotropic effects of miRNAs. Intriguingly, genes that were differentially expressed in response to mammary gland infections were significantly enriched in the miRNA-target networks associated with mastitis. All these findings were consistent across three breeds. Collectively, our observations demonstrate the importance of miRNAs and their targets for the expression of complex traits.

Quantitative Map of Proteome Dynamics during Neuronal Differentiation

Neuronal differentiation is a multistep process that shapes and re-shapes neurons by progressing through several typical stages, including axon outgrowth, dendritogenesis, and synapse formation. To systematically profile proteome dynamics throughout neuronal differentiation, we took cultured rat hippocampal neurons at different developmental stages and monitored changes in protein abundance using a combination of stable isotope labeling and high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS). Almost one third of all 4,500 proteins quantified underwent a more than 2-fold expression change during neuronal differentiation, indicating extensive remodeling of the neuron proteome. To highlight the strength of our resource, we studied the neural-cell-adhesion molecule 1 (NCAM1) and found that it stimulates dendritic arbor development by promoting actin filament growth at the dendritic growth cone. We anticipate that our quantitative map of neuronal proteome dynamics is a rich resource for further analyses of the many identified proteins in various neurodevelopmental processes.

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Systematic profile of proteome dynamics throughout neuronal development in vitro

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Approximately 1,800 proteins show significant expression changes during differentiation

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Six expression profile clusters describe stage-specific patterns of protein dynamics

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This protein database may help to identify neurodevelopment mechanisms

Multi-omics Comparative Analysis Reveals Multiple Layers of Host Signaling Pathway Regulation by the Gut Microbiota

Multiple host pathways were affected by its adaptation to the microbiota. We have found significant transcriptome-proteome discordance caused by the microbiota. This discovery leads to the definite conclusion that transcript-level analysis is not sufficient to predict protein levels and their influence on the function of many specific cellular pathways, so only analysis of combinations of the quantitative data determined at different levels will lead to a complete understanding of the complex relationships between the host and the microbiota. Therefore, our results demonstrate the importance of using an integrative approach to study host-microbiota interaction at the molecular level.

The bodies of mammals are hosts to vast microbial communities composed of trillions of bacteria from thousands of species, whose effects on health and development have begun to be appreciated only recently. In this investigation, an integrated analysis combining proteomics and transcriptomics was used to quantitatively compare the terminal ilia from conventional and germfree mice. Female and male mice responded similarly to the microbiota, but C57BL/10A mice responded more strongly than BALB/c mice at both the transcriptome and proteome levels. The microbiota primarily caused upregulation of immunological pathways and downregulation of metabolic pathways in the conventional mice. Many of the affected pathways were altered only at either the transcriptome or proteome level. Of the pathways that were affected at both levels, most were affected concordantly. The discordant pathways were not principally involved in the immune system but instead were related to metabolism, oxidative phosphorylation, protein translation, transport, and turnover. To broaden the discovery of affected host pathways, a meta-analysis was performed using intestinal transcriptomics data from previously published studies of germfree versus conventional mice with diverse microbiota populations. Similar transcript-level responses to the microbiota were found, and many additional affected host pathways were discovered.

IMPORTANCE Multiple host pathways were affected by its adaptation to the microbiota. We have found significant transcriptome-proteome discordance caused by the microbiota. This discovery leads to the definite conclusion that transcript-level analysis is not sufficient to predict protein levels and their influence on the function of many specific cellular pathways, so only analysis of combinations of the quantitative data determined at different levels will lead to a complete understanding of the complex relationships between the host and the microbiota. Therefore, our results demonstrate the importance of using an integrative approach to study host-microbiota interaction at the molecular level.

Genetics of hypertension: an assessment of progress in the spontaneously hypertensive rat

The application of gene mapping methods to uncover the genetic basis of hypertension in the inbred spontaneously hypertensive rat (SHR) began over 25 yr ago. This animal provides a useful model of genetic high blood pressure, and some of its features are described. In particular, it appears to be a polygenic model of disease, and polygenes participate in human hypertension genetic risk. The SHR hypertension alleles were fixed rapidly by selective breeding in just a few generations and so are presumably common genetic variants present in the outbred Wistar strain from which SHR was created. This review provides a background to the origins and genesis of this rat line. It considers its usefulness as a model organism for a common cardiovascular disease. The progress and obstacles facing mapping are considered in depth, as are the emergence and application of other genome-wide genetic discovery approaches that have been applied to investigate this model. Candidate genes, their identification, and the evidence to support their potential role in blood pressure elevation are considered. The review assesses the progress that has arisen from this work has been limited. Consideration is given to some of the factors that have impeded progress, and prospects for advancing understanding of the genetic basis of hypertension in this model are discussed.

