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This article provides a systematic review of research conducted on the proteomic composition of blood as part of a complex biological age estimation. We performed a comprehensive analysis of 17 publicly available datasets and compiled an integral list of proteins. These proteins were sorted based on their detection probability using mass spectrometry in human plasma. We propose this list as a basis for creating a panel of peptides and quantifying the content of selected proteins in the format of a proteomic aging clock. The selected proteins are especially notable for their roles in inflammatory processes and lipid metabolism. Our findings suggest, for the first time, that proteins associated with systemic disorders, including those approved by the FDA for clinical use, could serve as potential markers of aging.

Limit of detection (LoD) is a term that is used to characterize the sensitivity of an analytical method. The existing limitation of the sensitivity of analysis using modern mass spectrometry methods has been experimentally shown to be a limiting factor in the application of proteomic technologies in medicine. This article proposes a concept of a new technology that will set a new vector of development in the development of systems for solving problems of medical diagnostics and deals with theoretical and practical aspects of creating a new technology for the detection of single biomacromolecules (in particular, proteins) in biological samples. Such technology should be based on the principle of signal registration similar to that used in a Geiger counter (also known as a Geiger-Müller counter or G-M counter), a device that automatically counts the number of ionizing particles that hit it. This counter is free from probabilistic components; it registers a signal if there is at least one target molecule in the analysis chamber. Predictive medical diagnostics require technology based on methods where sensitivity allows for the detection of single marker molecules in a biological sample volume of 1-10 µL, the smallest volume of biomaterial used in laboratory diagnostics. Creation of a detector with a sensitivity of 10 -18 M would allow for the detection of one molecule in 1 µL of the sample, which fundamentally makes this approach analogous to a G-M counter for solutions. To date, bioanalytical methods are limited to a sensitivity of 10 -12 M (which is approximately 1 million molecules per 1 μL), which is insufficient to capture the early stages of pathological processes.

This work demonstrates the use of a modified mica to concentrate proteins, which is required for proteomic profiling of blood plasma by mass spectrometry (MS). The surface of mica substrates, which are routinely used in atomic force microscopy (AFM), was modified with a photocrosslinker to allow "irreversible" binding of proteins via covalent bond formation. This modified substrate was called the AFM chip. This study aimed to determine the role of the surface and crosslinker in the efficient concentration of various types of proteins in plasma over a wide concentration range. The substrate surface was modified with a 4-benzoylbenzoic acid N-succinimidyl ester (SuccBB) photocrosslinker, activated by UV irradiation. AFM chips were incubated with plasma samples from a healthy volunteer at various dilution ratios (10 2 X, 10 4 X, and 10 6 X). Control experiments were performed without UV irradiation to evaluate the contribution of physical protein adsorption to the concentration efficiency. AFM imaging confirmed the presence of protein layers on the chip surface after incubation with the samples. MS analysis of different samples indicated that the proteomic profile of the AFM-visualized layers contained common and unique proteins. In the working series of experiments, 228 proteins were identified on the chip surface for all samples, and 21 proteins were not identified in the control series. In the control series, a total of 220 proteins were identified on the chip surface, seven of which were not found in the working series. In plasma samples at various dilution ratios, a total of 146 proteins were identified without the concentration step, while 17 proteins were not detected in the series using AFM chips. The introduction of a concentration step using AFM chips allowed us to identify more proteins than in plasma samples without this step. We found that AFM chips with a modified surface facilitate the efficient concentration of proteins owing to the adsorption factor and the formation of covalent bonds between the proteins and the chip surface. The results of our study can be applied in the development of highly sensitive analytical systems for determining the complete composition of the plasma proteome.

