Generating and navigating proteome maps using mass spectrometry

The Contribution of Proteomics in Understanding Endometrial Protein Expression in Women with Recurrent Implantation Failure

Recurrent implantation failure (RIF) poses a significant challenge in assisted reproductive technology (ART) outcomes. The endometrium plays a crucial role in embryo implantation, and its protein expression profile is integral in determining receptivity. Proteomics has emerged as a valuable tool in unraveling the molecular intricacies underlying endometrial receptivity and RIF. The aim of the present review is to analyze the contribution of proteomics to the understanding of endometrial protein expression in women with RIF, based on the results of significant proteomic studies. Medline/Pubmed databases were searched using keywords pertaining to proteomics combined with terms related to RIF. 15 studies were included in the present review. Several proteins have been found to exbibit differential expression in endometrial biopsies and fluid samples between fertile women and women with RIF during the receptive endometrial phase. The profile of endometrial proteins varied significantly among the studies. Nevertheless, similar changes in the expression levels of annexin-6, progesterone receptor, MMP-2, and MMP-9 in the endometrium of women with RIF, were found in more than one study indicating that certain proteins could potentially be effective biomarkers of endometrial receptivity. Proteomics contributes significantly to the understanding of protein expression in the endometrium of women with RIF and the analysis of proteins in endometrial fluid are promising for improving the clinical management of RIF.

Characterization of the plasma proteome from healthy adult dogs

Introduction

Bloodwork is a widely used diagnostic tool in veterinary medicine, as diagnosis and therapeutic interventions often rely on blood biomarkers. However, biomarkers available in veterinary medicine often lack sensitivity or specificity. Mass spectrometry-based proteomics technology has been extensively used in the analysis of biological fluids. It offers excellent potential for a more comprehensive characterization of the plasma proteome in veterinary medicine.

Methods

In this study, we aimed to identify and quantify plasma proteins in a cohort of healthy dogs and compare two techniques for depleting high-abundance plasma proteins to enable the detection of lower-abundance proteins via label-free quantification liquid chromatography-mass spectrometry. We utilized surplus lithium-heparin plasma from 30 healthy dogs, subdivided into five groups of pooled plasma from 6 randomly selected individuals each. Firstly, we used a commercial kit to deplete high-abundance plasma proteins. Secondly, we employed an in-house method to remove albumin using Blue-Sepharose.

Results and discussion

Among all the samples, some of the most abundant proteins identified were apolipoprotein A and B, albumin, alpha-2-macroglobulin, fibrinogen beta chain, fibronectin, complement C3, serotransferrin, and coagulation factor V. However, neither of the depletion techniques achieved significant depletion of highly abundant proteins. Despite this limitation, we could detect and quantify many clinically relevant proteins. Determining the healthy canine proteome is a crucial first step in establishing a reference proteome for canine plasma. After enrichment, this reference proteome can later be utilized to identify protein markers associated with different diseases, thereby contributing to the diagnosis and prognosis of various pathologies.

Molecular Breeding and Drought Tolerance in Chickpea

Cicer arietinum L. is the third greatest widely planted imperative pulse crop worldwide, and it belongs to the Leguminosae family. Drought is the utmost common abiotic factor on plants, distressing their water status and limiting their growth and development. Chickpea genotypes have the natural ability to fight drought stress using certain strategies viz., escape, avoidance and tolerance. Assorted breeding methods, including hybridization, mutation, and marker-aided breeding, genome sequencing along with omics approaches, could be used to improve the chickpea germplasm lines(s) against drought stress. Root features, for instance depth and root biomass, have been recognized as the greatest beneficial morphological factors for managing terminal drought tolerance in the chickpea. Marker-aided selection, for example, is a genomics-assisted breeding (GAB) strategy that can considerably increase crop breeding accuracy and competence. These breeding technologies, notably marker-assisted breeding, omics, and plant physiology knowledge, underlined the importance of chickpea breeding and can be used in future crop improvement programmes to generate drought-tolerant cultivars(s).

Assessing normalization methods in mass spectrometry-based proteome profiling of clinical samples

BACKGROUND

Large-scale proteomic studies have to deal with unwanted variability, especially when samples originate from different centers and multiple analytical batches are needed. Such variability is typically added throughout all the steps of a clinical research study, from human biological sample collection and storage, sample preparation, spectral data acquisition, to peptide and protein quantification. In order to remove such diverse and unwanted variability, normalization of the protein data is performed. There have been already several published reviews comparing normalization methods in the -omics field, but reports focusing on proteomic data generated with mass spectrometry (MS) are much fewer. Additionally, most of these reports have only dealt with small datasets.

RESULTS

As a case study, here we focused on the normalization of a large MS-based proteomic dataset obtained from an overweight and obese pan-European cohort, where different normalization methods were evaluated, namely: center standardize, quantile protein, quantile sample, global standardization, ComBat, median centering, mean centering, single standard and removal of unwanted variation (RUV); some of these are generic normalization methods while others have been specifically created to deal with genomic or metabolomic data. We checked how relationships between proteins and clinical variables (e.g., gender, levels of triglycerides or cholesterol) were improved after normalizing the data with the different methods.

CONCLUSIONS

Some normalization methods were better adapted for this particular large-scale shotgun proteomic dataset of human plasma samples labeled with isobaric tags and analyzed with liquid chromatography-tandem MS. In particular, quantile sample normalization, RUV, mean and median centering showed very good performance, while quantile protein normalization provided worse results than those obtained with unnormalized data.

