Comprehensive proteomics

Multiomics data integration, limitations, and prospects to reveal the metabolic activity of the coral holobiont

Since their radiation in the Middle Triassic period ∼240 million years ago, stony corals have survived past climate fluctuations and five mass extinctions. Their long-term survival underscores the inherent resilience of corals, particularly when considering the nutrient-poor marine environments in which they have thrived. However, coral bleaching has emerged as a global threat to coral survival, requiring rapid advancements in coral research to understand holobiont stress responses and allow for interventions before extensive bleaching occurs. This review encompasses the potential, as well as the limits, of multiomics data applications when applied to the coral holobiont. Synopses for how different omics tools have been applied to date and their current restrictions are discussed, in addition to ways these restrictions may be overcome, such as recruiting new technology to studies, utilizing novel bioinformatics approaches, and generally integrating omics data. Lastly, this review presents considerations for the design of holobiont multiomics studies to support lab-to-field advancements of coral stress marker monitoring systems. Although much of the bleaching mechanism has eluded investigation to date, multiomic studies have already produced key findings regarding the holobiont's stress response, and have the potential to advance the field further.

Comprehensive Proteome Profiling of a Xanthomonas campestris pv. Campestris B100 Culture Grown in Minimal Medium with a Specific Focus on Nutrient Consumption and Xanthan Biosynthesis

Xanthan, a bacterial polysaccharide, is widespread in industrial applications, particularly as a food additive. However, little is known about the process of xanthan synthesis on the proteome level, even though Xanthomonas campestris is frequently used for xanthan fermentation. A label-free LC-MS/MS method was employed to study the protein changes during xanthan fermentation in minimal medium. According to the reference database, 2416 proteins were identified, representing 54.75 % of the proteome. The study examined changes in protein abundances concerning the growth phase and xanthan productivity. Throughout the experiment, changes in nitrate concentration appeared to affect the abundance of most proteins involved in nitrogen metabolism, except Gdh and GlnA. Proteins involved in sugar nucleotide metabolism stay unchanged across all growth phases. Apart from GumD, GumB, and GumC, the gum proteins showed no significant changes throughout the experiment. GumD, the first enzyme in the assembly of the xanthan-repeating unit, peaked during the early stationary phase but decreased during the late stationary phase. GumB and GumC, which are involved in exporting xanthan, increased significantly during the stationary phase. This study suggests that a potential bottleneck for xanthan productivity does not reside in the abundance of proteins directly involved in the synthesis pathways.

Deciphering a proteomic signature for the early detection of breast cancer from breast milk: the role of quantitative proteomics

INTRODUCTION

Breast cancer is one of the most prevalent cancers among women in the United States. Current research regarding breast milk has been focused on the composition and its role in infant growth and development. There is little information about the proteins, immune cells, and epithelial cells present in breast milk which can be indicative of the emergence of BC cells and tumors.

AREAS COVERED

We summarize all breast milk studies previously done in our group using proteomics. These studies include 1D-PAGE and 2D-PAGE analysis of breast milk samples, which include within woman and across woman comparisons to identify dysregulated proteins in breast milk and the roles of these proteins in both the development of BC and its diagnosis. Our projected outlook for the use of milk for cancer detection is also discussed.

EXPERT OPINION

Analyzing the samples by multiple methods allows one to interrogate a set of samples with various biochemical methods that complement each other, thus providing a more comprehensive proteome. Complementing methods like 1D-PAGE, 2D-PAGE, in-solution digestion and proteomics analysis with PTM-omics, peptidomics, degradomics, or interactomics will provide a better understanding of the dysregulated proteins, but also the modifications or interactions between these proteins.

Multifaceted Pharmacological Potentials of Curcumin, Genistein, and Tanshinone IIA through Proteomic Approaches: An In-Depth Review

Phytochemicals possess various intriguing pharmacological properties against diverse pathological conditions. Extensive studies are on-going to understand the structural/functional properties of phytochemicals as well as the molecular mechanisms of their therapeutic function against various disease conditions. Phytochemicals such as curcumin (Cur), genistein (Gen), and tanshinone-IIA (Tan IIA) have multifaceted therapeutic potentials and various efforts are in progress to understand the molecular dynamics of their function with different tools and technologies. Cur is an active lipophilic polyphenol with pleiotropic function, and it has been shown to possess various intriguing properties including antioxidant, anti-inflammatory, anti-microbial, anticancer, and anti-genotoxic properties besides others beneficial properties. Similarly, Gen (an isoflavone) exhibits a wide range of vital functions including antioxidant, anti-inflammatory, pro-apoptotic, anti-proliferative, anti-angiogenic activities etc. In addition, Tan IIA, a lipophilic compound, possesses antioxidant, anti-angiogenic, anti-inflammatory, anticancer activities, and so on. Over the last few decades, the field of proteomics has garnered great momentum mainly attributed to the recent advancement in mass spectrometry (MS) techniques. It is envisaged that the proteomics technology has considerably contributed to the biomedical research endeavors lately. Interestingly, they have also been explored as a reliable approach to understand the molecular intricacies related to phytochemical-based therapeutic interventions. The present review provides an overview of the proteomics studies performed to unravel the underlying molecular intricacies of various phytochemicals such as Cur, Gen, and Tan IIA. This in-depth study will help the researchers in better understanding of the pharmacological potential of the phytochemicals at the proteomics level. Certainly, this review will be highly instrumental in catalyzing the translational shift from phytochemical-based biomedical research to clinical practice in the near future.