Impact of Alternative Splicing on the Human Proteome

Alternative splicing is a critical determinant of genome complexity and, by implication, is assumed to engender proteomic diversity. This notion has not been experimentally tested in a targeted, quantitative manner. Here, we have developed an integrative approach to ask whether perturbations in mRNA splicing patterns alter the composition of the proteome. We integrate RNA sequencing (RNA-seq) (to comprehensively report intron retention, differential transcript usage, and gene expression) with a data-independent acquisition (DIA) method, SWATH-MS (sequential window acquisition of all theoretical spectra-mass spectrometry), to capture an unbiased, quantitative snapshot of the impact of constitutive and alternative splicing events on the proteome. Whereas intron retention is accompanied by decreased protein abundance, alterations in differential transcript usage and gene expression alter protein abundance proportionate to transcript levels. Our findings illustrate how RNA splicing links isoform expression in the human transcriptome with proteomic diversity and provides a foundation for studying perturbations associated with human diseases.

Proteogenomics: Key Driver for Clinical Discovery and Personalized Medicine

Proteogenomics is a multi-omics research field that has the aim to efficiently integrate genomics, transcriptomics and proteomics. With this approach it is possible to identify new patient-specific proteoforms that may have implications in disease development, specifically in cancer. Understanding the impact of a large number of mutations detected at the genomics level is needed to assess the effects at the proteome level. Proteogenomics data integration would help in identifying molecular changes that are persistent across multiple molecular layers and enable better interpretation of molecular mechanisms of disease, such as the causal relationship between single nucleotide polymorphisms (SNPs) and the expression of transcripts and translation of proteins compared to mainstream proteomics approaches. Identifying patient-specific protein forms and getting a better picture of molecular mechanisms of disease opens the avenue for precision and personalized medicine. Proteogenomics is, however, a challenging interdisciplinary science that requires the understanding of sample preparation, data acquisition and processing for genomics, transcriptomics and proteomics. This chapter aims to guide the reader through the technology and bioinformatics aspects of these multi-omics approaches, illustrated with proteogenomics applications having clinical or biological relevance.

The hepatocyte proteome in organotypic rat liver models and the influence of the local microenvironment

BACKGROUND

Liver models that closely mimic the in vivo microenvironment are useful for understanding liver functions, capabilities, and intercellular communication processes. Three-dimensional (3D) liver models assembled using hepatocytes and liver sinusoidal endothelial cells (LSECs) separated by a polyelectrolyte multilayer (PEM) provide a functional system while also permitting isolation of individual cell types for proteomic analyses.

METHODS

To better understand the mechanisms and processes that underlie liver model function, hepatocytes were maintained as monolayers and 3D PEM-based formats in the presence or absence of primary LSECs. The resulting hepatocyte proteomes, the proteins in the PEM, and extracellular levels of urea, albumin and glucose after three days of culture were compared.

RESULTS

All systems were ketogenic and found to release glucose. The presence of the PEM led to increases in proteins associated with both mitochondrial and peroxisomal-based β-oxidation. The PEMs also limited production of structural and migratory proteins associated with dedifferentiation. The presence of LSECs increased levels of Phase I and Phase II biotransformation enzymes as well as several proteins associated with the endoplasmic reticulum and extracellular matrix remodeling. The proteomic analysis of the PEMs indicated that there was no significant change after three days of culture. These results are discussed in relation to liver model function.

CONCLUSIONS

Heterotypic cell-cell and cell-ECM interactions exert different effects on hepatocyte functions and phenotypes.