Proteomic characterization of serum proteins from Atlantic salmon (Salmo salar L.) from an outbreak with cardiomyopathy syndrome

Cardiomyopathy syndrome (CMS), caused by piscine myocarditis virus (PMCV), is a serious challenge to Atlantic salmon (Salmo salar L.) aquaculture. Regrettably, husbandry techniques are the only tool to manage CMS outbreaks, and no prophylactic measures are available at present. Early diagnosis of CMS is therefore desirable, preferably with non-lethal diagnostic methods, such as serum biomarkers. To identify candidate biomarkers for CMS, the protein content of pools of sera (4 fish/pool) from salmon with a CMS outbreak (3 pools) and from clinically healthy salmon (3 pools) was compared using liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS). Overall, seven proteins were uniquely identified in the sera of clinically healthy fish, while 27 proteins were unique to the sera of CMS fish. Of the latter, 24 have been associated with cardiac disease in humans. These were grouped as leakage enzymes (creatine kinase, lactate dehydrogenase, glycogen phosphorylase and carbonic anhydrase); host reaction proteins (acute-phase response proteins-haptoglobin, fibrinogen, α2-macroglobulin and ceruloplasmin; and complement-related proteins); and regeneration/remodelling proteins (fibronectin, lumican and retinol). Clinical evaluation of the suitability of these proteins as biomarkers of CMS, either individually or as part of a panel, is a logical next step for the development of early diagnostic tools for CMS.

Pyroptosis inhibition improves the symptom of acute myocardial infarction

Acute myocardial infarction (AMI), the leading cause of mortality worldwide, is a rapidly developing and irreversible disease. Therefore, proper prompt intervention at the early stage of AMI is crucial for its treatment. However, the molecular features in the early stage have not been clarified. Here, we constructed mouse AMI model and profiled transcriptomes and proteomes at the early stages of AMI progress. Immune system was extensively activated at 6-h AMI. Then, pyroptosis was activated at 24-h AMI. VX-765 treatment, a pyroptosis inhibitor, significantly reduced the infarct size and improved the function of cardiomyocytes. Besides, we identified that WIPI1, specifically expressed in heart, was significantly upregulated at 1 h after AMI. Moreover, WIPI1 expression is significantly higher in the peripheral blood of patients with AMI than healthy control. WIPI1 can serve as a potential early diagnostic biomarker for AMI. It likely decelerates AMI progress by activating autophagy pathways. These findings shed new light on gene expression dynamics in AMI progress, and present a potential early diagnostic marker and a candidate drug for clinical pre-treatment to prolong the optimal cure time.

Harnessing the Microbiomes of Suppressive Composts for Plant Protection: From Metagenomes to Beneficial Microorganisms and Reliable Diagnostics

Soil-borne diseases cause significant yield losses worldwide, are difficult to treat and often only limited options for disease management are available. It has long been known that compost amendments, which are routinely applied in organic and integrated farming as a part of good agricultural practice to close nutrient cycles, can convey a protective effect. Yet, the targeted use of composts against soil-borne diseases is hampered by the unpredictability of the efficacy. Several studies have identified and/or isolated beneficial microorganisms (i.e., bacteria, oomycetes, and fungi) from disease suppressive composts capable of suppressing pathogens (e.g., Pythium and Fusarium) in various crops (e.g., tomato, lettuce, and cucumber), and some of them have been developed into commercial products. Yet, there is growing evidence that synthetic or complex microbial consortia can be more effective in controlling diseases than single strains, but the underlying molecular mechanisms are poorly understood. Currently, a major bottleneck concerns the lack of functional assays to identify the most potent beneficial microorganisms and/or key microbial consortia from complex soil and compost microbiomes, which can harbor tens of thousands of species. This focused review describes microorganisms, which have been isolated from, amended to or found to be abundant in disease-suppressive composts and for which a beneficial effect has been documented. We point out opportunities to increasingly harness compost microbiomes for plant protection through an integrated systems approach that combines the power of functional assays to isolate biocontrol and plant growth promoting strains and further prioritize them, with functional genomics approaches that have been successfully applied in other fields of microbiome research. These include detailed metagenomics studies (i.e., amplicon and shotgun sequencing) to achieve a better understanding of the complex system compost and to identify members of taxa enriched in suppressive composts. Whole-genome sequencing and complete assembly of key isolates and their subsequent functional profiling can elucidate the mechanisms of action of biocontrol strains. Integrating the benefits of these approaches will bring the long-term goals of employing microorganisms for a sustainable control of plant pathogens and developing reliable diagnostic assays to assess the suppressiveness of composts within reach.

A Proteogenomic Resource Enabling Integrated Analysis of Listeria Genotype-Proteotype-Phenotype Relationships

Listeria monocytogenes is an opportunistic foodborne pathogen responsible for listeriosis, a potentially fatal foodborne disease. Many different Listeria strains and serotypes exist, but a proteogenomic resource that bridges the gap in our molecular understanding of the relationships between the Listeria genotypes and phenotypes via proteotypes is still missing. Here, we devised a next-generation proteogenomics strategy that enables the community to rapidly proteotype Listeria strains and relate this information back to the genotype. Based on sequencing and de novo assembly of the two most commonly used Listeria model strains, EGD-e and ScottA, we established two comprehensive Listeria proteogenomic databases. A genome comparison established core- and strain-specific genes potentially responsible for virulence differences. Next, we established a DIA/SWATH-based proteotyping strategy, including a new and robust sample preparation workflow, that enables the reproducible, sensitive, and relative quantitative measurement of Listeria proteotypes. This reusable and publicly available DIA/SWATH library covers 70% of open reading frames of Listeria and represents the most extensive spectral library for Listeria proteotype analysis to date. We used these two new resources to investigate the Listeria proteotype in states mimicking the upper gastrointestinal passage. Exposure of Listeria to bile salts at 37 °C, which simulates conditions encountered in the duodenum, showed significant proteotype perturbations including an increase of FlaA, the structural protein of flagella. Given that Listeria is known to lose its flagella above 30 °C, this was an unexpected finding. The formation of flagella, which might have implications on infectivity, was validated by parallel reaction monitoring and light and scanning electron microscopy. flaA transcript levels did not change significantly upon exposure to bile salts at 37 °C, suggesting regulation at the post-transcriptional level. Together, these analyses provide a comprehensive proteogenomic resource and toolbox for the Listeria community enabling the analysis of Listeria genotype-proteotype-phenotype relationships.

