Multiomics Picture of Obesity in Young Adults

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.

Identifying N6-Methyladenosine Sites in HepG2 Cell Lines Using Oxford Nanopore Technology

RNA modifications, particularly N6-methyladenosine (m6A), are pivotal regulators of RNA functionality and cellular processes. We analyzed m6A modifications by employing Oxford Nanopore technology and the m6Anet algorithm, focusing on the HepG2 cell line. We identified 3968 potential m6A modification sites in 2851 transcripts, corresponding to 1396 genes. A gene functional analysis revealed the active involvement of m6A-modified genes in ubiquitination, transcription regulation, and protein folding processes, aligning with the known role of m6A modifications in histone ubiquitination in cancer. To ensure data robustness, we assessed reproducibility across technical replicates. This study underscores the importance of evaluating algorithmic reproducibility, especially in supervised learning. Furthermore, we examined correlations between transcriptomic, translatomic, and proteomic levels. A strong transcriptomic-translatomic correlation was observed. In conclusion, our study deepens our understanding of m6A modifications' multifaceted impacts on cellular processes and underscores the importance of addressing reproducibility concerns in analytical approaches.

Workability of mRNA Sequencing for Predicting Protein Abundance

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.

Rhizochalinin Exhibits Anticancer Activity and Synergizes with EGFR Inhibitors in Glioblastoma In Vitro Models

Rhizochalinin (Rhiz) is a recently discovered cytotoxic sphingolipid synthesized from the marine natural compound rhizochalin. Previously, Rhiz demonstrated high in vitro and in vivo efficacy in various cancer models. Here, we report Rhiz to be highly active in human glioblastoma cell lines as well as in patient-derived glioma-stem like neurosphere models. Rhiz counteracted glioblastoma cell proliferation by inducing apoptosis, G2/M-phase cell cycle arrest, and inhibition of autophagy. Proteomic profiling followed by bioinformatic analysis suggested suppression of the Akt pathway as one of the major biological effects of Rhiz. Suppression of Akt as well as IGF-1R and MEK1/2 kinase was confirmed in Rhiz-treated GBM cells. In addition, Rhiz pretreatment resulted in a more pronounced inhibitory effect of γ-irradiation on the growth of patient-derived glioma-spheres, an effect to which the Akt inhibition may also contribute decisively. In contrast, EGFR upregulation, observed in all GBM neurospheres under Rhiz treatment, was postulated to be a possible sign of incipient resistance. In line with this, combinational therapy with EGFR-targeted tyrosine kinase inhibitors synergistically increased the efficacy of Rhiz resulting in dramatic inhibition of GBM cell viability as well as a significant reduction of neurosphere size in the case of combination with lapatinib. Preliminary in vitro data generated using a parallel artificial membrane permeability (PAMPA) assay suggested that Rhiz cannot cross the blood brain barrier and therefore alternative drug delivery methods should be used in the further in vivo studies. In conclusion, Rhiz is a promising new candidate for the treatment of human glioblastoma, which should be further developed in combination with EGFR inhibitors.

The Expectation and Reality of the HepG2 Core Metabolic Profile

To represent the composition of small molecules circulating in HepG2 cells and the formation of the "core" of characteristic metabolites that often attract researchers' attention, we conducted a meta-analysis of 56 datasets obtained through metabolomic profiling via mass spectrometry and NMR. We highlighted the 288 most commonly studied compounds of diverse chemical nature and analyzed metabolic processes involving these small molecules. Building a complete map of the metabolome of a cell, which encompasses the diversity of possible impacts on it, is a severe challenge for the scientific community, which is faced not only with natural limitations of experimental technologies, but also with the absence of transparent and widely accepted standards for processing and presenting the obtained metabolomic data. Formulating our research design, we aimed to reveal metabolites crucial to the Hepg2 cell line, regardless of all chemical and/or physical impact factors. Unfortunately, the existing paradigm of data policy leads to a streetlight effect. When analyzing and reporting only target metabolites of interest, the community ignores the changes in the metabolomic landscape that hide many molecular secrets.