An Optimized Shotgun Strategy for the Rapid Generation of Comprehensive Human Proteomes

This study investigates the challenge of comprehensively cataloging the complete human proteome from a single-cell type using mass spectrometry (MS)-based shotgun proteomics. We modify a classical two-dimensional high-resolution reversed-phase peptide fractionation scheme and optimize a protocol that provides sufficient peak capacity to saturate the sequencing speed of modern MS instruments. This strategy enables the deepest proteome of a human single-cell type to date, with the HeLa proteome sequenced to a depth of ∼584,000 unique peptide sequences and ∼14,200 protein isoforms (∼12,200 protein-coding genes). This depth is comparable with next-generation RNA sequencing and enables the identification of post-translational modifications, including ∼7,000 N-acetylation sites and ∼10,000 phosphorylation sites, without the need for enrichment. We further demonstrate the general applicability and clinical potential of this proteomics strategy by comprehensively quantifying global proteome expression in several different human cancer cell lines and patient tissue samples.

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Multi-shot proteomics quantifies the protein levels of 12,200+ genes in HeLa cells

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This essentially complete HeLa proteome has coverage similar to next-gen RNA-seq

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Deep coverage of major PTMs is achieved without specific enrichment

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The approach is extendable to other human cell lines and patient samples

Alternative splicing shapes transcriptome but not proteome diversity in Physcomitrella patens

Alternative splicing (AS) can significantly impact the transcriptome and proteome of a eukaryotic cell. Here, using transcriptome and proteome profiling data, we analyzed AS in two life forms of the model moss Physcomitrella patens, namely protonemata and gametophores, as well as in protoplasts. We identified 12 043 genes subject to alternative splicing and analyzed the extent to which AS contributes to proteome diversity. We could distinguish a few examples that unambiguously indicated the presence of two or more splice isoforms from the same locus at the proteomic level. Our results indicate that alternative isoforms have a small effect on proteome diversity. We also revealed that mRNAs and pre-mRNAs have thousands of complementary binding sites for long non-coding RNAs (lncRNAs) that may lead to potential interactions in transcriptome. This finding points to an additional level of gene expression and AS regulation by non-coding transcripts in Physcomitrella patens. Among the differentially expressed and spliced genes we found serine/arginine-rich (SR) genes, which are known to regulate AS in cells. We found that treatment with abscisic (ABA) and methyl jasmonic acids (MeJA) led to an isoform-specific response and suggested that ABA in gametophores and MeJA in protoplasts regulate AS and the transcription of SR genes.

Regulatory signatures of liver regeneration distilled by integrative analysis of mRNA, histone methylation, and proteomics

The capacity of the liver to regenerate is likely to be encoded as a plasticity of molecular networks within the liver. By applying a combination of comprehensive analyses of the epigenome, transcriptome, and proteome, we herein depict the molecular landscape of liver regeneration. We demonstrated that histone H3 Lys-4 was trimethylated at the promoter regions of many loci, among which only a fraction, including cell-cycle-related genes, were transcriptionally up-regulated. A cistrome analysis guided by the histone methylation patterns and the transcriptome identified FOXM1 as the key transcription factor promoting liver regeneration, which was confirmed in vitro using a hepatocarcinoma cell line. The promoter regions of cell-cycle-related genes and Foxm1 acquired higher levels of trimethylated histone H3 Lys-4, suggesting that epigenetic regulations of these key regulatory genes define quiescence and regeneration of the liver cells. A quantitative proteome analysis of the regenerating liver revealed that conditional protein degradation also mediated regeneration-specific protein expression. These sets of informational resources should be useful for further investigations of liver regeneration.

Proteomic Analysis of Liver Proteins in a Rat Model of Chronic Restraint Stress-Induced Depression

Depression is a global mental disorder disease and greatly threatened human health and stress is considered to be one of the important factors that lead to depression. In this study, we used newly developed iTRAQ labeling and high performance liquid chromatography (HPLC) and mass spectrum united analysis technology obtained the 2176 accurate proteins. Successively, we used the GO analysis and IPA software to analyze the 98 differentially expressed proteins of liver in depression rats due to chronic restraint stress, showing a map of proteomics analysis of liver proteins from the aspects of related functions, disease and function analysis, canonical pathway analysis, and associated network. This study provide important information for comprehensively understanding the mechanisms of dysfunction or injury in the liver in depression.

Capillary zone electrophoresis–mass spectrometry with microliter-scale loading capacity, 140 min separation window and high peak capacity for bottom-up proteomics

CZE–MS can approach a microliter-scale loading capacity and a 140 min separation window for large-scale bottom-up proteomics.

