Assessment of a complete and classified platelet proteome from genome-wide transcripts of human platelets and megakaryocytes covering platelet functions.

Novel platelet and megakaryocyte transcriptome analysis allows prediction of the full or theoretical proteome of a representative human platelet. Here, we integrated the established platelet proteomes from six cohorts of healthy subjects, encompassing 5.2 k proteins, with two novel genome-wide transcriptomes (57.8 k mRNAs). For 14.8 k protein-coding transcripts, we assigned the proteins to 21 UniProt-based classes, based on their preferential intracellular localization and presumed function. This classified transcriptome-proteome profile of platelets revealed: (i) Absence of 37.2 k genome-wide transcripts. (ii) High quantitative similarity of platelet and megakaryocyte transcriptomes (R = 0.75) for 14.8 k protein-coding genes, but not for 3.8 k RNA genes or 1.9 k pseudogenes (R = 0.43-0.54), suggesting redistribution of mRNAs upon platelet shedding from megakaryocytes. (iii) Copy numbers of 3.5 k proteins that were restricted in size by the corresponding transcript levels (iv) Near complete coverage of identified proteins in the relevant transcriptome (log2fpkm > 0.20) except for plasma-derived secretory proteins, pointing to adhesion and uptake of such proteins. (v) Underrepresentation in the identified proteome of nuclear-related, membrane and signaling proteins, as well proteins with low-level transcripts. We then constructed a prediction model, based on protein function, transcript level and (peri)nuclear localization, and calculated the achievable proteome at ~ 10 k proteins. Model validation identified 1.0 k additional proteins in the predicted classes. Network and database analysis revealed the presence of 2.4 k proteins with a possible role in thrombosis and hemostasis, and 138 proteins linked to platelet-related disorders. This genome-wide platelet transcriptome and (non)identified proteome database thus provides a scaffold for discovering the roles of unknown platelet proteins in health and disease.

Identification of biomarkers for the accurate and sensitive diagnosis of three bacterial pneumonia pathogens using in silico approaches.

BACKGROUND: Pneumonia ranks as one of the main infectious sources of mortality among kids under 5 years of age, killing 2500 a day; late research has additionally demonstrated that mortality is higher in the elderly. A few biomarkers, which up to this point have been distinguished for its determination lack specificity, as these biomarkers fail to build up a differentiation between pneumonia and other related diseases, for example, pulmonary tuberculosis and Human Immunodeficiency Infection (HIV). There is an inclusive global consensus of an improved comprehension of the utilization of new biomarkers, which are delivered in light of pneumonia infection for precision identification to defeat these previously mentioned constraints. Antimicrobial peptides (AMPs) have been demonstrated to be promising remedial specialists against numerous illnesses. This research work sought to identify AMPs as biomarkers for three bacterial pneumonia pathogens such as Streptococcus pneumoniae, Klebsiella pneumoniae, Acinetobacter baumannii using in silico technology. Hidden Markov Models (HMMER) was used to identify putative anti-bacterial pneumonia AMPs against the identified receptor proteins of Streptococcus pneumoniae, Klebsiella pneumoniae, and Acinetobacter baumannii. The physicochemical parameters of these putative AMPs were computed and their 3-D structures were predicted using I-TASSER. These AMPs were subsequently subjected to docking interaction analysis against the identified bacterial pneumonia pathogen proteins using PATCHDOCK. RESULTS: The in silico results showed 18 antibacterial AMPs which were ranked based on their E values with significant physicochemical parameters in conformity with known experimentally validated AMPs. The AMPs also bound the pneumonia receptors of their respective pathogens sensitively at the extracellular regions. CONCLUSIONS: The propensity of these AMPs to bind pneumonia pathogens proteins justifies that they would be potential applicant biomarkers for the recognizable detection of these bacterial pathogens in a point-of-care POC pneumonia diagnostics. The high sensitivity, accuracy, and specificity of the AMPs likewise justify the utilization of HMMER in the design and discovery of AMPs for disease diagnostics and therapeutics.

Computational assessment of the feasibility of protonation-based protein sequencing.