Exploiting Multi-Omics Profiling and Systems Biology to Investigate Functions of TOMM34

Although modern biology is now in the post-genomic era with vastly increased access to high-quality data, the set of human genes with a known function remains far from complete. This is especially true for hundreds of mitochondria-associated genes, which are under-characterized and lack clear functional annotation. However, with the advent of multi-omics profiling methods coupled with systems biology algorithms, the cellular role of many such genes can be elucidated. Here, we report genes and pathways associated with TOMM34, Translocase of Outer Mitochondrial Membrane, which plays role in the mitochondrial protein import as a part of cytosolic complex together with Hsp70/Hsp90 and is upregulated in various cancers. We identified genes, proteins, and metabolites altered in TOMM34^-/- HepG2 cells. To our knowledge, this is the first attempt to study the functional capacity of TOMM34 using a multi-omics strategy. We demonstrate that TOMM34 affects various processes including oxidative phosphorylation, citric acid cycle, metabolism of purine, and several amino acids. Besides the analysis of already known pathways, we utilized de novo network enrichment algorithm to extract novel perturbed subnetworks, thus obtaining evidence that TOMM34 potentially plays role in several other cellular processes, including NOTCH-, MAPK-, and STAT3-signaling. Collectively, our findings provide new insights into TOMM34's cellular functions.

Estimating Total Quantitative Protein Content in Escherichia coli, Saccharomyces cerevisiae, and HeLa Cells

The continuous improvement of proteomic techniques, most notably mass spectrometry, has generated quantified proteomes of many organisms with unprecedented depth and accuracy. However, there is still a significant discrepancy in the reported numbers of total protein molecules per specific cell type. In this article, we explore the results of proteomic studies of Escherichia coli, Saccharomyces cerevisiae, and HeLa cells in terms of total protein copy numbers per cell. We observe up to a ten-fold difference between reported values. Investigating possible reasons for this discrepancy, we conclude that neither an unmeasured fraction of the proteome nor biases in the quantification of individual proteins can explain the observed discrepancy. We normalize protein copy numbers in each study using a total protein amount per cell as reported in the literature and create integrated proteome maps of the selected model organisms. Our results indicate that cells contain from one to three million protein molecules per µm³ and that protein copy density decreases with increasing organism complexity.

Blood Plasma Proteome: A Meta-Analysis of the Results of Protein Quantification in Human Blood by Targeted Mass Spectrometry

A meta-analysis of the results of targeted quantitative screening of human blood plasma was performed to generate a reference standard kit that can be used for health analytics. The panel included 53 of the 296 proteins that form a “stable” part of the proteome of a healthy individual; these proteins were found in at least 70% of samples and were characterized by an interindividual coefficient of variation <40%. The concentration range of the selected proteins was 10−10−10−3 M and enrichment analysis revealed their association with rare familial diseases. The concentration of ceruloplasmin was reduced by approximately three orders of magnitude in patients with neurological disorders compared to healthy volunteers, and those of gelsolin isoform 1 and complement factor H were abruptly reduced in patients with lung adenocarcinoma. Absolute quantitative data of the individual proteome of a healthy and diseased individual can be used as the basis for personalized medicine and health monitoring. Storage over time allows us to identify individual biomarkers in the molecular landscape and prevent pathological conditions.

Exploring Dynamic Metabolome of the HepG2 Cell Line: Rise and Fall

Both biological and technical variations can discredit the reliability of obtained data in omics studies. In this technical note, we investigated the effect of prolonged cultivation of the HepG2 hepatoma cell line on its metabolomic profile. Using the GC × GC-MS approach, we determined the degree of metabolic variability across HepG2 cells cultured in uniform conditions for 0, 5, 10, 15, and 20 days. Post-processing of obtained data revealed substantial changes in relative abundances of 110 metabolites among HepG2 samples under investigation. Our findings have implications for interpreting metabolomic results obtained from immortal cells, especially in longitudinal studies. There are still plenty of unanswered questions regarding metabolomics variability and many potential areas for future targeted and panoramic research. However, we suggest that the metabolome of cell lines is unstable and may undergo significant transformation over time, even if the culture conditions remain the same. Considering metabolomics variability on a relatively long-term basis, careful experimentation with particular attention to control samples is required to ensure reproducibility and relevance of the research results when testing both fundamentally and practically significant hypotheses.