Transcriptomics methods (RNA-Seq, PCR) today are more routine and reproducible than proteomics methods, i.e., both mass spectrometry and immunochemical analysis. For this reason, most scientific studies are limited to assessing the level of mRNA content. At the same time, protein content (and its post-translational status) largely determines the cell's state and behavior. Such a forced extrapolation of conclusions from the transcriptome to the proteome often seems unjustified. The ratios of "transcript-protein" pairs can vary by several orders of magnitude for different genes. As a rule, the correlation coefficient between transcriptome-proteome levels for different tissues does not exceed 0.3-0.5. Several characteristics determine the ratio between the content of mRNA and protein: among them, the rate of movement of the ribosome along the mRNA and the number of free ribosomes in the cell, the availability of tRNA, the secondary structure, and the localization of the transcript. The technical features of the experimental methods also significantly influence the levels of the transcript and protein of the corresponding gene on the outcome of the comparison. Given the above biological features and the performance of experimental and bioinformatic approaches, one may develop various models to predict proteomic profiles based on transcriptomic data. This review is devoted to the ability of RNA sequencing methods for protein abundance prediction.

Aims: The main goal of the Russian part of C-HPP is to detect and functionally annotate missing proteins (PE2-PE4) encoded by human chromosome 18. To achieve this goal, it is necessary to use the most sensitive methods of analysis., Background: However, identifying such proteins in a complex biological mixture using mass spectrometry (MS)-based methods is difficult due to the insufficient sensitivity of proteomic analysis methods. A possible solution to the problem is the pre-fractionation of a complex biological sample at the sample preparation stage., Objective: This study aims to measure the detection limit of SRM SIS analysis using a standard set of UPS1 proteins and find a way to enhance the sensitivity of the analysis and to, detect proteins encoded by the human chromosome 18 in liver tissue samples, and compare the data with transcriptomic analysis of the same samples., Methods: Mass spectrometry, data-dependent acquisition, selected reaction monitoring, highperformance liquid chromatography, data-dependent acquisition in combination with pre-fractionation by alkaline reversed-phase chromatography, selected reaction monitoring in combination with prefractionation by alkaline reversed-phase chromatography methods were used in this study., Results: The results revealed that 100% of UPS1 proteins in a mixture could only be identified at a concentration of at least 10 -9 М. The decrease in concentration leads to protein losses associated with technology sensitivity, and no UPS1 protein is detected at a concentration of 10 -13 М. Therefore, the two-dimensional fractionation of samples was applied to improve sensitivity. The human liver tissue was examined by selected reaction monitoring and shotgun methods of MS analysis using onedimensional and two-dimensional fractionation to identify the proteins encoded by human chromosome 18. A total of 134 proteins were identified. The overlap between proteomic and transcriptomic data in human liver tissue was ~50%., Conclusion: The sample concentration technique is well suited for a standard UPS1 system that is not contaminated with a complex biological sample. However, it is not suitable for use with a complex biological protein mixture. Thus, it is necessary to develop more sophisticated fractionation systems for the detection of all low-copy proteins. This weak convergence is due to the low sensitivity of proteomic technology compared to transcriptomic approaches. Also, total mRNA was used to perform RNA-seq analysis, but not all detected mRNA molecules could be translated into proteins. This introduces additional uncertainty in the data; in the future, we plan to study only translated mRNA molecules-the translatome. Data is available via ProteomeXchange with identifier PXD026997., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

One of the main goals of the Chromosome-Centric Human Proteome Project (C-HPP) is detection of "missing proteins" (PE2-PE4). Using the UPS2 (Universal proteomics standard 2) set as a model to simulate the range of protein concentrations in the cell, we have previously shown that 2D fractionation enables the detection of more than 95% of UPS2 proteins in a complex biological mixture. In this study, we propose a novel experimental workflow for protein detection during the analysis of biological samples. This approach is extremely important in the context of the C-HPP and the neXt-MP50 Challenge, which can be solved by increasing the sensitivity and the coverage of the proteome encoded by a particular human chromosome. In this study, we used 2D fractionation for in-depth analysis of the proteins encoded by human chromosome 18 (Chr 18) in the HepG2 cell line. Use of 2D fractionation increased the sensitivity of the SRM SIS method by 1.3-fold (68 and 88 proteins were identified by 1D fractionation and 2D fractionation, respectively) and the shotgun MS/MS method by 2.5-fold (21 and 53 proteins encoded by Chr 18 were detected by 1D fractionation and 2D fractionation, respectively). The results of all experiments indicate that 111 proteins encoded by human Chr 18 have been identified; this list includes 42% of the Chr 18 protein-coding genes and 67% of the Chr 18 transcriptome species (Illumina RNaseq) in the HepG2 cell line obtained using a single sample. Corresponding mRNAs were not registered for 13 of the detected proteins. The combination of 2D fractionation technology with SRM SIS and shotgun mass spectrometric analysis did not achieve full coverage, i.e., identification of at least one protein product for each of the 265 protein-coding genes of the selected chromosome. To further increase the sensitivity of the method, we plan to use 5-10 crude synthetic peptides for each protein to identify the proteins and select one of the peptides based on the obtained mass spectra for the synthesis of an isotopically labeled standard for subsequent quantitative analysis. Data are available via ProteomeXchange with the identifier PXD019263.