Multiomics technologies: role in disease biomarker discoveries and therapeutics

 

Medical research has been revolutionized after the publication of the full human genome. This was the major landmark that paved the way for understanding the biological functions of different macro and micro molecules. With the advent of different high-throughput technologies, biomedical research was further revolutionized. These technologies constitute genomics, transcriptomics, proteomics, metabolomics, etc. Collectively, these high-throughputs are referred to as multi-omics technologies. In the biomedical field, these omics technologies act as efficient and effective tools for disease diagnosis, management, monitoring, treatment and discovery of certain novel disease biomarkers. Genotyping arrays and other transcriptomic studies have helped us to elucidate the gene expression patterns in different biological states, i.e. healthy and diseased states. Further omics technologies such as proteomics and metabolomics have an important role in predicting the role of different biological molecules in an organism. It is because of these high throughput omics technologies that we have been able to fully understand the role of different genes, proteins, metabolites and biological pathways in a diseased condition. To understand a complex biological process, it is important to apply an integrative approach that analyses the multi-omics data in order to highlight the possible interrelationships of the involved biomolecules and their functions. Furthermore, these omics technologies offer an important opportunity to understand the information that underlies disease. In the current review, we will discuss the importance of omics technologies as promising tools to understand the role of different biomolecules in diseases such as cancer, cardiovascular diseases, neurodegenerative diseases and diabetes.

SUMMARY POINTS

Transcriptome-proteome compendium of the Antarctic krill (Euphausia superba): Metabolic potential and repertoire of hydrolytic enzymes

The Antarctic krill (Euphausia superba Dana) is a keystone species in the Southern Ocean that uses an arsenal of hydrolases for biomacromolecule decomposition to effectively digest its omnivorous diet. The present study builds on a hybrid-assembled transcriptome (13,671 ORFs) combined with comprehensive proteome profiling. The analysis of individual krill compartments allowed detection of significantly more different proteins compared to that of the entire animal (1,464 vs. 294 proteins). The nearby krill sampling stations in the Bransfield Strait (Antarctic Peninsula) yielded rather uniform proteome datasets. Proteins related to energy production and lipid degradation were particularly abundant in the abdomen, agreeing with the high energy demand of muscle tissue. A total of 378 different biomacromolecule hydrolysing enzymes were detected, including 250 proteases, 99 CAZymes, 14 nucleases and 15 lipases. The large repertoire in proteases is in accord with the protein-rich diet affiliated with E. superba's omnivorous lifestyle and complex biology. The richness in chitin-degrading enzymes allows not only digestion of zooplankton diet, but also the utilization of the discharged exoskeleton after moulting. This article is protected by copyright. All rights reserved.

Recent advancements in glycoproteomic studies: Glycopeptide enrichment and derivatization, characterization of glycosylation in SARS CoV2, and interacting glycoproteins

Proteomics studies allow for the determination of the identity, amount, and interactions of proteins under specific conditions that allow the biological state of an organism to ultimately change. These conditions can be either beneficial or detrimental. Diseases are due to detrimental changes caused by either protein overexpression or underexpression caused by as a result of a mutation or posttranslational modifications (PTM), among other factors. Identification of disease biomarkers through proteomics can be potentially used as clinical information for diagnostics. Common biomarkers to look for include PTM. For example, aberrant glycosylation of proteins is a common marker and will be a focus of interest in this review. A common way to analyze glycoproteins is by glycoproteomics involving mass spectrometry. Due to factors such as micro- and macroheterogeneity which result in a lower abundance of each version of a glycoprotein, it is difficult to obtain meaningful results unless rigorous sample preparation procedures are in place. Microheterogeneity represents the diversity of glycans at a single site, whereas macroheterogeneity depicts glycosylation levels at each site of a protein. Enrichment and derivatization of glycopeptides help to overcome these limitations. Over the time range of 2016 to 2020, several methods have been proposed in the literature and have contributed to drastically improve the outcome of glycosylation analysis, as presented in the sampling surveyed in this review. As a current topic in 2020, glycoproteins carried by pathogens can also cause disease and this is seen with SARS CoV2, causing the COVID-19 pandemic. This review will discuss glycoproteomic studies of the spike glycoprotein and interacting proteins such as the ACE2 receptor.

Identifying biomarkers for prenatal diagnosis of neural tube defects based on "omics"

Neural tube defects (NTDs) are the most severe birth defects and the main cause of newborn death; posing a great challenge to the affected children, families, and societies. Presently, the clinical diagnosis of NTDs mainly relies on ultrasound images combined with certain indices, such as alpha-fetoprotein levels in the maternal serum and amniotic fluid. Recently, the discovery of additional biomarkers in maternal tissue has presented new possibilities for prenatal diagnosis. Over the past 20 years, "omics" techniques have provided the premise for the study of biomarkers. This review summarizes recent advances in candidate biomarkers for the prenatal diagnosis of fetal NTDs based on omics techniques using maternal biological specimens of different origins, including amniotic fluid, blood, and urine, which may provide a foundation for the early prenatal diagnosis of NTDs.

Omics sciences for systems biology in Alzheimer's disease: State-of-the-art of the evidence

Alzheimer's disease (AD) is characterized by non-linear, genetic-driven pathophysiological dynamics with high heterogeneity in biological alterations and disease spatial-temporal progression. Human in-vivo and post-mortem studies point out a failure of multi-level biological networks underlying AD pathophysiology, including proteostasis (amyloid-β and tau), synaptic homeostasis, inflammatory and immune responses, lipid and energy metabolism, oxidative stress. Therefore, a holistic, systems-level approach is needed to fully capture AD multi-faceted pathophysiology. Omics sciences - genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics - embedded in the systems biology (SB) theoretical and computational framework can generate explainable readouts describing the entire biological continuum of a disease. Such path in Neurology is encouraged by the promising results of omics sciences and SB approaches in Oncology, where stage-driven pathway-based therapies have been developed in line with the precision medicine paradigm. Multi-omics data integrated in SB network approaches will help detect and chart AD upstream pathomechanistic alterations and downstream molecular effects occurring in preclinical stages. Finally, integrating omics and neuroimaging data - i.e., neuroimaging-omics - will identify multi-dimensional biological signatures essential to track the clinical-biological trajectories, at the subpopulation or even individual level.