Epitranscriptome: Review of Top 25 Most-Studied RNA Modifications

The alphabet of building blocks for RNA molecules is much larger than the standard four nucleotides. The diversity is achieved by the post-transcriptional biochemical modification of these nucleotides into distinct chemical entities that are structurally and functionally different from their unmodified counterparts. Some of these modifications are constituent and critical for RNA functions, while others serve as dynamic markings to regulate the fate of specific RNA molecules. Together, these modifications form the epitranscriptome, an essential layer of cellular biochemistry. As of the time of writing this review, more than 300 distinct RNA modifications from all three life domains have been identified. However, only a few of the most well-established modifications are included in most reviews on this topic. To provide a complete overview of the current state of research on the epitranscriptome, we analyzed the extent of the available information for all known RNA modifications. We selected 25 modifications to describe in detail. Summarizing our findings, we describe the current status of research on most RNA modifications and identify further developments in this field.

The Curious Case of the HepG2 Cell Line: 40 Years of Expertise

Liver cancer is the third leading cause of cancer death worldwide. Representing such a dramatic impact on our lives, liver cancer is a significant public health concern. Sustainable and reliable methods for preventing and treating liver cancer require fundamental research on its molecular mechanisms. Cell lines are treated as in vitro equivalents of tumor tissues, making them a must-have for basic research on the nature of cancer. According to recent discoveries, certified cell lines retain most genetic properties of the original tumor and mimic its microenvironment. On the other hand, modern technologies allowing the deepest level of detail in omics landscapes have shown significant differences even between samples of the same cell line due to cross- and mycoplasma infection. This and other observations suggest that, in some cases, cell cultures are not suitable as cancer models, with limited predictive value for the effectiveness of new treatments. HepG2 is a popular hepatic cell line. It is used in a wide range of studies, from the oncogenesis to the cytotoxicity of substances on the liver. In this regard, we set out to collect up-to-date information on the HepG2 cell line to assess whether the level of heterogeneity of the cell line allows in vitro biomedical studies as a model with guaranteed production and quality.

[PCR analysis of the expression of chromosome 18 genes in human liver tissue: interindividual variability]

Using human chromosome 18 (Ch18) genes as an example, a PCR analysis of the interindividual variability of gene expression in liver tissue was performed. Although the quantitative profiles of the Ch18 transcriptome, expressed in the number of cDNA copies per single cell, showed a high degree of correlation between donors (Pearson correlation coefficients ranged from 0.963 to 0.966), the expression of the significant number of genes (from 13% to 19%, depending on the method of experimental data normalization) varied by more than 4-fold when comparing donors pairwise. At the same time, the proportion of differentially expressed genes increased with a decrease in the level of their expression. It is shown that the higher quantitative variability of low-abundance transcripts is mainly not technical, but biological. Bioinformatic analysis of the interindividual variability of the differential expression of chromosome 18 genes in human liver tissue did not reveal any statistically significant groups of genes related to certain biological processes that indicated a rather transient nature of the interindividual variability of their expression, probably reflecting the response of cells of an individual to specific external stimuli.

Oxford Nanopore MinION Direct RNA-Seq for Systems Biology

Long-read direct RNA sequencing developed by Oxford Nanopore Technologies (ONT) is quickly gaining popularity for transcriptome studies, while fast turnaround time and low cost make it an attractive instrument for clinical applications. There is a growing interest to utilize transcriptome data to unravel activated biological processes responsible for disease progression and response to therapies. This trend is of particular interest for precision medicine which aims at single-patient analysis. Here we evaluated whether gene abundances measured by MinION direct RNA sequencing are suited to produce robust estimates of pathway activation for single sample scoring methods. We performed multiple RNA-seq analyses for a single sample that originated from the HepG2 cell line, namely five ONT replicates, and three replicates using Illumina NovaSeq. Two pathway scoring methods were employed-ssGSEA and singscore. We estimated the ONT performance in terms of detected protein-coding genes and average pairwise correlation between pathway activation scores using an exhaustive computational scheme for all combinations of replicates. In brief, we found that at least two ONT replicates are required to obtain reproducible pathway scores for both algorithms. We hope that our findings may be of interest to researchers planning their ONT direct RNA-seq experiments.