Recent advances in DNA sequencing methods revolutionized biology by providing highly accurate reads, with high throughput or high read length. These read data are being used in many biological and medical applications. Modern DNA sequencing methods have no equivalent in protein sequencing, severely limiting the widespread application of protein data. Recently, several optical protein sequencing methods have been proposed that rely on the fluorescent labeling of amino acids. Here, we introduce the reprotonation-deprotonation protein sequencing method. Unlike other methods, this proposed technique relies on the measurement of an electrical signal and requires no fluorescent labeling. In reprotonation-deprotonation protein sequencing, the terminal amino acid is identified through its unique protonation signal, and by repeatedly cleaving the terminal amino acids one-by-one, each amino acid in the peptide is measured. By means of simulations, we show that, given a reference database of known proteins, reprotonation-deprotonation sequencing has the potential to correctly identify proteins in a sample. Our simulations provide target values for the signal-to-noise ratios that sensor devices need to attain in order to detect reprotonation-deprotonation events, as well as suitable pH values and required measurement times per amino acid. For instance, an SNR of 10 is required for a 61.71% proteome recovery rate with 100 ms measurement time per amino acid.

Serum proteome assessment in nonalcoholic fatty liver disease in children: a preliminary study.

OBJECTIVES: Nonalcoholic fatty disease (NAFLD) affects 3-10% of the pediatric population, making it the most common chronic liver disease among children. The aim of the study is to identify potential biomarkers enabling the diagnosis of NAFLD and monitoring the course of the disease. METHODS: Proteome analysis was performed in a group of 30 patients (19 boys and 11 girls) in total, of whom 16 children had previously diagnosed NAFLD based on the abdominal ultrasound after excluding other diseases of this organ. RESULTS: A total of 297 proteins have been identified. Thirty-seven proteins (responsible for inflammation, stress response, and regulation of this process) differentiating both experimental groups were identified. Up-regulated proteins included afamin, retinol-binding protein-4, complement components, and hemopexin; while serum protease inhibitors, clusterin, immunoglobulin chains, and vitamin D binding protein were found in the down-regulated group. The correlation between selected proteins and indicators of noninvasive assessment of liver fibrosis (APRI, FIB-4) as well as differences between the serum proteome of patients with normal weight, overweight, and obesity were also assessed. CONCLUSION: The plasma protein profile is significantly altered in nonalcoholic liver disease in children and may prove to be a valuable source of biomarkers to evaluate the extent of liver disease.

Target 2035: probing the human proteome.

Biomedical scientists tend to focus on only a small fraction of the proteins encoded by the human genome despite overwhelming genetic evidence that many understudied proteins are important for human disease. One of the best ways to interrogate the function of a protein and to determine its relevance as a drug target is by using a pharmacological modulator, such as a chemical probe or an antibody. If these tools were available for most human proteins, it should be possible to translate the tremendous advances in genomics into a greater understanding of human health and disease, and catalyze the creation of innovative new medicines. Target 2035 is a global federation for developing and applying new technologies with the goal of creating chemogenomic libraries, chemical probes, and/or functional antibodies for the entire proteome.

In vitro toxicity assessment of emitted materials collected during the manufacture of water pipe plastic linings.

Objectives: US water infrastructure is in need of widespread repair due to age-related deterioration. Currently, the cured-in-place (CIPP) procedure is the most common method for water pipe repair. This method involves the on-site manufacture of a new polymer composite plastic liner within the damaged pipe. The CIPP process can release materials resulting in occupational and public health concerns. To understand hazards associated with CIPP-related emission exposures, an in vitro toxicity assessment was performed. Materials and Methods: Mouse alveolar epithelial and alveolar macrophage cell lines and condensates collected at 3 worksites utilizing styrene-based resins were utilized for evaluations. All condensate samples were normalized based on the major emission component, styrene. Further, a styrene-only exposure group was used as a control to determine mixture related toxicity. Results: Cytotoxicity differences were observed between worksite samples, with the CIPP worksite 4 sample inducing the most cell death. A proteomic evaluation was performed, which demonstrated styrene-, worksite-, and cell-specific alterations. This examination of protein expression changes determined potential biomarkers of exposure including transglutaminase 2, advillin, collagen type 1, perilipin-2, and others. Pathway analysis of exposure-induced proteomic alterations identified MYC and p53 to be regulators of cellular responses. Protein changes were also related to pathways involved in cell damage, immune response, and cancer. Conclusions: Together these findings demonstrate potential risks associated with the CIPP procedure as well as variations between worksites regarding emissions and toxicity. Our evaluation identified biological pathways that require a future evaluation and also demonstrates that exposure assessment of CIPP worksites should examine multiple chemical components beyond styrene, as many cellular responses were styrene-independent.