The Human Immunopeptidome Project: A Roadmap to Predict and Treat Immune Diseases

The science that investigates the ensembles of all peptides associated to human leukocyte antigen (HLA) molecules is termed "immunopeptidomics" and is typically driven by mass spectrometry (MS) technologies. Recent advances in MS technologies, neoantigen discovery and cancer immunotherapy have catalyzed the launch of the Human Immunopeptidome Project (HIPP) with the goal of providing a complete map of the human immunopeptidome and making the technology so robust that it will be available in every clinic. Here, we provide a long-term perspective of the field and we use this framework to explore how we think the completion of the HIPP will truly impact the society in the future. In this context, we introduce the concept of immunopeptidome-wide association studies (IWAS). We highlight the importance of large cohort studies for the future and how applying quantitative immunopeptidomics at population scale may provide a new look at individual predisposition to common immune diseases as well as responsiveness to vaccines and immunotherapies. Through this vision, we aim to provide a fresh view of the field to stimulate new discussions within the community, and present what we see as the key challenges for the future for unlocking the full potential of immunopeptidomics in this era of precision medicine.

Microarray and Mass Spectrometry-Based Methodology for Lipid Profiling of Tissues and Cell Cultures

The recent developments in mass spectrometry have revealed the importance of lipids as biomarkers in the context of different diseases and as indicators of the cell's homeostasis. However, further advances are required to unveil the complex relationships between lipid classes and lipid species with proteins. Here, we present a new methodology that combines microarrays with mass spectrometry to obtain the lipid fingerprint of samples of a different nature in a standardized and fast way, with minimal sample consumption. As a proof of concept, we use the methodology to obtain the lipid fingerprint of 20 rat tissues and to create a lipid library for tissue classification. Then, we combine those results with immunohistochemistry and enzymatic assays to unveil the relationship between some lipid species and two enzymes. Finally, we demonstrate the performance of the methodology to explore changes in lipid composition of the nucleus accumbens from mice subjected to two lipid diets.

The Densitometric Analysis of Protein Pattern in Cleft Lip and Palate Patients

Objectives

Cleft lip and palate (CLP) belongs to the congenital anomaly that is clinically seen as cleft in lip, alveolar bone, palate, and nasal septum. The patients suffer from esthetic and various functional defects. CLP is resulted from impaired palatogenesis during the embryonic phase. The etiology of CLP is influenced by genetic, environmental, and combination of both. According to the literature, CLP is highly associated with defect in interferon regulatory factor 6 (IRF6) and poliovirus receptor-like (PVRL1) genes. The present study aimed to investigate the total protein profile and to identify protein IRF6 and PVRL1 in plasma of CLP patients.

Materials and Methods

Dot-Blot analysis was performed to identify protein target of IRF6 and PVRL1. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis was performed in gel concentration 12% using plasma of CLP patients, their parents, and control population. The gels were stained by Coomassie blue afterward. Gels were analyzed through ImageLab 5.2.1 software.

Results

The intensity of major bands in CLP patients was darker than control group, but remains similar to the parents group. The target protein IRF6 and PVRL1 were positively identified through Dot-Blot. Retardation factor value was significantly different in major bands of CLP patients compared to control group.

Conclusion

There pattern of protein profile in CLP patients was different compared to non-CLP.

An Integrated Systems Approach Unveils New Aspects of Microoxia-Mediated Regulation in Bradyrhizobium diazoefficiens

The adaptation of rhizobia from the free-living state in soil to the endosymbiotic state comprises several physiological changes in order to cope with the extremely low oxygen availability (microoxia) within nodules. To uncover cellular functions required for bacterial adaptation to microoxia directly at the protein level, we applied a systems biology approach on the key rhizobial model and soybean endosymbiont Bradyrhizobium diazoefficiens USDA 110 (formerly B. japonicum USDA 110). As a first step, the complete genome of B. diazoefficiens 110spc4, the model strain used in most prior functional genomics studies, was sequenced revealing a deletion of a ~202 kb fragment harboring 223 genes and several additional differences, compared to strain USDA 110. Importantly, the deletion strain showed no significantly different phenotype during symbiosis with several host plants, reinforcing the value of previous OMICS studies. We next performed shotgun proteomics and detected 2,900 and 2,826 proteins in oxically and microoxically grown cells, respectively, largely expanding our knowledge about the inventory of rhizobial proteins expressed in microoxia. A set of 62 proteins was significantly induced under microoxic conditions, including the two nitrogenase subunits NifDK, the nitrogenase reductase NifH, and several subunits of the high-affinity terminal cbb₃ oxidase (FixNOQP) required for bacterial respiration inside nodules. Integration with the previously defined microoxia-induced transcriptome uncovered a set of 639 genes or proteins uniquely expressed in microoxia. Finally, besides providing proteogenomic evidence for novelties, we also identified proteins with a regulation similar to that of FixK₂: transcript levels of these protein-coding genes were significantly induced, while the corresponding protein abundance remained unchanged or was down-regulated. This suggested that, apart from fixK₂, additional B. diazoefficiens genes might be under microoxia-specific post-transcriptional control. This hypothesis was indeed confirmed for several targets (HemA, HemB, and ClpA) by immunoblot analysis.