Efficacy and Mechanism of Action of Marine Alkaloid 3,10-Dibromofascaplysin in Drug-Resistant Prostate Cancer Cells

Efficacy and mechanism of action of marine alkaloid 3,10-dibromofascaplysin (DBF) were investigated in human prostate cancer (PCa) cells harboring different levels of drug resistance. Anticancer activity was observed across all cell lines examined without signs of cross-resistance to androgen receptor targeting agents (ARTA) or taxane based chemotherapy. Kinome analysis followed by functional investigation identified JNK1/2 to be one of the molecular targets of DBF in 22Rv1 cells. In contrast, no activation of p38 and ERK1/2 MAPKs was observed. Inhibition of the drug-induced JNK1/2 activation or of the basal p38 activity resulted in increased cytotoxicity of DBF, whereas an active ERK1/2 was identified to be important for anticancer activity of the alkaloid. Synergistic effects of DBF were observed in combination with PARP-inhibitor olaparib most likely due to the induction of ROS production by the marine alkaloid. In addition, DBF intensified effects of platinum-based drugs cisplatin and carboplatin, and taxane derivatives docetaxel and cabazitaxel. Finally, DBF inhibited AR-signaling and resensitized AR-V7-positive 22Rv1 prostate cancer cells to enzalutamide, presumably due to AR-V7 down-regulation. These findings propose DBF to be a promising novel drug candidate for the treatment of human PCa regardless of resistance to standard therapy.

Methods of Computational Interactomics for Investigating Interactions of Human Proteoforms

Human genome contains ca. 20,000 protein-coding genes that could be translated into millions of unique protein species (proteoforms). Proteoforms coded by a single gene often have different functions, which implies different protein partners. By interacting with each other, proteoforms create a network reflecting the dynamics of cellular processes in an organism. Perturbations of protein-protein interactions change the network topology, which often triggers pathological processes. Studying proteoforms is a relatively new research area in proteomics, and this is why there are comparatively few experimental studies on the interaction of proteoforms. Bioinformatics tools can facilitate such studies by providing valuable complementary information to the experimental data and, in particular, expanding the possibilities of the studies of proteoform interactions.

[Medical subject headings for the scientific groups evolution analysis on the example of academician A.I. Archakov's scientific school]

This paper proposes a method of comparative analysis of scientific trajectories based on bibliographic profiles. The bibliographic profile ("meshprint") is a list of MeSH terms (key terms used to index articles in the PubMed), indicating the relative frequency of occurrence of each term in the scientist's articles. Comparison of personalized bibliographic profiles can be represented in the form of a semantic network, where the nodes are the names of scientists, and the relationships are proportional to the calculated measures of similarity of bibliographic profiles. The proposed method was used to analyze the semantic network of scientists united by the academic school of the academician A.I. Archakov. The results of the work allowed us to show the relationship between the scientific trajectories of one scientific school and to correlate the results with world trends.

Challenges of the Human Proteome Project: 10-Year Experience of the Russian Consortium

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 "Missing" Proteome: Undetected Proteins, Not-Translated Transcripts, and Untranscribed Genes

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.

200+ Protein Concentrations in Healthy Human Blood Plasma: Targeted Quantitative SRM SIS Screening of Chromosomes 18, 13, Y, and the Mitochondrial Chromosome Encoded Proteome

This work continues the series of the quantitative measurements of the proteins encoded by different chromosomes in the blood plasma of a healthy person. Selected Reaction Monitoring with Stable Isotope-labeled peptide Standards (SRM SIS) and a gene-centric approach, which is the basis for the implementation of the international Chromosome-centric Human Proteome Project (C-HPP), were applied for the quantitative measurement of proteins in human blood plasma. Analyses were carried out in the frame of C-HPP for each protein-coding gene of the four human chromosomes: 18, 13, Y, and mitochondrial. Concentrations of proteins encoded by 667 genes were measured in 54 blood plasma samples of the volunteers, whose health conditions were consistent with requirements for astronauts. The gene list included 276, 329, 47, and 15 genes of chromosomes 18, 13, Y, and the mitochondrial chromosome, respectively. This paper does not make claims about the detection of missing proteins. Only 205 proteins (30.7%) were detected in the samples. Of them, 84, 106, 10, and 5 belonged to chromosomes 18, 13, and Y and the mitochondrial chromosome, respectively. Each detected protein was found in at least one of the samples analyzed. The SRM SIS raw data are available in the ProteomeXchange repository (PXD004374, PASS01192).