One of the major goals of the Chromosome-Centric Human Proteome Project (C-HPP) is to catalog and annotate a myriad of heterogeneous proteoforms, produced by ca. 20 thousand genes. To achieve a detailed and personalized understanding into proteomes, we suggest using a customized RNA-seq library of potential proteoforms, which includes aberrant variants specific to certain biological samples. Two-dimensional electrophoresis coupled with high-performance liquid chromatography allowed us to downgrade the difficulty of biological mixing following shotgun mass spectrometry. To benchmark the proposed pipeline, we examined heterogeneity of the HepG2 hepatoblastoma cell line proteome. Data are available via ProteomeXchange with identifier PXD018450., Competing Interests: The authors declare no conflict of interest.

This manuscript collects all the efforts of the Russian Consortium, bottlenecks revealed in the course of the C-HPP realization, and ways of their overcoming. One of the main bottlenecks in the C-HPP is the insufficient sensitivity of proteomic technologies, hampering the detection of low- and ultralow-copy number proteins forming the "dark part" of the human proteome. In the frame of MP-Challenge, to increase proteome coverage we suggest an experimental workflow based on a combination of shotgun technology and selected reaction monitoring with two-dimensional alkaline fractionation. Further, to detect proteins that cannot be identified by such technologies, nanotechnologies such as combined atomic force microscopy with molecular fishing and/or nanowire detection may be useful. These technologies provide a powerful tool for single molecule analysis, by analogy with nanopore sequencing during genome analysis. To systematically analyze the functional features of some proteins (CP50 Challenge), we created a mathematical model that predicts the number of proteins differing in amino acid sequence: proteoforms. According to our data, we should expect about 100 000 different proteoforms in the liver tissue and a little more in the HepG2 cell line. The variety of proteins forming the whole human proteome significantly exceeds these results due to post-translational modifications (PTMs). As PTMs determine the functional specificity of the protein, we propose using a combination of gene-centric transcriptome-proteomic analysis with preliminary fractionation by two-dimensional electrophoresis to identify chemically modified proteoforms. Despite the complexity of the proposed solutions, such integrative approaches could be fruitful for MP50 and CP50 Challenges in the framework of the C-HPP.

The Chromosome-centric Human Proteome Project aims at characterizing the expression of proteins encoded in each chromosome at the tissue, cell, and subcellular levels. The proteomic profiling of a particular tissue or cell line commonly results in a substantial portion of proteins that are not observed (the "missing" proteome). The concurrent transcriptome profiling of the analyzed tissue/cells samples may help define the set of untranscribed genes in a given type of tissue or cell, thus narrowing the size of the "missing" proteome and allowing us to focus on defining the reasons behind undetected proteins, namely, whether they are technical (insufficient sensitivity of protein detection) or biological (correspond to not-translated transcripts). We believe that the quantitative polymerase chain reaction (qPCR) can provide an efficient approach to studying low-abundant transcripts related to undetected proteins due to its high sensitivity and the possibility of ensuring the specificity of detection via the simple Sanger sequencing of PCR products. Here we illustrated the feasibility of such an approach on a set of low-abundant transcripts. Although inapplicable to the analysis of whole transcriptome, qPCR can successfully be utilized to profile a limited cohort of transcripts encoded on a particular chromosome, as we previously demonstrated for human chromosome 18.