A Fast and Economical Sample Preparation Protocol for Interaction Proteomics Analysis.

A simple and fast immunoprecipitation (IP) protocol is designed with the sample preparation incorporated, applicable to both low and high throughput. This new protocol combines two procedures based on magnetic beads in 96-well plate format. Protein complexes are captured by antibodies and magnetic beads conjugated with protein A. Proteins are washed and on-bead digested by using Single-Pot solid-phase sample preparation (SP3). The whole IP-SP3 approach can be completed in one day, which is considerably faster compared to the classical approach. No major quantitative differences are found between SP3 and FASP (filter-aided sample preparation) or a longer incubation protocol. Taken together, the IP-SP3 protocol is a fast and economical approach easily applicable for large-scale protein interactome analysis.

Tissue-Specific Subcellular Localization Prediction Using Multi-Label Markov Random Fields.

The understanding of subcellular localization (SCL) of proteins and proteome variation in the different tissues and organs of the human body are two crucial aspects for increasing our knowledge of the dynamic rules of proteins, the cell biology, and the mechanism of diseases. Although there have been tremendous contributions to these two fields independently, the lack of knowledge of the variation of spatial distribution of proteins in the different tissues still exists. Here, we proposed an approach that allows predicting protein SCL on tissue specificity through the use of tissue-specific functional associations and physical protein-protein interactions (PPIs). We applied our previously developed Bayesian collective Markov random fields (BCMRFs) on tissue-specific protein-protein interaction network (PPI network) for nine types of tissues focusing on eight high-level SCL. The evaluated results demonstrate the strength of our approach in predicting tissue-specific SCL. We identified 1,314 proteins that their SCL were previously proven cell line dependent. We predicted 549 novel tissue-specific localized candidate proteins while some of them were validated via text-mining.

Quantitative insights into the cyanobacterial cell economy.

Phototrophic microorganisms are promising resources for green biotechnology. Compared to heterotrophic microorganisms, however, the cellular economy of phototrophic growth is still insufficiently understood. We provide a quantitative analysis of light-limited, light-saturated, and light-inhibited growth of the cyanobacterium Synechocystis sp. PCC 6803 using a reproducible cultivation setup. We report key physiological parameters, including growth rate, cell size, and photosynthetic activity over a wide range of light intensities. Intracellular proteins were quantified to monitor proteome allocation as a function of growth rate. Among other physiological acclimations, we identify an upregulation of the translational machinery and downregulation of light harvesting components with increasing light intensity and growth rate. The resulting growth laws are discussed in the context of a coarse-grained model of phototrophic growth and available data obtained by a comprehensive literature search. Our insights into quantitative aspects of cyanobacterial acclimations to different growth rates have implications to understand and optimize photosynthetic productivity.

Biosynthetic energy cost for amino acids decreases in cancer evolution.

Rapidly proliferating cancer cells have much higher demand for proteinogenic amino acids than normal cells. The use of amino acids in human proteomes is largely affected by their bioavailability, which is constrained by the biosynthetic energy cost in living organisms. Conceptually distinct from gene-based analyses, we introduce the energy cost per amino acid (ECPA) to quantitatively characterize the use of 20 amino acids during protein synthesis in human cells. By analyzing gene expression data from The Cancer Genome Atlas, we find that cancer cells evolve to utilize amino acids more economically by optimizing gene expression profile and ECPA shows robust prognostic power across many cancer types. We further validate this pattern in an experimental evolution of xenograft tumors. Our ECPA analysis reveals a common principle during cancer evolution.