Proteomic Analysis of Horseweed (Conyza canadensis) Subjected to Caprylic Acid Stress

Caprylic acid (CAP) is anticipated to be a potential biocontrol herbicide in the control of weeds, however the molecular mechanism of how CAP affects weeds is poorly understood. Here, the physiological and biochemical (protein-level) changes in horseweed (Conyza canadensis L.) are studied under CAP treatment, with infrared gas analyzer and label-free quantitative proteomics methods. In total, 112 differentially-accumulated proteins (DAPs) (>1.5 fold change, p < 0.05) are present between treated horseweed and control samples, with 46 up-regulated and 66 down-regulated proteins. These DAPs are involved in 28 biochemical pathways, including photosynthesis pathways. In particular, six photosynthesis proteins show significant abundance changes in the CAP-treated horseweed. The qRT-PCR results confirm three of the six genes involved in photosynthesis. Moreover, by measuring photosynthesis characteristics, CAP was shown to decrease photosynthetic rate, stomatal conductance, intercellular CO₂ concentration, and the transpiration rate of horseweed. These results suggest that photosystem I is one of the main biological processes involved in the response of horseweed to CAP.

Complex-centric proteome profiling by SEC-SWATH-MS

Proteins are major effectors and regulators of biological processes that can elicit multiple functions depending on their interaction with other proteins. The organization of proteins into macromolecular complexes and their quantitative distribution across these complexes is, therefore, of great biological and clinical significance. In this paper, we describe an integrated experimental and computational technique to quantify hundreds of protein complexes in a single operation. The method consists of size exclusion chromatography (SEC) to fractionate native protein complexes, SWATH/DIA mass spectrometry to precisely quantify the proteins in each SEC fraction, and the computational framework CCprofiler to detect and quantify protein complexes by error‐controlled, complex‐centric analysis using prior information from generic protein interaction maps. Our analysis of the HEK293 cell line proteome delineates 462 complexes composed of 2,127 protein subunits. The technique identifies novel sub‐complexes and assembly intermediates of central regulatory complexes while assessing the quantitative subunit distribution across them. We make the toolset CCprofiler freely accessible and provide a web platform, SECexplorer, for custom exploration of the HEK293 proteome modularity.

The Human Brain Proteome Project: Biological and Technological Challenges

Brain proteomics has become a method of choice that allows zooming-in where neuropathophysiological alterations are taking place, detecting protein mediators that might eventually be measured in cerebrospinal fluid (CSF) as potential neuropathologically derived biomarkers. Following this hypothesis, mass spectrometry-based neuroproteomics has emerged as a powerful approach to profile neural proteomes derived from brain structures and CSF in order to map the extensive protein catalog of the human brain. This chapter provides a historical perspective on the Human Brain Proteome Project (HBPP), some recommendation to the experimental design in neuroproteomic projects, and a brief description of relevant technological and computational innovations that are emerging in the neurobiology field thanks to the proteomics community. Importantly, this chapter highlights recent discoveries from the biology- and disease-oriented branch of the HBPP (B/D-HBPP) focused on spatiotemporal proteomic characterizations of mouse models of neurodegenerative diseases, elucidation of proteostatic networks in different types of dementia, the characterization of unresolved clinical phenotypes, and the discovery of novel biomarker candidates in CSF.

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.

Urinary Glycopeptide Analysis for the Investigation of Novel Biomarkers

PURPOSE

Urine is a rich source of potential biomarkers, including glycoproteins. Glycoproteomic analysis remains difficult due to the high heterogeneity of glycans. Nevertheless, recent advances in glycoproteomics software solutions facilitate glycopeptide identification and characterization. The aim is to investigate intact glycopeptides in the urinary peptide profiles of normal subjects using a novel PTM-centric software-Byonic.

EXPERIMENTAL DESIGN

The urinary peptide profiles of 238 normal subjects, previously analyzed using CE-MS and CE-MS/MS and/or LC-MS/MS, are subjected to glycopeptide analysis. Additionally, glycopeptide distribution is assessed in a set of 969 patients with five different cancer types: bladder, prostate and pancreatic cancer, cholangiocarcinoma, and renal cell carcinoma.

RESULTS

A total of 37 intact O-glycopeptides and 23 intact N-glycopeptides are identified in the urinary profiles of 238 normal subjects. Among the most commonly identified O-glycoproteins are Apolipoprotein C-III and insulin-like growth factor II, while titin among the N-glycoproteins. Further statistical analysis reveals that three O-glycopeptides and five N-glycopeptides differed significantly in their abundance among the different cancer types, comparing to normal subjects.

CONCLUSIONS AND CLINICAL RELEVANCE

Through the established glycoproteomics workflow, intact O- and N-glycopeptides in human urine are identified and characterized, providing novel insights for further exploration of the glycoproteome with respect to specific diseases.

The implementation of omics technologies in cancer microbiome research

Whilst the interplay between host genetics and the environment plays a pivotal role in the aetiopathogenesis of cancer, there are other key contributors of importance as well. One such factor of central and growing interest is the contribution of the microbiota to cancer. Even though the field is only a few years old, investigation of the 'cancer microbiome' has already led to major advances in knowledge of the basic biology of cancer risk and progression, opened novel avenues for biomarkers and diagnostics, and given a better understanding of mechanisms underlying response to therapy. Recent developments in microbial DNA sequencing techniques (and the bioinformatics required for analysis of these datasets) have allowed much more in-depth profiling of the structure of microbial communities than was previously possible. However, for more complete assessment of the functional implications of microbial changes, there is a growing recognition of the importance of the integration of microbial profiling with other omics modalities, with metabonomics (metabolite profiling) and proteomics (protein profiling) both gaining particular recent attention. In this review, we give an overview of some of the key scientific techniques being used to unravel the role of the cancer microbiome. We have aimed to highlight practical aspects related to sample collection and preparation, choice of the modality of analysis, and examples of where different omics technologies have been complementary to each other to highlight the significance of the cancer microbiome.

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.