The Future of "Omics" in Hypertension

Despite decades of research and clinical practice, the pathogenesis of hypertension remains incompletely understood, and blood pressure is often suboptimally controlled. “Omics” technologies allow the description of a large number of molecular features and have the potential to identify new factors that contribute to blood pressure regulation and how they interact. In this review, we focus on the potential of genomics, transcriptomics, proteomics, and metabolomics and explore their roles in unraveling the pathophysiology and diagnosis of hypertension, the prediction of organ damage and treatment response, and monitoring treatment effect. Substantial progress has been made in the area of genomics, in which genome-wide association studies have identified > 50 blood pressure–related, single-nucleotide polymorphisms, and sequencing studies (especially in secondary forms of hypertension) have discovered novel regulatory pathways. In contrast, other omics technologies, despite their ability to provide detailed insights into the physiological state of an organism, have only more recently demonstrated their impact on hypertension research and clinical practice. The majority of current proteomic studies focus on organ damage resulting from hypertension and may have the potential to help us understand the link between blood pressure and organ failure but also serve as biomarkers for early detection of cerebrovascular or coronary disease. Examples include signatures for early detection of left ventricular dysfunction or albuminuria. Metabolomic studies have the potential to integrate environmental and intrinsic factors and are particularly suited to monitor the response to treatment. We discuss examples of omics studies in hypertension and explore the challenges related to these novel technologies.

Mass Spectrometry-Based Approaches to Understand the Molecular Basis of Memory

The central nervous system is responsible for an array of cognitive functions such as memory, learning, language, and attention. These processes tend to take place in distinct brain regions; yet, they need to be integrated to give rise to adaptive or meaningful behavior. Since cognitive processes result from underlying cellular and molecular changes, genomics and transcriptomics assays have been applied to human and animal models to understand such events. Nevertheless, genes and RNAs are not the end products of most biological functions. In order to gain further insights toward the understanding of brain processes, the field of proteomics has been of increasing importance in the past years. Advancements in liquid chromatography-tandem mass spectrometry (LC-MS/MS) have enabled the identification and quantification of thousands of proteins with high accuracy and sensitivity, fostering a revolution in the neurosciences. Herein, we review the molecular bases of explicit memory in the hippocampus. We outline the principles of mass spectrometry (MS)-based proteomics, highlighting the use of this analytical tool to study memory formation. In addition, we discuss MS-based targeted approaches as the future of protein analysis.

From integrative genomics to systems genetics in the rat to link genotypes to phenotypes

Complementary to traditional gene mapping approaches used to identify the hereditary components of complex diseases, integrative genomics and systems genetics have emerged as powerful strategies to decipher the key genetic drivers of molecular pathways that underlie disease. Broadly speaking, integrative genomics aims to link cellular-level traits (such as mRNA expression) to the genome to identify their genetic determinants. With the characterization of several cellular-level traits within the same system, the integrative genomics approach evolved into a more comprehensive study design, called systems genetics, which aims to unravel the complex biological networks and pathways involved in disease, and in turn map their genetic control points. The first fully integrated systems genetics study was carried out in rats, and the results, which revealed conserved trans-acting genetic regulation of a pro-inflammatory network relevant to type 1 diabetes, were translated to humans. Many studies using different organisms subsequently stemmed from this example. The aim of this Review is to describe the most recent advances in the fields of integrative genomics and systems genetics applied in the rat, with a focus on studies of complex diseases ranging from inflammatory to cardiometabolic disorders. We aim to provide the genetics community with a comprehensive insight into how the systems genetics approach came to life, starting from the first integrative genomics strategies [such as expression quantitative trait loci (eQTLs) mapping] and concluding with the most sophisticated gene network-based analyses in multiple systems and disease states. Although not limited to studies that have been directly translated to humans, we will focus particularly on the successful investigations in the rat that have led to primary discoveries of genes and pathways relevant to human disease.

Proteogenomics: Integrating Next-Generation Sequencing and Mass Spectrometry to Characterize Human Proteomic Variation

Mass spectrometry-based proteomics has emerged as the leading method for detection, quantification, and characterization of proteins. Nearly all proteomic workflows rely on proteomic databases to identify peptides and proteins, but these databases typically contain a generic set of proteins that lack variations unique to a given sample, precluding their detection. Fortunately, proteogenomics enables the detection of such proteomic variations and can be defined, broadly, as the use of nucleotide sequences to generate candidate protein sequences for mass spectrometry database searching. Proteogenomics is experiencing heightened significance due to two developments: (a) advances in DNA sequencing technologies that have made complete sequencing of human genomes and transcriptomes routine, and (b) the unveiling of the tremendous complexity of the human proteome as expressed at the levels of genes, cells, tissues, individuals, and populations. We review here the field of human proteogenomics, with an emphasis on its history, current implementations, the types of proteomic variations it reveals, and several important applications.