Biophysical prediction of protein-peptide interactions and signaling networks using machine learning

In mammalian cells, much of signal transduction is mediated by weak protein-protein interactions between globular peptide-binding domains (PBDs) and unstructured peptidic motifs in partner proteins. The number and diversity of these PBDs (over 1,800 are known), low binding affinities, and sensitivity of binding properties to minor sequence variation represent a substantial challenge to experimental and computational analysis of PBD specificity and the networks PBDs create. Here we introduce a bespoke machine learning approach, hierarchical statistical mechanical modelling (HSM), capable of accurately predicting the affinities of PBD-peptide interactions across multiple protein families. By synthesizing biophysical priors within a modern machine learning framework, HSM outperforms existing computational methods and high-throughput experimental assays. HSM models are interpretable in familiar biophysical terms at three spatial scales: the energetics of protein-peptide binding, the multi-dentate organization of protein-protein interactions, and the global architecture of signaling networks.

Hypothesis: protein and RNA attributes are continuously optimized over time

Background

Little is known why proteins and RNAs exhibit half-lives varying over several magnitudes. Despite many efforts, a conclusive link between half-lives and gene function could not be established suggesting that other determinants may influence these molecular attributes.

Results

Here, I find that with increasing gene age there is a gradual and significant increase of protein and RNA half-lives, protein structure, and other molecular attributes that tend to affect protein abundance. These observations are accommodated in a hypothesis which posits that new genes at ‘birth’ are not optimized and thus their products exhibit low half-lives and less structure but continuous mutagenesis eventually improves these attributes. Thus, the protein and RNA products of the oldest genes obtained their high degrees of stability and structure only after billions of years while the products of younger genes had less time to be optimized and are therefore less stable and structured. Because more stable proteins with lower turnover require less transcription to maintain the same level of abundance, reduced transcription-associated mutagenesis (TAM) would fixate the changes by increasing gene conservation.

Conclusions

Consequently, the currently observed diversity of molecular attributes is a snapshot of gene products being at different stages along their temporal path of optimization.

Angiogenic and Immunologic Proteins Identified by Deep Proteomic Profiling of Human Retinal and Choroidal Vascular Endothelial Cells: Potential Targets for New Biologic Drugs

PURPOSE

Diseases that involve retinal or choroidal vascular endothelial cells are leading causes of vision loss: age-related macular degeneration, retinal ischemic vasculopathies, and noninfectious posterior uveitis. Proteins differentially expressed by these endothelial cell populations are potential drug targets. We used deep proteomic profiling to define the molecular phenotype of human retinal and choroidal endothelial cells at the protein level.

METHODS

Retinal and choroidal vascular endothelial cells were separately isolated from 5 human eye pairs by selection on CD31. Total protein was extracted and digested, and peptide fractions were analyzed by reverse-phase liquid chromatography tandem mass spectrometry. Peptide sequences were assigned to fragment ion spectra, and proteins were inferred from openly accessible protein databases. Protein abundance was determined by spectral counting. Publicly available software packages were used to identify proteins that were differentially expressed between human retinal and choroidal endothelial cells, and to classify proteins that were highly abundant in each endothelial cell population.

RESULTS

Human retinal and/or choroidal vascular endothelial cells expressed 5042 nonredundant proteins. Setting the differential expression false discovery rate at 0.05, 498 proteins of 3454 quantifiable proteins (14.4%) with minimum mean spectral counts of 2.5 were differentially abundant in the 2 cell populations. Retinal and choroidal endothelial cells were enriched in angiogenic proteins, and retinal endothelial cells were also enriched in immunologic proteins.

CONCLUSIONS

This work describes the different protein expression profiles of human retinal and choroidal vascular endothelial cells, and provides multiple candidates for further study as novel treatments or drug targets for posterior eye diseases. NOTE: Publication of this article is sponsored by the American Ophthalmological Society.

Evaluating the Possibility of Detecting Variants in Shotgun Proteomics via LeTE-Fusion Analysis Pipeline

In proteogenomic studies, many genome-annotated events, for example, single amino acid variation (SAAV) and short INDEL, are often unobserved in shotgun proteomics. Therefore, we propose an analysis pipeline called LeTE-fusion (Le, peptide length; T, theoretical values; E, experimental data) to first investigate whether peptides with certain lengths are observed more often in mass spectrometry (MS)-based proteomics, which may hinder peptide identification causing difficulty in detecting genome-annotated events. By applying LeTE-fusion on different MS-based proteome data sets, we found peptides within 7-20 amino acids are more frequently identified, possibly attributed to MS-related factors instead of proteases. We then further extended the usage of LeTE-fusion on four variant-containing-sequence data sets (SAAV-only) with various sample complexity up to the whole human proteome scale, which yields theoretically ∼70% variants observable in an ideal shotgun proteomics. However, only ∼40% of variants might be detectable in real shotgun proteomic experiments when LeTE-fusion utilizes the experimentally observed variant-site-containing wild-type peptides in PeptideAtlas to estimate the expected observable coverage of variants. Finally, we conducted a case study on HEK293 cell line with variants reported at genomic level that were also identified in shotgun proteomics to demonstrate the efficacy of LeTE-fusion on estimating expected observable coverage of variants. To the best of our knowledge, this is the first study to systematically investigate the detection limits of genome-annotated events via shotgun proteomics using such analysis pipeline.

Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment

Precision medicine is medicine optimized to the genotypic and phenotypic characteristics of an individual and, when present, his or her disease. It has a host of targets, including genes and their transcripts, proteins, and metabolites. Studying precision medicine involves a systems biology approach that integrates mathematical modeling and biology genomics, transcriptomics, proteomics, and metabolomics. Moreover, precision medicine must consider not only the relatively static genetic codes of individuals, but also the dynamic and heterogeneous genetic codes of cancers. Thus, precision medicine relies not only on discovering identifiable targets for treatment and surveillance modification, but also on reliable, noninvasive methods of identifying changes in these targets over time. Imaging via radiomics and radiogenomics is poised for a central role. Radiomics, which extracts large volumes of quantitative data from digital images and amalgamates these together with clinical and patient data into searchable shared databases, potentiates radiogenomics, which is the combination of genetic and radiomic data. Radiogenomics may provide voxel-by-voxel genetic information for a complete, heterogeneous tumor or, in the setting of metastatic disease, set of tumors and thereby guide tailored therapy. Radiogenomics may also quantify lesion characteristics, to better differentiate between benign and malignant entities, and patient characteristics, to better stratify patients according to risk for disease, thereby allowing for more precise imaging and screening. This report provides an overview of precision medicine and discusses radiogenomics specifically in breast cancer. ^© RSNA, 2018.

Fixed-Charge Trimethyl Pyrilium Modification for Enabling Enhanced Top-Down Mass Spectrometry Sequencing of Intact Protein Complexes

Mass spectrometry of intact proteins and protein complexes has the potential to provide a transformative level of information on biological systems, ranging from sequence and post-translational modification analysis to the structures of whole protein assemblies. This ambitious goal requires the efficient fragmentation of both intact proteins and the macromolecular, multicomponent machines they collaborate to create through noncovalent interactions. Improving technologies in an effort to achieve such fragmentation remains perhaps the greatest challenge facing current efforts to comprehensively analyze cellular protein composition and is essential to realizing the full potential of proteomics. In this work, we describe the use of a trimethyl pyrylium (TMP) fixed-charge covalent labeling strategy aimed at enhancing fragmentation for challenging intact proteins and intact protein complexes. Combining analysis of TMP-modified and unmodified protein complexes results in a greater diversity of regions within the protein that give rise to fragments, and results in an up to 2.5-fold increase in sequence coverage when compared to unmodified protein alone, for protein complexes up to 148 kDa. TMP modification offers a simple and powerful platform to expand the capabilities of existing mass spectrometric instrumentation for the complete characterization of intact protein assemblies.

IMTBX and Grppr: Software for Top-Down Proteomics Utilizing Ion Mobility-Mass Spectrometry

Top-down proteomics has emerged as a transformative method for the analysis of protein sequence and post-translational modifications (PTMs). Top-down experiments have historically been performed primarily on ultrahigh resolution mass spectrometers due to the complexity of spectra resulting from fragmentation of intact proteins, but recent advances in coupling ion mobility separations to faster, lower resolution mass analyzers now offer a viable alternative. However, software capable of interpreting the highly complex two-dimensional spectra that result from coupling ion mobility separation to top-down experiments is currently lacking. In this manuscript we present a software suite consisting of two programs, IMTBX ("IM Toolbox") and Grppr ("Grouper"), that enable fully automated processing of such data. We demonstrate the capabilities of this software suite by examining a series of intact proteins on a Waters Synapt G2 ion-mobility equipped mass spectrometer and compare the results to the manual and semiautomated data analysis procedures we have used previously.

Phosphoproteomics of Primary Cells Reveals Druggable Kinase Signatures in Ovarian Cancer

Our understanding of the molecular determinants of cancer is still inadequate because of cancer heterogeneity. Here, using epithelial ovarian cancer (EOC) as a model system, we analyzed a minute amount of patient-derived epithelial cells from either healthy or cancerous tissues by single-shot mass-spectrometry-based phosphoproteomics. Using a multi-disciplinary approach, we demonstrated that primary cells recapitulate tissue complexity and represent a valuable source of differentially expressed proteins and phosphorylation sites that discriminate cancer from healthy cells. Furthermore, we uncovered kinase signatures associated with EOC. In particular, CDK7 targets were characterized in both EOC primary cells and ovarian cancer cell lines. We showed that CDK7 controls cell proliferation and that pharmacological inhibition of CDK7 selectively represses EOC cell proliferation. Our approach defines the molecular landscape of EOC, paving the way for efficient therapeutic approaches for patients. Finally, we highlight the potential of phosphoproteomics to identify clinically relevant and druggable pathways in cancer.

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We analyze ex-vivo-cultured primary cells using phosphoproteomics

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We investigate epithelial ovarian cancer (EOC) and healthy tissue

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We uncover expression of cancer-specific proteins and kinase signatures

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The kinase CDK7 phosphorylates POLR2A and regulates EOC cell proliferation

Advances in microscale separations towards nanoproteomics applications

Microscale separation (e.g., liquid chromatography or capillary electrophoresis) coupled with mass spectrometry (MS) has become the primary tool for advanced proteomics, an indispensable technology for gaining understanding of complex biological processes. In recent decades significant advances have been achieved in MS-based proteomics. However, the current proteomics platforms still face an analytical challenge in overall sensitivity towards nanoproteomics applications for starting materials of less than 1μg total proteins (e.g., cellular heterogeneity in tissue pathologies). Herein, we review recent advances in microscale separation techniques and integrated sample processing strategies that improve the overall sensitivity and proteome coverage of the proteomics workflow, and their contributions towards nanoproteomics applications.