Rapid Screening of Ellagitannins in Natural Sources via Targeted Reporter Ion Triggered Tandem Mass Spectrometry

Complex biomolecules present in their natural sources have been difficult to analyze using traditional analytical approaches. Ultrahigh-performance liquid chromatography (UHPLC-MS/MS) methods have the potential to enhance the discovery of a less well characterized and challenging class of biomolecules in plants, the ellagitannins. We present an approach that allows for the screening of ellagitannins by employing higher energy collision dissociation (HCD) to generate reporter ions for classification and collision-induced dissociation (CID) to generate unique fragmentation spectra for isomeric variants of previously unreported species. Ellagitannin anions efficiently form three characteristic reporter ions after HCD fragmentation that allows for the classification of unknown precursors that we call targeted reporter ion triggering (TRT). We demonstrate how a tandem HCD-CID experiment might be used to screen natural sources using UHPLC-MS/MS by application of 22 method conditions from which an optimized data-dependent acquisition (DDA) emerged. The method was verified not to yield false-positive results in complex plant matrices. We were able to identify 154 non-isomeric ellagitannins from strawberry leaves, which is 17 times higher than previously reported in the same matrix. The systematic inclusion of CID spectra for isomers of each species classified as an ellagitannin has never been possible before the development of this approach.

Functional Genomics Approaches to Studying Symbioses between Legumes and Nitrogen-Fixing Rhizobia

Biological nitrogen fixation gives legumes a pronounced growth advantage in nitrogen-deprived soils and is of considerable ecological and economic interest. In exchange for reduced atmospheric nitrogen, typically given to the plant in the form of amides or ureides, the legume provides nitrogen-fixing rhizobia with nutrients and highly specialised root structures called nodules. To elucidate the molecular basis underlying physiological adaptations on a genome-wide scale, functional genomics approaches, such as transcriptomics, proteomics, and metabolomics, have been used. This review presents an overview of the different functional genomics approaches that have been performed on rhizobial symbiosis, with a focus on studies investigating the molecular mechanisms used by the bacterial partner to interact with the legume. While rhizobia belonging to the alpha-proteobacterial group (alpha-rhizobia) have been well studied, few studies to date have investigated this process in beta-proteobacteria (beta-rhizobia).

Application of targeted mass spectrometry in bottom-up proteomics for systems biology research

The enormous diversity of proteoforms produces tremendous complexity within cellular proteomes, facilitates intricate networks of molecular interactions, and constitutes a formidable analytical challenge for biomedical researchers. Currently, quantitative whole-proteome profiling often relies on non-targeted liquid chromatography-mass spectrometry (LC-MS), which samples proteoforms broadly, but can suffer from lower accuracy, sensitivity, and reproducibility compared with targeted LC-MS. Recent advances in bottom-up proteomics using targeted LC-MS have enabled previously unachievable identification and quantification of target proteins and posttranslational modifications within complex samples. Consequently, targeted LC-MS is rapidly advancing biomedical research, especially systems biology research in diverse areas that include proteogenomics, interactomics, kinomics, and biological pathway modeling. With the recent development of targeted LC-MS assays for nearly the entire human proteome, targeted LC-MS is positioned to enable quantitative proteomic profiling of unprecedented quality and accessibility to support fundamental and clinical research. Here we review recent applications of bottom-up proteomics using targeted LC-MS for systems biology research. SIGNIFICANCE: Advances in targeted proteomics are rapidly advancing systems biology research. Recent applications include systems-level investigations focused on posttranslational modifications (such as phosphoproteomics), protein conformation, protein-protein interaction, kinomics, proteogenomics, and metabolic and signaling pathways. Notably, absolute quantification of metabolic and signaling pathway proteins has enabled accurate pathway modeling and engineering. Integration of targeted proteomics with other technologies, such as RNA-seq, has facilitated diverse research such as the identification of hundreds of "missing" human proteins (genes and transcripts that appear to encode proteins but direct experimental evidence was lacking).

Comparative Genomics of Completely Sequenced Lactobacillus helveticus Genomes Provides Insights into Strain-Specific Genes and Resolves Metagenomics Data Down to the Strain Level

Although complete genome sequences hold particular value for an accurate description of core genomes, the identification of strain-specific genes, and as the optimal basis for functional genomics studies, they are still largely underrepresented in public repositories. Based on an assessment of the genome assembly complexity for all lactobacilli, we used Pacific Biosciences' long read technology to sequence and de novo assemble the genomes of three Lactobacillus helveticus starter strains, raising the number of completely sequenced strains to 12. The first comparative genomics study for L. helveticus—to our knowledge—identified a core genome of 988 genes and sets of unique, strain-specific genes ranging from about 30 to more than 200 genes. Importantly, the comparison of MiSeq- and PacBio-based assemblies uncovered that not only accessory but also core genes can be missed in incomplete genome assemblies based on short reads. Analysis of the three genomes revealed that a large number of pseudogenes were enriched for functional Gene Ontology categories such as amino acid transmembrane transport and carbohydrate metabolism, which is in line with a reductive genome evolution in the rich natural habitat of L. helveticus. Notably, the functional Clusters of Orthologous Groups of proteins categories “cell wall/membrane biogenesis” and “defense mechanisms” were found to be enriched among the strain-specific genes. A genome mining effort uncovered examples where an experimentally observed phenotype could be linked to the underlying genotype, such as for cell envelope proteinase PrtH3 of strain FAM8627. Another possible link identified for peptidoglycan hydrolases will require further experiments. Of note, strain FAM22155 did not harbor a CRISPR/Cas system; its loss was also observed in other L. helveticus strains and lactobacillus species, thus questioning the value of the CRISPR/Cas system for diagnostic purposes. Importantly, the complete genome sequences proved to be very useful for the analysis of natural whey starter cultures with metagenomics, as a larger percentage of the sequenced reads of these complex mixtures could be unambiguously assigned down to the strain level.