Systems biology applications to study mechanisms of human immunodeficiency virus latency and reactivation

Eradication of human immunodeficiency virus (HIV) in infected individuals is currently not possible because of the presence of the persistent cellular reservoir of latent infection. The identification of HIV latency biomarkers and a better understanding of the molecular mechanisms contributing to regulation of HIV expression might provide essential tools to eliminate these latently infected cells. This review aims at summarizing gene expression profiling and systems biology applications to studies of HIV latency and eradication. Studies comparing gene expression in latently infected and uninfected cells identify candidate latency biomarkers and novel mechanisms of latency control. Studies that profiled gene expression changes induced by existing latency reversing agents (LRAs) highlight uniting themes driving HIV reactivation and novel mechanisms that contribute to regulation of HIV expression by different LRAs. Among the reviewed gene expression studies, the common approaches included identification of differentially expressed genes and gene functional category assessment. Integration of transcriptomic data with other biological data types is presently scarce, and the field would benefit from increased adoption of these methods in future studies. In addition, designing prospective studies that use the same methods of data acquisition and statistical analyses will facilitate a more reliable identification of latency biomarkers using different model systems and the comparison of the effects of different LRAs on host factors with a role in HIV reactivation. The results from such studies would have the potential to significantly impact the process by which candidate drugs are selected and combined for future evaluations and advancement to clinical trials.

Six alternative proteases for mass spectrometry-based proteomics beyond trypsin

Protein digestion using a dedicated protease represents a key element in a typical mass spectrometry (MS)-based shotgun proteomics experiment. Up to now, digestion has been predominantly performed with trypsin, mainly because of its high specificity, widespread availability and ease of use. Lately, it has become apparent that the sole use of trypsin in bottom-up proteomics may impose certain limits in our ability to grasp the full proteome, missing out particular sites of post-translational modifications, protein segments or even subsets of proteins. To overcome this problem, the proteomics community has begun to explore alternative proteases to complement trypsin. However, protocols, as well as expected results generated from these alternative proteases, have not been systematically documented. Therefore, here we provide an optimized protocol for six alternative proteases that have already shown promise in their applicability in proteomics, namely chymotrypsin, LysC, LysN, AspN, GluC and ArgC. This protocol is formulated to promote ease of use and robustness, which enable parallel digestion with each of the six tested proteases. We present data on protease availability and usage including recommendations for reagent preparation. We additionally describe the appropriate MS data analysis methods and the anticipated results in the case of the analysis of a single protein (BSA) and a more complex cellular lysate (Escherichia coli). The digestion protocol presented here is convenient and robust and can be completed in ∼2 d.

An NGS-Independent Strategy for Proteome-Wide Identification of Single Amino Acid Polymorphisms by Mass Spectrometry

Detection of proteins containing single amino acid polymorphisms (SAPs) encoded by nonsynonymous SNPs (nsSNPs) can aid researchers in studying the functional significance of protein variants. Most proteogenomic approaches for large-scale SAPs mapping require construction of a sample-specific database containing protein variants predicted from the next-generation sequencing (NGS) data. Searching shotgun proteomic data sets against these NGS-derived databases allowed for identification of SAP peptides, thus validating the proteome-level sequence variation. Contrary to the conventional approaches, our study presents a novel strategy for proteome-wide SAP detection without relying on sample-specific NGS data. By searching a deep-coverage proteomic data set from an industrial thermotolerant yeast strain using our strategy, we identified 337 putative SAPs compared to the reference genome. Among the SAP peptides identified with stringent criteria, 85.2% of SAP sites were validated using whole-genome sequencing data obtained for this organism, which indicates high accuracy of SAP identification with our strategy. More interestingly, for certain SAP peptides that cannot be predicted by genomic sequencing, we used synthetic peptide standards to verify expression of peptide variants in the proteome. Our study has provided a unique tool for proteogenomics to enable proteome-wide direct SAP identification and capture nongenetic protein variants not linked to nsSNPs.