Loss-less Nano-fractionator for High Sensitivity, High Coverage Proteomics

Recent advances in mass spectrometry (MS)-based proteomics now allow very deep coverage of cellular proteomes. To achieve near-comprehensive identification and quantification, the combination of a first HPLC-based peptide fractionation orthogonal to the on-line LC-MS/MS step has proven to be particularly powerful. This first dimension is typically performed with milliliter/min flow and relatively large column inner diameters, which allow efficient pre-fractionation but typically require peptide amounts in the milligram range. Here, we describe a novel approach termed "spider fractionator" in which the post-column flow of a nanobore chromatography system enters an eight-port flow-selector rotor valve. The valve switches the flow into different flow channels at constant time intervals, such as every 90 s. Each flow channel collects the fractions into autosampler vials of the LC-MS/MS system. Employing a freely configurable collection mechanism, samples are concatenated in a loss-less manner into 2-96 fractions, with efficient peak separation. The combination of eight fractions with 100 min gradients yields very deep coverage at reasonable measurement time, and other parameters can be chosen for even more rapid or for extremely deep measurements. We demonstrate excellent sensitivity by decreasing sample amounts from 100 μg into the sub-microgram range, without losses attributable to the spider fractionator and while quantifying close to 10,000 proteins. Finally, we apply the system to the rapid automated and in-depth characterization of 12 different human cell lines to a median depth of 11,472 different proteins, which revealed differences recapitulating their developmental origin and differentiation status. The fractionation technology described here is flexible, easy to use, and facilitates comprehensive proteome characterization with minimal sample requirements.

Chemical Glycoproteomics

Chemical tools have accelerated progress in glycoscience, reducing experimental barriers to studying protein glycosylation, the most widespread and complex form of posttranslational modification. For example, chemical glycoproteomics technologies have enabled the identification of specific glycosylation sites and glycan structures that modulate protein function in a number of biological processes. This field is now entering a stage of logarithmic growth, during which chemical innovations combined with mass spectrometry advances could make it possible to fully characterize the human glycoproteome. In this review, we describe the important role that chemical glycoproteomics methods are playing in such efforts. We summarize developments in four key areas: enrichment of glycoproteins and glycopeptides from complex mixtures, emphasizing methods that exploit unique chemical properties of glycans or introduce unnatural functional groups through metabolic labeling and chemoenzymatic tagging; identification of sites of protein glycosylation; targeted glycoproteomics; and functional glycoproteomics, with a focus on probing interactions between glycoproteins and glycan-binding proteins. Our goal with this survey is to provide a foundation on which continued technological advancements can be made to promote further explorations of protein glycosylation.

Demonstration of Protein-Based Human Identification Using the Hair Shaft Proteome

Human identification from biological material is largely dependent on the ability to characterize genetic polymorphisms in DNA. Unfortunately, DNA can degrade in the environment, sometimes below the level at which it can be amplified by PCR. Protein however is chemically more robust than DNA and can persist for longer periods. Protein also contains genetic variation in the form of single amino acid polymorphisms. These can be used to infer the status of non-synonymous single nucleotide polymorphism alleles. To demonstrate this, we used mass spectrometry-based shotgun proteomics to characterize hair shaft proteins in 66 European-American subjects. A total of 596 single nucleotide polymorphism alleles were correctly imputed in 32 loci from 22 genes of subjects' DNA and directly validated using Sanger sequencing. Estimates of the probability of resulting individual non-synonymous single nucleotide polymorphism allelic profiles in the European population, using the product rule, resulted in a maximum power of discrimination of 1 in 12,500. Imputed non-synonymous single nucleotide polymorphism profiles from European-American subjects were considerably less frequent in the African population (maximum likelihood ratio = 11,000). The converse was true for hair shafts collected from an additional 10 subjects with African ancestry, where some profiles were more frequent in the African population. Genetically variant peptides were also identified in hair shaft datasets from six archaeological skeletal remains (up to 260 years old). This study demonstrates that quantifiable measures of identity discrimination and biogeographic background can be obtained from detecting genetically variant peptides in hair shaft protein, including hair from bioarchaeological contexts.

Improving GENCODE reference gene annotation using a high-stringency proteogenomics workflow

Complete annotation of the human genome is indispensable for medical research. The GENCODE consortium strives to provide this, augmenting computational and experimental evidence with manual annotation. The rapidly developing field of proteogenomics provides evidence for the translation of genes into proteins and can be used to discover and refine gene models. However, for both the proteomics and annotation groups, there is a lack of guidelines for integrating this data. Here we report a stringent workflow for the interpretation of proteogenomic data that could be used by the annotation community to interpret novel proteogenomic evidence. Based on reprocessing of three large-scale publicly available human data sets, we show that a conservative approach, using stringent filtering is required to generate valid identifications. Evidence has been found supporting 16 novel protein-coding genes being added to GENCODE. Despite this many peptide identifications in pseudogenes cannot be annotated due to the absence of orthogonal supporting evidence.

Identifying and annotating functional elements in the human genome remains a challenging but important task. Here the authors propose a priority annotation score to rank identifications and suggest how proteogenomics evidence can be interpreted and what additional information substantiates protein-coding potential for annotation.

Micro-proteomics with iterative data analysis: Proteome analysis in C. elegans at the single worm level

Proteomics studies typically analyze proteins at a population level, using extracts prepared from tens of thousands to millions of cells. The resulting measurements correspond to average values across the cell population and can mask considerable variation in protein expression and function between individual cells or organisms. Here, we report the development of micro‐proteomics for the analysis of Caenorhabditis elegans, a eukaryote composed of 959 somatic cells and ∼1500 germ cells, measuring the worm proteome at a single organism level to a depth of ∼3000 proteins. This includes detection of proteins across a wide dynamic range of expression levels (>6 orders of magnitude), including many chromatin‐associated factors involved in chromosome structure and gene regulation. We apply the micro‐proteomics workflow to measure the global proteome response to heat‐shock in individual nematodes. This shows variation between individual animals in the magnitude of proteome response following heat‐shock, including variable induction of heat‐shock proteins. The micro‐proteomics pipeline thus facilitates the investigation of stochastic variation in protein expression between individuals within an isogenic population of C. elegans. All data described in this study are available online via the Encyclopedia of Proteome Dynamics (http://www.peptracker.com/epd), an open access, searchable database resource.