Identification of AHL- and BDSF-Controlled Proteins in Burkholderia cenocepacia by Proteomics

We used comparative proteome analysis to determine the target genes of the two quorum sensing (QS) circuits in the opportunistic pathogen Burkholderia cenocepacia: the N-acyl homoserine lactone (AHL)-based CepIR system and the BDSF (B urkholderia diffusible signal factor, cis-2-dodecenoic acid)-based RpfFR system. In this book chapter, we focus on the description of the practical procedure we currently use in the laboratory to perform a sensitive GeLC-MS/MS shotgun proteomics experiment; we also briefly describe the downstream bioinformatic data analysis.

An integrative strategy to identify the entire protein coding potential of prokaryotic genomes by proteogenomics

Accurate annotation of all protein-coding sequences (CDSs) is an essential prerequisite to fully exploit the rapidly growing repertoire of completely sequenced prokaryotic genomes. However, large discrepancies among the number of CDSs annotated by different resources, missed functional short open reading frames (sORFs), and overprediction of spurious ORFs represent serious limitations. Our strategy toward accurate and complete genome annotation consolidates CDSs from multiple reference annotation resources, ab initio gene prediction algorithms and in silico ORFs (a modified six-frame translation considering alternative start codons) in an integrated proteogenomics database (iPtgxDB) that covers the entire protein-coding potential of a prokaryotic genome. By extending the PeptideClassifier concept of unambiguous peptides for prokaryotes, close to 95% of the identifiable peptides imply one distinct protein, largely simplifying downstream analysis. Searching a comprehensive Bartonella henselae proteomics data set against such an iPtgxDB allowed us to unambiguously identify novel ORFs uniquely predicted by each resource, including lipoproteins, differentially expressed and membrane-localized proteins, novel start sites and wrongly annotated pseudogenes. Most novelties were confirmed by targeted, parallel reaction monitoring mass spectrometry, including unique ORFs and single amino acid variations (SAAVs) identified in a re-sequenced laboratory strain that are not present in its reference genome. We demonstrate the general applicability of our strategy for genomes with varying GC content and distinct taxonomic origin. We release iPtgxDBs for B. henselae, Bradyrhizobium diazoefficiens and Escherichia coli and the software to generate both proteogenomics search databases and integrated annotation files that can be viewed in a genome browser for any prokaryote.

Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses

Liquid chromatography coupled to tandem mass spectrometry is the main method for high-throughput identification and quantification of peptides and inferred proteins. Within this field, data-independent acquisition (DIA) combined with peptide-centric scoring, exemplified by SWATH-MS, emerged as a scalable method to achieve deep and consistent proteome coverage across large-scale datasets. Here we discuss the adaptation of statistical concepts developed for discovery proteomics based on spectrum-centric scoring to large-scale DIA experiments analyzed with peptide-centric scoring strategies and provide guidance on their application. We show that optimal tradeoffs between sensitivity and specificity require careful considerations of the relationship between proteins in the samples and proteins represented in the spectral library. We propose the application of a global analyte constraint to prevent accumulation of false positives across large-scale datasets. Furthermore, to increase the quality and reproducibility of published proteomic results, well-established confidence criteria should be reported for detected peptide queries, peptides and inferred proteins.

The Use of Proteomics in Assisted Reproduction

Despite the explosive increase in the use of Assisted Reproductive Technologies (ART) over the last 30 years, their success rates remain suboptimal. Proteomics is a rapidly-evolving technology-driven science that has already been widely applied in the exploration of human reproduction and fertility, providing useful insights into its physiology and leading to the identification of numerous proteins that may be potential biomarkers and/or treatment targets of a successful ART pregnancy. Here we present a brief overview of the techniques used in proteomic analyses and attempt a comprehensive presentation of recent data from mass spectrometry-based proteomic studies in humans, regarding all components of ARTs, including the male and female gamete, the derived zygote and embryo, the endometrium and, finally, the ART offspring both pre- and postnatally.

Proteogenomic studies on cancer drug resistance: towards biomarker discovery and target identification

INTRODUCTION

Chemoresistance is a major obstacle for current cancer treatment. Proteogenomics is a powerful multi-omics research field that uses customized protein sequence databases generated by genomic and transcriptomic information to identify novel genes (e.g. noncoding, mutation and fusion genes) from mass spectrometry-based proteomic data. By identifying aberrations that are differentially expressed between tumor and normal pairs, this approach can also be applied to validate protein variants in cancer, which may reveal the response to drug treatment. Areas covered: In this review, we will present recent advances in proteogenomic investigations of cancer drug resistance with an emphasis on integrative proteogenomic pipelines and the biomarker discovery which contributes to achieving the goal of using precision/personalized medicine for cancer treatment. Expert commentary: The discovery and comprehensive understanding of potential biomarkers help identify the cohort of patients who may benefit from particular treatments, and will assist real-time clinical decision-making to maximize therapeutic efficacy and minimize adverse effects. With the development of MS-based proteomics and NGS-based sequencing, a growing number of proteogenomic tools are being developed specifically to investigate cancer drug resistance.

Optimizing Analytical Depth and Cost Efficiency of IEF-LC/MS Proteomics

IEF LC-MS/MS is an analytical method that incorporates a two-step sample separation prior to MS identification of proteins. When analyzing complex samples this preparatory separation allows for higher analytical depth and improved quantification accuracy of proteins. However, cost and analysis time are greatly increased as each analyzed IEF fraction is separately profiled using LC-MS/MS. We propose an approach that selects a subset of IEF fractions for LC-MS/MS analysis that is highly informative in the context of a group of proteins of interest. Specifically, our method allows a significant reduction in cost and instrument time as compared to the standard protocol of running all fractions, with little compromise to coverage. We develop algorithmics to optimize the selection of the IEF fractions on which to run LC-MS/MS. We translate the fraction optimization task to Minimum Set Cover, a well-studied NP-hard problem. We develop heuristic solutions and compare them in terms of effectiveness and running times. We provide examples to demonstrate advantages and limitations of each algorithmic approach. Finally, we test our methodology by applying it to experimental data obtained from IEF LC-MS/MS analysis of yeast and human samples. We demonstrate the benefit of this approach for analyzing complex samples with a focus on different protein sets of interest.