The fundamental mission of the Chromosome-Centric Human Proteome Project (C-HPP) is the research of human proteome diversity, including rare variants. Liver tissues, HepG2 cells, and plasma were selected as one of the major objects for C-HPP studies. The proteogenomic approach, a recently introduced technique, is a powerful method for predicting and validating proteoforms coming from alternative splicing, mutations, and transcript editing. We developed PPLine, a Python-based proteogenomic pipeline providing automated single-amino-acid polymorphism (SAP), indel, and alternative-spliced-variants discovery based on raw transcriptome and exome sequence data, single-nucleotide polymorphism (SNP) annotation and filtration, and the prediction of proteotypic peptides (available at https://sourceforge.net/projects/ppline). In this work, we performed deep transcriptome sequencing of HepG2 cells and liver tissues using two platforms: Illumina HiSeq and Applied Biosystems SOLiD. Using PPLine, we revealed 7756 SAP and indels for HepG2 cells and liver (including 659 variants nonannotated in dbSNP). We found 17 indels in transcripts associated with the translation of alternate reading frames (ARF) longer than 300 bp. The ARF products of two genes, SLMO1 and TMEM8A, demonstrate signatures of caspase-binding domain and Gcn5-related N-acetyltransferase. Alternative splicing analysis predicted novel proteoforms encoded by 203 (liver) and 475 (HepG2) genes according to both Illumina and SOLiD data. The results of the present work represent a basis for subsequent proteomic studies by the C-HPP consortium.

Insufficient sensitivity of methods for detection of proteins at a single molecule level does not yet allow obtaining the whole image of human proteome. But to go further, we need at least to know the proteome size, or how many different protein species compose this proteome. This is the task that could be at least partially realized by the method described in this article. The approach used in our study is based on detection of protein spots in 2DE after staining by protein dyes with various sensitivities. As the different protein spots contain different protein species, counting the spots opens a way for estimation of number of protein species. The function representing the dependence of the number of protein spots on sensitivity or LOD of protein dyes was generated. And extrapolation of this function curve to theoretical point of the maximum sensitivity (detection of a single smallest polypeptide) allowed to counting the number of different molecules (polypeptide species) at the concentration level of a single polypeptide per proteome. Using this approach, it was estimated that the minimal numbers of protein species for model objects, Escherichia coli and Pirococcus furiosus, are 6200 and 3400, respectively. We expect a single human cell (HepG2) to contain minimum 70 000 protein species., (© 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Today, proteomics usually compares clinical samples by use of bottom-up profiling with high resolution mass spectrometry, where all protein products of a single gene are considered as an integral whole. At the same time, proteomics of proteoforms, which considers the variety of protein species, offers the potential to discover valuable biomarkers. Proteoforms are protein species that arise as a consequence of genetic polymorphisms, alternative splicing, post-translational modifications and other less-explored molecular events. The comprehensive observation of proteoforms has been an exclusive privilege of top-down proteomics. Here, we review the possibilities of a bottom-up approach to address the microheterogeneity of the human proteome. Special focus is given to shotgun proteomics and structure-based bioinformatics as a source of hypothetical proteoforms, which can potentially be verified by targeted mass spectrometry to determine the relevance of proteoforms to diseases.

During the 2010 Human Proteome Organization Congress in Sydney, a gene-centric approach emerged as a feasible and tractable scaffold for assemblage of the Human Proteome Project. Bringing the gene-centric principle into practice, a roadmap for the 18th chromosome was drafted, postulating the limited sensitivity of analytical methods, as a serious bottleneck in proteomics. In the context of the sensitivity problem, we refer to the "copy number of protein molecules" as a measurable assessment of protein abundance. The roadmap is focused on the development of technology to attain the low- and ultralow -"copied" portion of the proteome. Roadmap merges the genomic, transcriptomic and proteomic levels to identify the majority of 285 proteins from 18th chromosome - master proteins. Master protein is the primary translation of the coding sequence and resembling at least one of the known isoforms, coded by the gene. The executive phase of the roadmap includes the expansion of the study of the master proteins with alternate splicing, single amino acid polymorphisms (SAPs) and post-translational modifications. In implementing the roadmap, Russian scientists are expecting to establish proteomic technologies for integrating MS and atomic force microscopy (AFM). These technologies are anticipated to unlock the value of new biomarkers at a detection limit of 10(-18) M, i.e. 1 protein copy per 1 μL of plasma. The roadmap plan is posted at www.proteome.ru/en/roadmap/ and a forum for discussion of the document is supported., (Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