Increased Sensitivity of Mass Spectrometry by Alkaline Two-Dimensional Liquid Chromatography: Deep Cover of the Human Proteome in Gene-Centric Mode

Currently, great interest is paid to the identification of "missing" proteins that have not been detected in any biological material at the protein level (PE1). In this paper, using the Universal Proteomic Standard sets 1 and 2 (UPS1 and UPS2, respectively) as an example, we characterized mass spectrometric approaches from the point of view of sensitivity (Sn), specificity (Sp), and accuracy (Ac). The aim of the paper was to show the utility of a mass spectra approach for protein detection. This sets consists of 48 high-purity human proteins without single aminoacid polymorphism (SAP) or post translational modification (PTM). The UPS1 set consists of the same 48 proteins at 5 pmols each, and in UPS2, proteins were grouped into 5 groups in accordance with their molar concentration, ranging from 10^-11 to 10^-6 M. Single peptides from the 92% and 96% of all sets of proteins could be detected in a pure solution of UPS2 and UPS1, respectively, by selected reaction monitoring with stable isotope-labeled standards (SRM-SIS). We also found that, in the presence of a biological matrix such as Escherichia coli extract or human blood plasma (HBP), SRM-SIS makes it possible to detect from 63% to 79% of proteins in the UPS2 set (sensitivity) with the highest specificity (∼100%) and an accuracy of 80% by increasing the sensitivity of shotgun and selected reaction monitoring combined with a stable-isotope-labeled peptide standard (SRM-SIS technology) by fractionating samples using reverse-phase liquid chromatography under alkaline conditions (2D-LC_alk). It is shown that this technique of sample fractionation allows the SRM-SIS to detect 98% of the single peptides from the proteins present in the pure solution of UPS2 (47 out of 48 proteins). When the extracts of E. coli or Pichia pastoris are added as biological matrixes to the UPS2, 46, and 45 out of 48 proteins (∼95%) can be detected, respectively, using the SRM-SIS combined with 2D-LC_alk. The combination of the 2D-LC_alk SRM-SIS and shotgun technologies allows us to increase the sensitivity up to 100% in the case of the proteins of the UPS2 set. The usage of that technology can be a solution for identifying the so-called "missing" proteins and, eventually, creating the deep proteome of a particular chromosome of tissue or organs. Experimental data have been deposited in the PeptideAtlas SRM Experiment Library with the dataset identifier PASS01192 and the PRIDE repository with the dataset identifier PXD007643.

[Proteoforms: Methods of Analysis and Clinical Prospects]

A critical analysis of proteomes provides a basis for understanding the operation of complex biochemical systems. A personalized approach to therapy takes into account biological uniqueness of each patient at genome, transcriptome, and proteome levels, and is a priority area in molecular medicine. The identification of proteoforms, which have dramatic impact on the phenotype of a disease, is a fundamental task of personal molecular profiling. Considerable progress of proteomic approaches presented new avenues for accurate, specific, and high-performance protein analysis. Thus, the identification of new efficient bio-markers can be expected based on studies of aberrant proteoforms associated with various diseases.

The Gene-Centric Content Management System and Its Application for Cognitive Proteomics

The Human Proteome Project is moving into the next phase of creating and/or reconsidering the functional annotations of proteins using the chromosome-centric paradigm. This challenge cannot be solved exclusively using automated means, but rather requires human intelligence for interpreting the combined data. To foster the integration between human cognition and post-genome array a number of specific tools were recently developed, among them CAPER, GenomewidePDB, and The Proteome Browser (TPB). For the purpose of tackling the task of protein functional annotating the Gene-Centric Content Management System (GenoCMS) was expanded with new features. The goal was to enable bioinformaticans to develop self-made applications and to position these applets within the generalized informational canvas supported by GenoCMS. We report the results of GenoCMS-enabled integration of the concordant informational flows in the chromosome-centric framework of the human chromosome 18 project. The workflow described in the article can be scaled to other human chromosomes, and also supplemented with new tracks created by the user. The GenoCMS is an example of a project-oriented informational system, which are important for public data sharing.

[Multiomics study of HepG2 cell line proteome]

Current proteomic studies are generally focused on the most abundant proteoforms encoded by canonical nucleic sequences. Transcriptomic and proteomic data, accumulated in a variety of postgenome sources and coupled with state-of-art analytical technologies, allow to start the identification of aberrant (non-canonical) proteoforms. The main sources of aberrant proteoforms are alternative splicing, single nucleotide polymorphism, and post-translational modifications. The aim of this work was to estimate the heterogeneity of HepG2 proteome. We suggested multiomics approach, which combines transcriptomic (RNAseq) and proteomic (2DE-MS/MS) methods, as a promising strategy to explore the proteome.