Building ProteomeTools based on a complete synthetic human proteome

We describe ProteomeTools, a project building molecular and digital tools from the human proteome to facilitate biomedical research. Here we report the generation and multimodal liquid chromatography-tandem mass spectrometry analysis of >330,000 synthetic tryptic peptides representing essentially all canonical human gene products, and we exemplify the utility of these data in several applications. The resource (available at http://www.proteometools.org) will be extended to >1 million peptides, and all data will be shared with the community via ProteomicsDB and ProteomeXchange.

The Exposed Proteomes of Brachyspira hyodysenteriae and B. pilosicoli

Brachyspira hyodysenteriae and Brachyspira pilosicoli are well-known intestinal pathogens in pigs. B. hyodysenteriae is the causative agent of swine dysentery, a disease with an important impact on pig production while B. pilosicoli is responsible of a milder diarrheal disease in these animals, porcine intestinal spirochetosis. Recent sequencing projects have provided information for the genome of these species facilitating the search of vaccine candidates using reverse vaccinology approaches. However, practically no experimental evidence exists of the actual gene products being expressed and of those proteins exposed on the cell surface or released to the cell media. Using a cell-shaving strategy and a shotgun proteomic approach we carried out a large-scale characterization of the exposed proteins on the bacterial surface in these species as well as of peptides and proteins in the extracellular medium. The study included three strains of B. hyodysenteriae and two strains of B. pilosicoli and involved 148 LC-MS/MS runs on a high resolution Orbitrap instrument. Overall, we provided evidence for more than 29,000 different peptides pointing to 1625 and 1338 different proteins in B. hyodysenteriae and B. pilosicoli, respectively. Many of the most abundant proteins detected corresponded to described virulence factors and vaccine candidates. The level of expression of these proteins, however, was different among species and strains, stressing the value of determining actual gene product levels as a complement of genomic-based approaches for vaccine design.

Genome-wide transcription start site mapping of Bradyrhizobium japonicum grown free-living or in symbiosis - a rich resource to identify new transcripts, proteins and to study gene regulation

BACKGROUND

Differential RNA-sequencing (dRNA-seq) is indispensable for determination of primary transcriptomes. However, using dRNA-seq data to map transcriptional start sites (TSSs) and promoters genome-wide is a bioinformatics challenge. We performed dRNA-seq of Bradyrhizobium japonicum USDA 110, the nitrogen-fixing symbiont of soybean, and developed algorithms to map TSSs and promoters.

RESULTS

A specialized machine learning procedure for TSS recognition allowed us to map 15,923 TSSs: 14,360 in free-living bacteria, 4329 in symbiosis with soybean and 2766 in both conditions. Further, we provide proteomic evidence for 4090 proteins, among them 107 proteins corresponding to new genes and 178 proteins with N-termini different from the existing annotation (72 and 109 of them with TSS support, respectively). Guided by proteomics evidence, previously identified TSSs and TSSs experimentally validated here, we assign a score threshold to flag 14 % of the mapped TSSs as a class of lower confidence. However, this class of lower confidence contains valid TSSs of low-abundant transcripts. Moreover, we developed a de novo algorithm to identify promoter motifs upstream of mapped TSSs, which is publicly available, and found motifs mainly used in symbiosis (similar to RpoN-dependent promoters) or under both conditions (similar to RpoD-dependent promoters). Mapped TSSs and putative promoters, proteomic evidence and updated gene annotation were combined into an annotation file.

CONCLUSIONS

The genome-wide TSS and promoter maps along with the extended genome annotation of B. japonicum represent a valuable resource for future systems biology studies and for detailed analyses of individual non-coding transcripts and ORFs. Our data will also provide new insights into bacterial gene regulation during the agriculturally important symbiosis between rhizobia and legumes.

SORLA regulates calpain-dependent degradation of synapsin

INTRODUCTION

Sorting-related receptor with A-type repeats (SORLA) is an intracellular sorting receptor in neurons and a major risk factor for Alzheimer disease.

METHODS

Here, we performed global proteome analyses in the brain of SORLA-deficient mice followed by biochemical and histopathologic studies to identify novel neuronal pathways affected by receptor dysfunction.

RESULTS

We demonstrate that the lack of SORLA results in accumulation of phosphorylated synapsins in cortex and hippocampus. We propose an underlying molecular mechanism by demonstrating that SORLA interacts with phosphorylated synapsins through 14-3-3 adaptor proteins to deliver synapsins to calpain-mediated proteolytic degradation.

DISCUSSION

Our results suggest a novel function for SORLA which is in control of synapsin degradation, potentially impacting on synaptic vesicle endocytosis and/or exocytosis.

The bovine milk microbiota: insights and perspectives from -omics studies

Recent findings and future perspectives of -omics studies on the bovine milk microbiota, focusing on its impact on animal health.

Preprocessing and Analysis of LC-MS-Based Proteomic Data

Liquid chromatography coupled with mass spectrometry (LC-MS) has been widely used for profiling protein expression levels. This chapter is focused on LC-MS data preprocessing, which is a crucial step in the analysis of LC-MS based proteomics. We provide a high-level overview, highlight associated challenges, and present a step-by-step example for analysis of data from LC-MS based untargeted proteomic study. Furthermore, key procedures and relevant issues with the subsequent analysis by multiple reaction monitoring (MRM) are discussed.