Cell type- and brain region-resolved mouse brain proteome

Brain transcriptome and connectome maps are being generated, but an equivalent effort on the proteome is currently lacking. We performed high-resolution mass spectrometry-based proteomics for in-depth analysis of the mouse brain and its major brain regions and cell types. Comparisons of the 12,934 identified proteins in oligodendrocytes, astrocytes, microglia and cortical neurons with deep sequencing data of the transcriptome indicated deep coverage of the proteome. Cell type-specific proteins defined as tenfold more abundant than average expression represented about a tenth of the proteome, with an overrepresentation of cell surface proteins. To demonstrate the utility of our resource, we focused on this class of proteins and identified Lsamp, an adhesion molecule of the IgLON family, as a negative regulator of myelination. Our findings provide a framework for a system-level understanding of cell-type diversity in the CNS and serves as a rich resource for analyses of brain development and function.

A human interactome in three quantitative dimensions organized by stoichiometries and abundances

The organization of a cell emerges from the interactions in protein networks. The interactome is critically dependent on the strengths of interactions and the cellular abundances of the connected proteins, both of which span orders of magnitude. However, these aspects have not yet been analyzed globally. Here, we have generated a library of HeLa cell lines expressing 1,125 GFP-tagged proteins under near-endogenous control, which we used as input for a next-generation interaction survey. Using quantitative proteomics, we detect specific interactions, estimate interaction stoichiometries, and measure cellular abundances of interacting proteins. These three quantitative dimensions reveal that the protein network is dominated by weak, substoichiometric interactions that play a pivotal role in defining network topology. The minority of stable complexes can be identified by their unique stoichiometry signature. This study provides a rich interaction dataset connecting thousands of proteins and introduces a framework for quantitative network analysis.

Deep proteomic profiling of vasopressin-sensitive collecting duct cells. I. Virtual Western blots and molecular weight distributions

The mouse mpkCCD cell line is a continuous cultured epithelial cell line with characteristics of renal collecting duct principal cells. This line is widely used to study epithelial transport and its regulation. To provide a data resource useful for experimental design and interpretation in studies using mpkCCD cells, we have carried out "deep" proteomic profiling of these cells using three levels of fractionation (differential centrifugation, SDS-PAGE, and HPLC) followed by tandem mass spectrometry to identify and quantify proteins. The analysis of all resulting samples generated 34.6 gigabytes of spectral data. As a result, we identified 6,766 proteins in mpkCCD cells at a high level of stringency. These proteins are expressed over eight orders of magnitude of protein abundance. The data are provided to users as a public data base (https://helixweb.nih.gov/ESBL/Database/mpkFractions/). The mass spectrometry data were mapped back to their gel slices to generate "virtual Western blots" for each protein. For most of the 6,766 proteins, the apparent molecular weight from SDS-PAGE agreed closely with the calculated molecular weight. However, a substantial fraction (>15%) of proteins was found to run aberrantly, with much higher or much lower mobilities than predicted. These proteins were analyzed to identify mechanisms responsible for altered mobility on SDS-PAGE, including high or low isoelectric point, high or low hydrophobicity, physiological cleavage, residence in the lysosome, posttranslational modifications, and expression of alternative isoforms due to alternative exon usage. Additionally, this analysis identified a previously unrecognized isoform of aquaporin-2 with apparent molecular mass <20 kDa.

Comparative Proteomic Analysis of Human Liver Tissue and Isolated Hepatocytes with a Focus on Proteins Determining Drug Exposure

Freshly isolated human hepatocytes are considered the gold standard for in vitro studies of liver functions, including drug transport, metabolism, and toxicity. For accurate predictions of the in vivo outcome, the isolated hepatocytes should reflect the phenotype of their in vivo counterpart, i.e., hepatocytes in human liver tissue. Here, we quantified and compared the membrane proteomes of freshly isolated hepatocytes and human liver tissue using a label-free shotgun proteomics approach. A total of 5144 unique proteins were identified, spanning over 6 orders of magnitude in abundance. There was a good global correlation in protein abundance. However, the expression of many plasma membrane proteins was lower in the isolated hepatocytes than in the liver tissue. This included transport proteins that determine hepatocyte exposure to many drugs and endogenous compounds. Pathway analysis of the differentially expressed proteins confirmed that hepatocytes are exposed to oxidative stress during isolation and suggested that plasma membrane proteins were degraded via the protein ubiquitination pathway. Finally, using pitavastatin as an example, we show how protein quantifications can improve in vitro predictions of in vivo liver clearance. We tentatively conclude that our data set will be a useful resource for improved hepatocyte predictions of the in vivo outcome.

Proteomics and Transcriptomics of BJAB Cells Expressing the Epstein-Barr Virus Noncoding RNAs EBER1 and EBER2

In Epstein-Barr virus (EBV) latent infection, the EBV-encoded RNAs EBER1 and EBER2 accumulate in the host cell nucleus to ~10⁶ copies. While the expression of EBERs in cell lines is associated with transformation, a mechanistic explanation of their roles in EBV latency remains elusive. To identify EBER-specific gene expression features, we compared the proteome and mRNA transcriptome from BJAB cells (an EBV-negative B lymphoma cell line) stably transfected with an empty plasmid or with one carrying both EBER genes. We identified ~1800 proteins with at least 2 SILAC pair measurements, of which only 8 and 12 were up- and downregulated ≥ 2-fold, respectively. One upregulated protein was PIK3AP1, a B-cell specific protein adapter known to activate the PI3K-AKT signaling pathway, which regulates alternative splicing and translation in addition to its pro-survival effects. In the mRNA-seq data, the mRNA levels for some of the proteins changing in the SILAC data did not change. We instead observed isoform switch events. We validated the most relevant findings with biochemical assays. These corroborated the upregulation of PIK3AP1 and AKT activation in BJAB cells expressing high levels of both EBERs and EBNA1 (a surrogate of Burkitt’s lymphoma EBV latency I) relative to those expressing only EBNA1. The mRNA-seq data in these cells showed multiple upregulated oncogenes whose mRNAs are enriched for 3´-UTR AU-rich elements (AREs), such as ccl3, ccr7, il10, vegfa and zeb1. The CCL3, CCR7, IL10 and VEGFA proteins promote cell proliferation and are associated with EBV-mediated lymphomas. In EBV latency, ZEB1 represses the transcription of ZEBRA, an EBV lytic phase activation factor. We previously found that EBER1 interacts with AUF1 in vivo and proposed stabilization of ARE-containing mRNAs. Thus, the ~10⁶ copies of EBER1 may promote not only cell proliferation due to an increase in the levels of ARE-containing genes like ccl3, ccr7, il10, and vegfa, but also the maintenance of latency, through higher levels of zeb1.