Annotation resource of tandem repeat-containing secretory proteins in sixty fungi.

Fungal secretory proteins that interact with host plants are regarded as effectors. Because fungal effectors rarely contain conserved sequence features, identification and annotation of fungal effectors from predicted secretory proteins are difficult using outward comparison methods such as BLAST or hidden Markov model. In desire of more sequence features to prioritize research interests of fungal secretory proteins, this study developed a pipeline to identify tandem repeat (TR) domain within putative secretory proteins and tested a hypothesis that at least one type of TR domain in non-orthologous secretory proteins has emerged from convergent evolution for plant pathogenicity. There were 2804 types of TR domains and a total of 2925 TR-containing secretory proteins found from 60 fungi. There was no conserved type of TR domain shared only by plant pathogens, indicating functional divergence for different types of TR domain and TR-containing secretory proteins. The annotation resource of putative fungal TR-containing secretory proteins provides new sequence features that will be useful for the community interested in fungal effector biology.

Multiplexed Antigen Bead Arrays for the Assessment of Antibody Selectivity and Epitope Mapping.

With the increasing number of binding reagents for affinity-based investigations of the human proteome, high-throughput tools for the characterization of the used reagents become essential. For the analysis of binding selectivity, bead-based antigen arrays offer a miniaturized and parallelized assay platform to meet such needs, as they enable two-dimensional multiplexing to analyze up to 384 samples against up to 500 analytes in a single round of analysis. In this chapter, we describe our protocols for the generation of multiplex bead arrays built on immobilized protein fragments, as well as biotinylated peptides. Combined together, these two versions of antigen arrays offer a versatile approach for multiplexed characterization of antibody binding selectivity, off-target interactions, as well as mapping for the amino acids of epitopes involved in antibody binding.

Thermophilic Adaptation in Prokaryotes Is Constrained by Metabolic Costs of Proteostasis.

Prokaryotes evolved to thrive in an extremely diverse set of habitats, and their proteomes bear signatures of environmental conditions. Although correlations between amino acid usage and environmental temperature are well-documented, understanding of the mechanisms of thermal adaptation remains incomplete. Here, we couple the energetic costs of protein folding and protein homeostasis to build a microscopic model explaining both the overall amino acid composition and its temperature trends. Low biosynthesis costs lead to low diversity of physical interactions between amino acid residues, which in turn makes proteins less stable and drives up chaperone activity to maintain appropriate levels of folded, functional proteins. Assuming that the cost of chaperone activity is proportional to the fraction of unfolded client proteins, we simulated thermal adaptation of model proteins subject to minimization of the total cost of amino acid synthesis and chaperone activity. For the first time, we predicted both the proteome-average amino acid abundances and their temperature trends simultaneously, and found strong correlations between model predictions and 402 genomes of bacteria and archaea. The energetic constraint on protein evolution is more apparent in highly expressed proteins, selected by codon adaptation index. We found that in bacteria, highly expressed proteins are similar in composition to thermophilic ones, whereas in archaea no correlation between predicted expression level and thermostability was observed. At the same time, thermal adaptations of highly expressed proteins in bacteria and archaea are nearly identical, suggesting that universal energetic constraints prevail over the phylogenetic differences between these domains of life.

Understanding the response of Desulfovibrio desulfuricans ATCC 27774 to the electron acceptors nitrate and sulfate - biosynthetic costs modulate substrate selection.