The mouse liver microsome proteome was investigated using ion trap MS combined with three separation workflows including SDS-PAGE followed by reverse-phase LC of in-gel protein digestions (519 proteins identified); 2-D LC of protein digestion (1410 proteins); whole protein separation on mRP heat-stable column followed by 2-D LC of protein digestions from each fraction (3-D LC; 3703 proteins). The higher number of proteins identified in the workflow corresponded to the lesser percentage of run-to-run reproducibility. Gel-based method yielded a number of predicted membrane proteins similar to LC-based workflows.

Simple Summary: Obesity is a significant health concern associated with fat accumulation and complications like inflammation, cancer, and diabetes. Understanding its molecular roots is crucial, especially for young individuals who are capable of lifestyle changes. Our study examined underweight, lean, overweight, and obese individuals by analyzing blood samples using metabolomics, proteomics, and genomics. Using metabolomics, we identified 313 substances; proteomics revealed 708 proteins, and genomics explored 647,250 point mutations. Models predicting body mass index showed that, individually, proteomics provided more value for prediction, followed by metabolomics and genomics. Combining proteomics and metabolomics in a multiomic approach yielded the best results, surpassing single-factor analyses. This pioneering study is the first to focus on obesity in young people, integrating genomic, proteomic, and metabolomic data. Our findings provide valuable insights into the molecular mechanisms of obesity, offering potential for more targeted interventions. Obesity is a socially significant disease that is characterized by a disproportionate accumulation of fat. It is also associated with chronic inflammation, cancer, diabetes, and other comorbidities. Investigating biomarkers and pathological processes linked to obesity is especially vital for young individuals, given their increased potential for lifestyle modifications. By comparing the genetic, proteomic, and metabolomic profiles of individuals categorized as underweight, normal, overweight, and obese, we aimed to determine which omics layer most accurately reflects the phenotypic changes in an organism that result from obesity. We profiled blood plasma samples by employing three omics methodologies. The untargeted GC×GC–MS metabolomics approach identified 313 metabolites. To augment the metabolomic dataset, we integrated a label-free HPLC–MS/MS proteomics method, leading to the identification of 708 proteins. The genomic layer encompassed the genotyping of 647,250 SNPs. Utilizing omics data, we trained sparse Partial Least Squares models to predict body mass index. Molecular features exhibiting frequently non-zero coefficients were selected as potential biomarkers, and we further explored enriched biological pathways. Proteomics was the most effective in single-omics analyses, with a median absolute error (MAE) of 5.44 ± 0.31 kg/m2, incorporating an average of 24 proteins per model. Metabolomics showed slightly lower performance (MAE = 6.06 ± 0.33 kg/m2), followed by genomics (MAE = 6.20 ± 0.34 kg/m2). As expected, multiomic models demonstrated better accuracy, particularly the combination of proteomics and metabolomics (MAE = 4.77 ± 0.33 kg/m2), while including genomics data did not enhance the results. This manuscript is the first multiomics study of obesity in a gender-balanced cohort of young adults profiled by genomic, proteomic, and metabolomic methods. The comprehensive approach provides novel insights into the molecular mechanisms of obesity, opening avenues for more targeted interventions. [ABSTRACT FROM AUTHOR]