Systems biology of diuretic resistance

Diuretics are commonly used to treat hypertension and extracellular fluid volume expansion. However, the development of compensatory responses in the kidney limits the benefit of this class of drugs. In this issue of the JCI, Grimm and colleagues use a systems biology approach in mice lacking the kinase SPAK and unravel a complex mechanism that explains thiazide diuretic resistance. The overall process involves interactions among six different cell types in the kidney.

A "proteomic ruler" for protein copy number and concentration estimation without spike-in standards

Absolute protein quantification using mass spectrometry (MS)-based proteomics delivers protein concentrations or copy numbers per cell. Existing methodologies typically require a combination of isotope-labeled spike-in references, cell counting, and protein concentration measurements. Here we present a novel method that delivers similar quantitative results directly from deep eukaryotic proteome datasets without any additional experimental steps. We show that the MS signal of histones can be used as a "proteomic ruler" because it is proportional to the amount of DNA in the sample, which in turn depends on the number of cells. As a result, our proteomic ruler approach adds an absolute scale to the MS readout and allows estimation of the copy numbers of individual proteins per cell. We compare our protein quantifications with values derived via the use of stable isotope labeling by amino acids in cell culture and protein epitope signature tags in a method that combines spike-in protein fragment standards with precise isotope label quantification. The proteomic ruler approach yields quantitative readouts that are in remarkably good agreement with results from the precision method. We attribute this surprising result to the fact that the proteomic ruler approach omits error-prone steps such as cell counting or protein concentration measurements. The proteomic ruler approach is readily applicable to any deep eukaryotic proteome dataset-even in retrospective analysis-and we demonstrate its usefulness with a series of mouse organ proteomes.

The proteomics quantification dilemma

Proteomics is dominated today by the protein expression discourse, which favorites the bottom-up approach because of its high throughput and its high sensitivity. For quantification this proceeding is misleading, if a protein is present with more than one protein species in the sample to be analyzed. The protein speciation discourse considers this more realistic situation and affords the top-down procedures or at least a separation of the protein species in advance to identification and quantification. Today all of the top-down procedures are one order of magnitude less sensitive than the bottom-up ones. To increase sensitivity and to increase throughput are major challenges for proteomics of the next years. This article is part of a Special Issue entitled: 20years of Proteomics in memory of Viatliano Pallini. Guest Editors: Luca Bini, Juan J. Calvete, Natacha Turck, Denis Hochstrasser and Jean-Charles Sanchez.

Cancer metabolism and mass spectrometry-based proteomics

Cancer metabolism has been extensively investigated by various tools, and the fact of diverse metabolic reprogramming in cancer cells has been gradually unveiled. In this review, we discuss some contributions in cancer metabolism by general proteomic analysis and post-translational modification analysis using mass spectrometry (MS) technique. Instead of following one or several metabolic enzymes/pathways, the current MS approach can quickly identify a large number of proteins and compare their expression levels in different samples, providing a potentially comprehensive picture of cancer metabolism. The MS analyses from pancreatic cancer cells support a hypothesis that hypoxia promotes cells in solid tumor to reprogram metabolic pathways in order to minimize the oxygen consumption. The oxidative stress in pancreatic cancer cells is lower than that in normal duct cells, and the cancer cells adaptively express less antioxidant proteins, contrary to claims that oxidative stress is higher in cancer cells. Separately, the MS analyses confirm that pyruvate kinase isoform 2 (PKM2) can be detected in both cancer and normal cells, disagreeing with report that tumor cells express exclusively PKM2. In addition, MS analyses from pancreatic cancer cells demonstrate that lactate dehydrogenase-B is significantly upregulated in pancreatic cancer cells, whereas previous reports show that lactate dehydrogenase-A is overexpressed and is responsible for lactate production in cancer cells. Lastly, the result from MS analysis suggests that the glutaminolysis in pancreatic cancer cells is different from that observed in glioblastoma cells.

Optimized nonlinear gradients for reversed-phase liquid chromatography in shotgun proteomics

Reversed-phase liquid chromatography has become the preferred method for separating peptides in most of the mass spectrometry-based proteomics workflows of today. In the way the technique is typically applied, the peptides are released from the chromatography column by the gradual addition of an organic buffer according to a linear function. However, when applied to complex peptide mixtures, this approach leads to unequal spreads of the peptides over the chromatography time. To address this, we investigated the use of nonlinear gradients, customized for each setup at hand. We developed an algorithm to generate optimized gradient functions for shotgun proteomics experiments and evaluated it for two data sets consisting each of four replicate runs of a human complex sample. Our results show that the optimized gradients produce a more even spread of the peptides over the chromatography run, while leading to increased numbers of confident peptide identifications. In addition, the list of peptides identified using nonlinear gradients differed considerably from those found with the linear ones, suggesting that such gradients can be a valuable tool for increasing the proteome coverage of mass spectrometry-based experiments.