[Proteomic profiling of HaCaT keratinocytes exposed to skin damaging detergents]

The effects of sodium dodecyl sulfate (25 mg/ml) and Triton X-100 (12.5 mg/ml and 25 mg/ml) on the HaCaT immortalized keratinocytes exposed to these surfactants for 48 h were studied. Using shotgun proteomics, a comparative analysis of the proteomic profiles of control and experimental cells after surfactants exposure was carried out. 260 common proteins were identified in control and experimental cells; 33 proteins were found in cells exposed to all three treatments, but not in control cells. These 33 proteins apparently reflect a nonspecific (universal) response of cells to toxic damage by the surfactants. These proteins are associated with activation of cell proliferation, changes in the functional activity of their ER and mitochondria, increased mRNA stability and activation of protein degradation processes in the cells. The possibility of using these proteins as a nonspecific parameter of cell response to cytotoxic damage is discussed. The mass spectrometry proteomics data ("raw", "mgf" and "xml" files) have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD007789 and PXD007776.

Why Are the Correlations between mRNA and Protein Levels so Low among the 275 Predicted Protein-Coding Genes on Human Chromosome 18?

In this work targeted (selected reaction monitoring, SRM, PASSEL: PASS00697) and panoramic (shotgun LC-MS/MS, PRIDE: PXD00244) mass-spectrometric methods as well as transcriptomic analysis of the same samples using RNA-Seq and PCR methods (SRA experiment IDs: SRX341198, SRX267708, SRX395473, SRX390071) were applied for quantification of chromosome 18 encoded transcripts and proteins in human liver and HepG2 cells. The obtained data was used for the estimation of quantitative mRNA-protein ratios for the 275 genes of the selected chromosome in the selected tissues. The impact of methodological limitations of existing analytical proteomic methods on gene-specific mRNA-protein ratios and possible ways of overcoming these limitations for detection of missing proteins are also discussed.

Changes in the Proteome of HaCaT Keratinocytes Induced by Cytotoxic Substance Triton X-100

Changes in the proteome of keratinocytes of immortalized HaCaT line exposed to cytotoxic substance Triton X-100 in concentrations of 12.5 and 25 μg/ml were studied by liquid chromatography combined with mass spectrometry. The appearance of proteins involved in the regulation of mitosis, RNA stability, and catabolic processes were detected; the number of apoptosis-associated proteins increased, while the number of proteins involved in differentiation and energy metabolism of keratinocytes decreased.

State of the Art of Chromosome 18-Centric HPP in 2016: Transcriptome and Proteome Profiling of Liver Tissue and HepG2 Cells

A gene-centric approach was applied for a large-scale study of expression products of a single chromosome. Transcriptome profiling of liver tissue and HepG2 cell line was independently performed using two RNA-Seq platforms (SOLiD and Illumina) and also by Droplet Digital PCR (ddPCR) and quantitative RT-PCR. Proteome profiling was performed using shotgun LC-MS/MS as well as selected reaction monitoring with stable isotope-labeled standards (SRM/SIS) for liver tissue and HepG2 cells. On the basis of SRM/SIS measurements, protein copy numbers were estimated for the Chromosome 18 (Chr 18) encoded proteins in the selected types of biological material. These values were compared with expression levels of corresponding mRNA. As a result, we obtained information about 158 and 142 transcripts for HepG2 cell line and liver tissue, respectively. SRM/SIS measurements and shotgun LC-MS/MS allowed us to detect 91 Chr 18-encoded proteins in total, while an intersection between the HepG2 cell line and liver tissue proteomes was ∼66%. In total, there were 16 proteins specifically observed in HepG2 cell line, while 15 proteins were found solely in the liver tissue. Comparison between proteome and transcriptome revealed a poor correlation (R² ≈ 0.1) between corresponding mRNA and protein expression levels. The SRM and shotgun data sets (obtained during 2015-2016) are available in PASSEL (PASS00697) and ProteomeExchange/PRIDE (PXD004407). All measurements were also uploaded into the in-house Chr 18 Knowledgebase at http://kb18.ru/protein/matrix/416126 .