Foodborne bacteria interconnect food and human health. Despite significant progress in food safety regulation, bacterial contamination is still a serious public health concern and the reason for significant commercial losses. The screening of the microbiome in meals is one of the main aspects of food production safety influencing the health of the end-consumers. Our research provides an overview of proteomics findings in the field of food safety made over the last decade. It was believed that proteomics offered an accurate snapshot of the complex networks of the major biological machines called proteins. The proteomic methods for the detection of pathogens were armed with bioinformatics algorithms, allowing us to map the data onto the genome and transcriptome. The mechanisms of the interaction between bacteria and their environment were elucidated with unprecedented sensitivity, specificity, and depth. Using our web-based tool ScanBious for automated publication analysis, we analyzed over 48,000 scientific articles on antibiotic and disinfectant resistance and highlighted the benefits of proteomics for the food safety field. The most promising approach to studying safety in food production is the combination of classical genomic and metagenomic approaches and the advantages provided by proteomic methods with the use of panoramic and targeted mass spectrometry. [ABSTRACT FROM AUTHOR]

The cutoff level applied in sequencing analysis varies according to the sequencing technology, sample type, and study purpose, which can largely affect the coverage and reliability of the data obtained. In this study, we aimed to determine the optimal combination of parameters for reliable RNA transcriptome data analysis. Toward this end, we compared the results obtained from different transcriptome analysis platforms (quantitative polymerase chain reaction, Illumina RNASeq, and Oxford Nanopore Technologies MinION) for the transcriptome encoded by human chromosome 18 (Chr 18) using the same sample types (HepG2 cells and liver tissue). A total of 275 protein-coding genes encoded by Chr 18 was taken as the gene set for evaluation. The combination of Illumina RNASeq and MinION nanopore technologies enabled the detection of at least one transcript for each protein-coding gene encoded by Chr 18. This combination also reduced the probability of false-positive detection of low-copy transcripts due to the simultaneous confirmation of the presence of a transcript by the two fundamentally different technologies: short reads essential for reliable detection (Illumina RNASeq) and long-read sequencing data (MinION). The combination of these technologies achieved complete coverage of all 275 protein-coding genes on Chr 18, identifying transcripts with non-zero expression levels. This approach can improve distinguishing the biological and technical reasons for the absence of mRNA detection for a given gene in transcriptomics. [ABSTRACT FROM AUTHOR]

Obesity is a frightening chronic disease, which has tripled since 1975. It is not expected to slow down staying one of the leading cases of preventable death and resulting in an increased clinical and economic burden. Poor lifestyle choices and excessive intake of "cheap calories" are major contributors to obesity, triggering type 2 diabetes, cardiovascular diseases, and other comorbidities. Understanding the molecular mechanisms responsible for development of obesity is essential as it might result in the introducing of anti-obesity targets and early-stage obesity biomarkers, allowing the distinction between metabolic syndromes. The complex nature of this disease, coupled with the phenomenon of metabolically healthy obesity, inspired us to perform data-centric, hypothesis-generating pilot research, aimed to find correlations between parameters of classic clinical blood tests and proteomic profiles of 104 lean and obese subjects. As the result, we assembled patterns of proteins, which presence or absence allows predicting the weight of the patient fairly well. We believe that such proteomic patterns with high prediction power should facilitate the translation of potential candidates into biomarkers of clinical use for early-stage stratification of obesity therapy. [ABSTRACT FROM AUTHOR]

Abstract: The Human Proteome Project (HPP) was started two years ago and the international consortia have elaborated a number of informational resources to harbor the HPP data. Selected informational resources are currently used to elaborate the HPP baseline metrics, which were introduced to estimate future contribution of HPP to the knowledge domain. We developed a Web-based tool Gene-centric Content Management System (GenoCMS) for comparing public resources to proprietary results by using the representation of proteins as color-coded catalog. Within our CMS, the features of protein-coding genes are uploaded from the public domain and then appended by additional features derived from original experimental workflows. We describe the heat-map/traffic light representation of our proteomic experiments as the background of data taken from NeXtProt, MS/MS repositories, the Human Protein Atlas and the RNAseqAtlas. The system presented at www.kb18.ru comprises a collaborative knowledge base for annotating the gene sets and disseminating these annotations through the Web. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan. [Copyright &y& Elsevier]