Sulfate-reducing bacteria (SRB) are a diverse group of anaerobic microorganisms that obtain their energy from dissimilatory sulfate reduction. Some SRB species have high respiratory versatility due to the possible use of alternative electron acceptors. A good example is Desulfovibrio desulfuricans ATCC 27774, which grows in the presence of nitrate (end product: ammonium) with higher rates and yields to those observed in sulfate containing medium (end product: sulfide). In this work, the mechanisms supporting the respiratory versatility of D. desulfuricans were unraveled through the analysis of the proteome of the bacterium under different experimental conditions. The most remarkable difference in the two-dimensional gel electrophoresis maps is the high number of spots exclusively represented in the nitrate medium. Most of the proteins with increase abundance are involved in the energy metabolism and the biosynthesis of amino acids (or proteins), especially those participating in ammonium assimilation processes. qPCR analysis performed during different stages of the bacterium's growth showed that the genes involved in nitrate and nitrite reduction (napA and nrfA, respectively) have different expressions profiles: while napA did not vary significantly, nrfA was highly expressed at a 6h time point. Nitrite levels measured along the growth curve revealed a peak at 3h. Thus, the initial consumption of nitrate and concomitant production of nitrite must induce nrfA expression. The activation of alternative mechanisms for energy production, aside several N-assimilation metabolisms and detoxification processes, solves potential survival problems in adapting to different environments and contributes to higher bacterial growth rates.

Mutations in Cancer Cause Gain of Cysteine, Histidine, and Tryptophan at the Expense of a Net Loss of Arginine on the Proteome Level.

Accumulation of somatic mutations is critical for the transition of a normal cell to become cancerous. Mutations cause amino acid substitutions that change properties of proteins. However, it has not been studied as to what extent the composition and accordingly chemical properties of the cell proteome is altered as a result of the increased mutation load in cancer. Here, we analyzed data on amino acid substitutions caused by mutations in about 2000 protein coding genes from the Cancer Cell Line Encyclopedia that contains information on nucleotide and amino acid alterations in 782 cancer cell lines, and validated the analysis with information on amino acid substitutions for the same set of proteins in the Catalogue of Somatic Mutations in Cancer (COSMIC; v78) in circa 18,000 tumor samples. We found that nonsynonymous single nucleotide substitutions in the analyzed proteome subset ultimately result in a net gain of cysteine, histidine, and tryptophan at the expense of a net loss of arginine. The extraordinary loss of arginine may be attributed to some extent to composition of its codons as well as to the importance of arginine in the functioning of prominent tumor suppressor proteins like p53.

Architecture of the human interactome defines protein communities and disease networks.

The physiology of a cell can be viewed as the product of thousands of proteins acting in concert to shape the cellular response. Coordination is achieved in part through networks of protein-protein interactions that assemble functionally related proteins into complexes, organelles, and signal transduction pathways. Understanding the architecture of the human proteome has the potential to inform cellular, structural, and evolutionary mechanisms and is critical to elucidating how genome variation contributes to disease. Here we present BioPlex 2.0 (Biophysical Interactions of ORFeome-derived complexes), which uses robust affinity purification-mass spectrometry methodology to elucidate protein interaction networks and co-complexes nucleated by more than 25% of protein-coding genes from the human genome, and constitutes, to our knowledge, the largest such network so far. With more than 56,000 candidate interactions, BioPlex 2.0 contains more than 29,000 previously unknown co-associations and provides functional insights into hundreds of poorly characterized proteins while enhancing network-based analyses of domain associations, subcellular localization, and co-complex formation. Unsupervised Markov clustering of interacting proteins identified more than 1,300 protein communities representing diverse cellular activities. Genes essential for cell fitness are enriched within 53 communities representing central cellular functions. Moreover, we identified 442 communities associated with more than 2,000 disease annotations, placing numerous candidate disease genes into a cellular framework. BioPlex 2.0 exceeds previous experimentally derived interaction networks in depth and breadth, and will be a valuable resource for exploring the biology of incompletely characterized proteins and for elucidating larger-scale patterns of proteome organization.

Assessment of Label-Free Quantification in Discovery Proteomics and Impact of Technological Factors and Natural Variability of Protein Abundance.

We evaluated the state of label-free discovery proteomics focusing especially on technological contributions and contributions of naturally occurring differences in protein abundance to the intersample variability in protein abundance estimates in this highly peptide-centric technology. First, the performance of popular quantitative proteomics software, Proteome Discoverer, Scaffold, MaxQuant, and Progenesis QIP, was benchmarked using their default parameters and some modified settings. Beyond this, the intersample variability in protein abundance estimates was decomposed into variability introduced by the entire technology itself and variable protein amounts inherent to individual plants of the Arabidopsis thaliana Col-0 accession. The technical component was considerably higher than the biological intersample variability, suggesting an effect on the degree and validity of reported biological changes in protein abundance. Surprisingly, the biological variability, protein abundance estimates, and protein fold changes were recorded differently by the software used to quantify the proteins, warranting caution in the comparison of discovery proteomics results. As expected, ∼99% of the proteome was invariant in the isogenic plants in the absence of environmental factors; however, few proteins showed substantial quantitative variability. This naturally occurring variation between individual organisms can have an impact on the causality of reported protein fold changes.