The Size of the Human Proteome: The Width and Depth

This work discusses bioinformatics and experimental approaches to explore the human proteome, a constellation of proteins expressed in different tissues and organs. As the human proteome is not a static entity, it seems necessary to estimate the number of different protein species (proteoforms) and measure the number of copies of the same protein in a specific tissue. Here, meta-analysis of neXtProt knowledge base is proposed for theoretical prediction of the number of different proteoforms that arise from alternative splicing (AS), single amino acid polymorphisms (SAPs), and posttranslational modifications (PTMs). Three possible cases are considered: (1) PTMs and SAPs appear exclusively in the canonical sequences of proteins, but not in splice variants; (2) PTMs and SAPs can occur in both proteins encoded by canonical sequences and in splice variants; (3) all modification types (AS, SAP, and PTM) occur as independent events. Experimental validation of proteoforms is limited by the analytical sensitivity of proteomic technology. A bell-shaped distribution histogram was generated for proteins encoded by a single chromosome, with the estimation of copy numbers in plasma, liver, and HepG2 cell line. The proposed metabioinformatics approaches can be used for estimation of the number of different proteoforms for any group of protein-coding genes.

Chromosome 18 transcriptoproteome of liver tissue and HepG2 cells and targeted proteome mapping in depleted plasma: update 2013

We report the results obtained in 2012-2013 by the Russian Consortium for the Chromosome-centric Human Proteome Project (C-HPP). The main scope of this work was the transcriptome profiling of genes on human chromosome 18 (Chr 18), as well as their encoded proteome, from three types of biomaterials: liver tissue, the hepatocellular carcinoma-derived cell line HepG2, and blood plasma. The transcriptome profiling for liver tissue was independently performed using two RNaseq platforms (SOLiD and Illumina) and also by droplet digital PCR (ddPCR) and quantitative RT-PCR. The proteome profiling of Chr 18 was accomplished by quantitatively measuring protein copy numbers in the three types of biomaterial (the lowest protein concentration measured was 10(-13) M) using selected reaction monitoring (SRM). In total, protein copy numbers were estimated for 228 master proteins, including quantitative data on 164 proteins in plasma, 171 in the HepG2 cell line, and 186 in liver tissue. Most proteins were present in plasma at 10(8) copies/μL, while the median abundance was 10(4) and 10(5) protein copies per cell in HepG2 cells and liver tissue, respectively. In summary, for liver tissue and HepG2 cells a "transcriptoproteome" was produced that reflects the relationship between transcript and protein copy numbers of the genes on Chr 18. The quantitative data acquired by RNaseq, PCR, and SRM were uploaded into the "Update_2013" data set of our knowledgebase (www.kb18.ru) and investigated for linear correlations.

Gene-centric content management system

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.

[Protocols of proteins interactomics: molecular fishing on optical chips and magnetic nanoparticles]

Now it is absolutely clear, that the majority of proteins in living systems function due to interaction with each other in stable or dynamic proteins complexes. Therefore necessity of deeper studies of proteins functions causes expansion of protein-protein interaction research. In the present review the brief description and comparative estimation of experimental methods and protocols of protein interactomics, based on technology of molecular fishing on an optical chips and paramagnetic nanoparticles is given.

Chromosome 18 transcriptome profiling and targeted proteome mapping in depleted plasma, liver tissue and HepG2 cells

The final goal of the Russian part of the Chromosome-centric Human Proteome Project (C-HPP) was established as the analysis of the chromosome 18 (Chr 18) protein complement in plasma, liver tissue and HepG2 cells with the sensitivity of 10(-18) M. Using SRM, we have recently targeted 277 Chr 18 proteins in plasma, liver, and HepG2 cells. On the basis of the results of the survey, the SRM assays were drafted for 250 proteins: 41 proteins were found only in the liver tissue, 82 proteins were specifically detected in depleted plasma, and 127 proteins were mapped in both samples. The targeted analysis of HepG2 cells was carried out for 49 proteins; 41 of them were successfully registered using ordinary SRM and 5 additional proteins were registered using a combination of irreversible binding of proteins on CN-Br Sepharose 4B with SRM. Transcriptome profiling of HepG2 cells performed by RNAseq and RT-PCR has shown a significant correlation (r = 0.78) for 42 gene transcripts. A pilot affinity-based interactome analysis was performed for cytochrome b5 using analytical and preparative optical biosensor fishing followed by MS analysis of the fished proteins. All of the data on the proteome complement of the Chr 18 have been integrated into our gene-centric knowledgebase ( www.kb18.ru ).

A Statistical Analysis of Competitive Research Contracting in the Field of Life Sciences

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