Yeast Interspecies Comparative Proteomics Reveals Divergence in Expression Profiles and Provides Insights into Proteome Resource Allocation and Evolutionary Roles of Gene Duplication

Omics analysis is a versatile approach for understanding the conservation and diversity of molecular systems across multiple taxa. In this study, we compared the proteome expression profiles of four yeast species (Saccharomyces cerevisiae, Saccharomyces mikatae, Kluyveromyces waltii, and Kluyveromyces lactis) grown on glucose- or glycerol-containing media. Conserved expression changes across all species were observed only for a small proportion of all proteins differentially expressed between the two growth conditions. Two Kluyveromyces species, both of which exhibited a high growth rate on glycerol, a nonfermentative carbon source, showed distinct species-specific expression profiles. In K. waltii grown on glycerol, proteins involved in the glyoxylate cycle and gluconeogenesis were expressed in high abundance. In K. lactis grown on glycerol, the expression of glycolytic and ethanol metabolic enzymes was unexpectedly low, whereas proteins involved in cytoplasmic translation, including ribosomal proteins and elongation factors, were highly expressed. These marked differences in the types of predominantly expressed proteins suggest that K. lactis optimizes the balance of proteome resource allocation between metabolism and protein synthesis giving priority to cellular growth. In S. cerevisiae, about 450 duplicate gene pairs were retained after whole-genome duplication. Intriguingly, we found that in the case of duplicates with conserved sequences, the total abundance of proteins encoded by a duplicate pair in S. cerevisiae was similar to that of protein encoded by nonduplicated ortholog in Kluyveromyces yeast. Given the frequency of haploinsufficiency, this observation suggests that conserved duplicate genes, even though minor cases of retained duplicates, do not exhibit a dosage effect in yeast, except for ribosomal proteins. Thus, comparative proteomic analyses across multiple species may reveal not only species-specific characteristics of metabolic processes under nonoptimal culture conditions but also provide valuable insights into intriguing biological principles, including the balance of proteome resource allocation and the role of gene duplication in evolutionary history.

Using data-independent, high-resolution mass spectrometry in protein biomarker research: perspectives and clinical applications

In medicine, there is an urgent need for protein biomarkers in a range of applications that includes diagnostics, disease stratification, and therapeutic decisions. One of the main technologies to address this need is MS, used for protein biomarker discovery and, increasingly, also for protein biomarker validation. Currently, data-dependent analysis (also referred to as shotgun proteomics) and targeted MS, exemplified by SRM, are the most frequently used mass spectrometric methods. Recently developed data-independent acquisition techniques combine the strength of shotgun and targeted proteomics, while avoiding some of the limitations of the respective methods. They provide high-throughput, accurate quantification, and reproducible measurements within a single experimental setup. Here, we describe and review data-independent acquisition strategies and their recent use in clinically oriented studies. In addition, we also provide a detailed guide for the implementation of SWATH-MS (where SWATH is sequential window acquisition of all theoretical mass spectra)-one of the data-independent strategies that have gained wide application of late.

Activity-based protein profiling: recent advances in probe development and applications

The completion of the human genome sequencing project has provided a wealth of new information regarding the genomic blueprint of the cell. Although, to date, there are roughly 20,000 genes in the human genome, the functions of only a handful of proteins are clear. The major challenge lies in translating genomic information into an understanding of their cellular functions. The recently developed activity-based protein profiling (ABPP) is an unconventional approach that is complementary for gene expression analysis and an ideal utensil in decoding this overflow of genomic information. This approach makes use of synthetic small molecules that covalently modify a set of related proteins and subsequently facilitates identification of the target protein, enabling rapid biochemical analysis and inhibitor discovery. This tutorial review introduces recent advances in the field of ABPP and its applications.

Microbial Proteome Profiling and Systems Biology: Applications to Mycobacterium tuberculosis

Each year, 1.3 million people die from tuberculosis, an infectious disease caused by Mycobacterium tuberculosis. Systems biology-based strategies might significantly contribute to the knowledge-guided development of more effective vaccines and drugs to prevent and cure infectious diseases. To build models simulating the behaviour of a system in response to internal or external stimuli and to identify potential targets for therapeutic intervention, systems biology approaches require the acquisition of quantitative molecular profiles on many perturbed states. Here we review the current state of proteomic analyses in Mycobacterium tuberculosis and discuss the potential of recently emerging targeting mass spectrometry-based techniques which enable fast, sensitive and accurate protein measurements.

Quantitative and Systems-Based Approaches for Deciphering Bacterial Membrane Interactome and Gene Function

High-throughput genomic and proteomic methods provide a concise description of the molecular constituents of a cell, whereas systems biology strives to understand the way these components function as a whole. Recent developments, such as genome editing technologies and protein epitope-tagging coupled with high-sensitivity mass-spectrometry, allow systemic studies to be performed at an unprecedented scale. Available methods can be successfully applied to various goals, both expanding fundamental knowledge and solving applied problems. In this review, we discuss the present state and future of bacterial cell envelope interactomics, with a specific focus on host-pathogen interactions and drug target discovery. Both experimental and computational methods will be outlined together with examples of their practical implementation.

DIGESTIF: a universal quality standard for the control of bottom-up proteomics experiments

In bottom-up mass spectrometry-based proteomics analyses, variability at any step of the process, particularly during sample proteolysis, directly affects the sensitivity, accuracy, and precision of peptide detection and quantification. Currently, no generic internal standards are available to control the quality of sample processing steps. This makes it difficult to assess the comparability of MS proteomic data obtained under different experimental conditions. Here, we describe the design, synthesis, and validation of a universal protein standard, called DIGESTIF, that can be added to any biological sample. The DIGESTIF standard consists of a soluble recombinant protein scaffold to which a set of 11 artificial peptides (iRT peptides) with good ionization properties has been incorporated. In the protein scaffold, the amino acids flanking iRT peptide cleavage sites were selected either to favor or hinder protease cleavage. After sample processing, the retention time and relative intensity pattern of the released iRT peptides can be used to assess the quality of sample workup, the extent of digestion, and the performance of the LC-MS system. Thus, DIGESTIF can be used to standardize a broad spectrum of applications, ranging from simple replicate measurements to large-scale biomarker screening in biomedical applications.