Fundamental amino acid mass distributions and entropy costs in proteomes.

We examine whether the frequency of amino acids across an organism's proteome is primarily determined by optimization to function or other factors, such as the structure of the genetic code. Considering all available proteins together, we first point out that the frequency of an amino acid in a proteome negatively correlates with its mass, suggesting that the genome preserves a fundamental distribution ruled by simple energetics. Given the universality of such distributions, one can use outliers, cysteine and leucine, to identify amino acids that deviate from this simple rule for functional purposes and examine those functions. We quantify the strength of such selection as the entropic cost outliers pay to defy the mass-frequency relation. Codon degeneracy of an amino acid partially explains the correlation between mass and frequency: light amino acids being typically encoded by highly degenerate codon families, with the exception of arginine. While degeneracy may be a factor in hard wiring the relationship between mass and frequency in proteomes, it does not provide a complete explanation. By examining extremophiles, we are able to show that this law weakens with temperature, likely due to protein stability considerations, thus the environment is essential.

Conservation in Mammals of Genes Associated with Aggression-Related Behavioral Phenotypes in Honey Bees.

The emerging field of sociogenomics explores the relations between social behavior and genome structure and function. An important question is the extent to which associations between social behavior and gene expression are conserved among the Metazoa. Prior experimental work in an invertebrate model of social behavior, the honey bee, revealed distinct brain gene expression patterns in African and European honey bees, and within European honey bees with different behavioral phenotypes. The present work is a computational study of these previous findings in which we analyze, by orthology determination, the extent to which genes that are socially regulated in honey bees are conserved across the Metazoa. We found that the differentially expressed gene sets associated with alarm pheromone response, the difference between old and young bees, and the colony influence on soldier bees, are enriched in widely conserved genes, indicating that these differences have genomic bases shared with many other metazoans. By contrast, the sets of differentially expressed genes associated with the differences between African and European forager and guard bees are depleted in widely conserved genes, indicating that the genomic basis for this social behavior is relatively specific to honey bees. For the alarm pheromone response gene set, we found a particularly high degree of conservation with mammals, even though the alarm pheromone itself is bee-specific. Gene Ontology identification of human orthologs to the strongly conserved honey bee genes associated with the alarm pheromone response shows overrepresentation of protein metabolism, regulation of protein complex formation, and protein folding, perhaps associated with remodeling of critical neural circuits in response to alarm pheromone. We hypothesize that such remodeling may be an adaptation of social animals to process and respond appropriately to the complex patterns of conspecific communication essential for social organization.

Quantification and Classification of E. coli Proteome Utilization and Unused Protein Costs across Environments.

The costs and benefits of protein expression are balanced through evolution. Expression of un-utilized protein (that have no benefits in the current environment) incurs a quantifiable fitness costs on cellular growth rates; however, the magnitude and variability of un-utilized protein expression in natural settings is unknown, largely due to the challenge in determining environment-specific proteome utilization. We address this challenge using absolute and global proteomics data combined with a recently developed genome-scale model of Escherichia coli that computes the environment-specific cost and utility of the proteome on a per gene basis. We show that nearly half of the proteome mass is unused in certain environments and accounting for the cost of this unused protein expression explains >95% of the variance in growth rates of Escherichia coli across 16 distinct environments. Furthermore, reduction in unused protein expression is shown to be a common mechanism to increase cellular growth rates in adaptive evolution experiments. Classification of the unused protein reveals that the unused protein encodes several nutrient- and stress- preparedness functions, which may convey fitness benefits in varying environments. Thus, unused protein expression is the source of large and pervasive fitness costs that may provide the benefit of hedging against environmental change.