Directed shotgun proteomics guided by saturated RNA-seq identifies a complete expressed prokaryotic proteome

Prokaryotes, due to their moderate complexity, are particularly amenable to the comprehensive identification of the protein repertoire expressed under different conditions. We applied a generic strategy to identify a complete expressed prokaryotic proteome, which is based on the analysis of RNA and proteins extracted from matched samples. Saturated transcriptome profiling by RNA-seq provided an endpoint estimate of the protein-coding genes expressed under two conditions which mimic the interaction of Bartonella henselae with its mammalian host. Directed shotgun proteomics experiments were carried out on four subcellular fractions. By specifically targeting proteins which are short, basic, low abundant, and membrane localized, we could eliminate their initial underrepresentation compared to the estimated endpoint. A total of 1250 proteins were identified with an estimated false discovery rate below 1%. This represents 85% of all distinct annotated proteins and ∼90% of the expressed protein-coding genes. Genes that were detected at the transcript but not protein level, were found to be highly enriched in several genomic islands. Furthermore, genes that lacked an ortholog and a functional annotation were not detected at the protein level; these may represent examples of overprediction in genome annotations. A dramatic membrane proteome reorganization was observed, including differential regulation of autotransporters, adhesins, and hemin binding proteins. Particularly noteworthy was the complete membrane proteome coverage, which included expression of all members of the VirB/D4 type IV secretion system, a key virulence factor.

Rapid and deep human proteome analysis by single-dimension shotgun proteomics

Multiparameter optimization of an LC-MS/MS shotgun proteomics experiment was performed without any hardware or software modification of the commercial instrument. Under the optimized experimental conditions, with a 50-cm-long separation column and a 4-h LC-MS run (including a 3-h optimized gradient), 4,825 protein groups and 37,550 peptides were identified in a single run and 5,354 protein groups and 56,390 peptides in a triplicate analysis of the A375 human cell line, for approximately 50% coverage of the expressed proteome. The major steps enabling such performance included optimization of the cell lysis and protein extraction, digestion of even insoluble cell debris, tailoring the LC gradient profile, and choosing the optimal dynamic exclusion window in data-dependent MS/MS, as well as the optimal m/z scan window.

The coming age of complete, accurate, and ubiquitous proteomes

High-resolution mass spectrometry (MS)-based proteomics has progressed tremendously over the years. For model organisms like yeast, we can now quantify complete proteomes in just a few hours. Developments discussed in this Perspective will soon enable complete proteome analysis of mammalian cells, as well, with profound impact on biology and biomedicine.

Data standardization and sharing-the work of the HUPO-PSI

Significant advances have been made over the past ten years to standardize the data emerging from the proteomic workflows adopted by laboratories all over the world. Differences in workflows, instrumentation, analysis software and reporting methods initially resulted in very disparate data being generated by many of these research groups, making data storage and comparison challenging. As the data standards proposed by the HUPO-PSI have increasingly been adopted, and tools and databases implementing these data formats have become more readily available, data generated by these complex experimental procedures is now becoming easier to manipulate, to visualize and to analyse. Public domain databases now exist to collate the information generated by experimentalists and to make the generation of specific protein expression maps, and monitoring of changes in protein expression levels in response to external stimuli a real possibility. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

Metabolomics as a tool to investigate abiotic stress tolerance in plants

Metabolites reflect the integration of gene expression, protein interaction and other different regulatory processes and are therefore closer to the phenotype than mRNA transcripts or proteins alone. Amongst all –omics technologies, metabolomics is the most transversal and can be applied to different organisms with little or no modifications. It has been successfully applied to the study of molecular phenotypes of plants in response to abiotic stress in order to find particular patterns associated to stress tolerance. These studies have highlighted the essential involvement of primary metabolites: sugars, amino acids and Krebs cycle intermediates as direct markers of photosynthetic dysfunction as well as effectors of osmotic readjustment. On the contrary, secondary metabolites are more specific of genera and species and respond to particular stress conditions as antioxidants, Reactive Oxygen Species (ROS) scavengers, coenzymes, UV and excess radiation screen and also as regulatory molecules. In addition, the induction of secondary metabolites by several abiotic stress conditions could also be an effective mechanism of cross-protection against biotic threats, providing a link between abiotic and biotic stress responses. Moreover, the presence/absence and relative accumulation of certain metabolites along with gene expression data provides accurate markers (mQTL or MWAS) for tolerant crop selection in breeding programs.

Quantitative analysis of fission yeast transcriptomes and proteomes in proliferating and quiescent cells

Data on absolute molecule numbers will empower the modeling, understanding, and comparison of cellular functions and biological systems. We quantified transcriptomes and proteomes in fission yeast during cellular proliferation and quiescence. This rich resource provides the first comprehensive reference for all RNA and most protein concentrations in a eukaryote under two key physiological conditions. The integrated data set supports quantitative biology and affords unique insights into cell regulation. Although mRNAs are typically expressed in a narrow range above 1 copy/cell, most long, noncoding RNAs, except for a distinct subset, are tightly repressed below 1 copy/cell. Cell-cycle-regulated transcription tunes mRNA numbers to phase-specific requirements but can also bring about more switch-like expression. Proteins greatly exceed mRNAs in abundance and dynamic range, and concentrations are regulated to functional demands. Upon transition to quiescence, the proteome changes substantially, but, in stark contrast to mRNAs, proteins do not uniformly decrease but scale with cell volume.

Identifying specific protein-DNA interactions using SILAC-based quantitative proteomics

A comprehensive identification of protein-DNA interactions that drive processes such as transcription and replication, both in prokaryotic and eukaryotic organisms, remains a major technical challenge. In this chapter, we present a SILAC-based DNA affinity purification method that can be used to identify specific interactions between proteins and functional DNA elements in an unbiased manner.