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.

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.

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.

Identification of Potential Therapeutic Targets on the Level of DNA/mRNAs, Proteins and Metabolites: A Systematic Mapping Review of Scientific Texts' Fragments from Open Targets.

Database records contain useful information, which is readily available, but, unfortunately, limited compared to the source (publications). Our study reviewed the text fragments supporting the association between the biological macromolecules and diseases from Open Targets to map them on the biological level of study (DNA/RNA, proteins, metabolites). We screened records using a dictionary containing terms related to the selected levels of study, reviewed 600 hits manually and used machine learning to classify 31,260 text fragments. Our results indicate that association studies between diseases and macromolecules conducted on the level of DNA and RNA prevail, followed by the studies on the level of proteins and metabolites. We conclude that there is a clear need to translate the knowledge from the DNA/RNA level to the evidence on the level of proteins and metabolites. Since genes and their transcripts rarely act in the cell by themselves, more direct evidence may be of greater value for basic and applied research.

The Knowns and Unknowns in Protein-Metabolite Interactions.

Increasing attention has been focused on the study of protein-metabolite interactions (PMI), which play a key role in regulating protein functions and directing an orchestra of cellular processes. The investigation of PMIs is complicated by the fact that many such interactions are extremely short-lived, which requires very high resolution in order to detect them. As in the case of protein-protein interactions, protein-metabolite interactions are still not clearly defined. Existing assays for detecting protein-metabolite interactions have an additional limitation in the form of a limited capacity to identify interacting metabolites. Thus, although recent advances in mass spectrometry allow the routine identification and quantification of thousands of proteins and metabolites today, they still need to be improved to provide a complete inventory of biological molecules, as well as all interactions between them. Multiomic studies aimed at deciphering the implementation of genetic information often end with the analysis of changes in metabolic pathways, as they constitute one of the most informative phenotypic layers. In this approach, the quantity and quality of knowledge about PMIs become vital to establishing the full scope of crosstalk between the proteome and the metabolome in a biological object of interest. In this review, we analyze the current state of investigation into the detection and annotation of protein-metabolite interactions, describe the recent progress in developing associated research methods, and attempt to deconstruct the very term "interaction" to advance the field of interactomics further.

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.

Food for Thought: Proteomics for Meat Safety.

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.

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.

Some Lessons Learned on the Impact of the Storage Conditions, Syringe Wash Solvent, and the Way of GC-MS Injection on the Reproducibility of Metabolomic Studies.

Metabolomics based on two-dimensional gas chromatography coupled with mass spectrometry is making high demands on accuracy at all stages of sample preparation, up to the storage and injection into the analytical system. In high sample flow conditions, good repeatability in peak areas and a list of detectable metabolites is sometimes challenging to obtain. In this research, we successfully obtained good repeatability for the peak areas of MSFTA-derivatives of 29 core blood plasma metabolites. Six different strategies of storage and injection were investigated and evaluated for the reproducibility of the obtained data. As the essential factors, we considered popular GC-MS syringe washing solvents (methanol and pyridine); storage conditions (freshly prepared samples and stored for 24 h in ambient temperature or in the refrigerator); scheme of injection (one injection per intact vial or three sequential injections per vial). Our GC×GC-MS results demonstrated that the usage of pyridine as a syringe wash solvent and triple injecting the sample from the same vial was the most appropriate for minimizing the coefficient of variation (CV) of the results obtained (in general, <10%). The prolonged storage of samples does not have a noticeable effect on the change in the areas of chromatographic peaks of metabolites, although it reduces CV in some cases. These storage and injection recommendations can be used in future study protocols for the GC×GC-MS analysis of blood plasma.

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.

The Effect of Whole Blood and Bone Marrow with the Addition of Pyrimidine-2,4(1h,3h)-dione Thietanyl Derivatives on Free Radical Oxidation.

BACKGROUND: It is relevant to study the general patterns and identify non-specific mechanisms of body protective and adaptive reactions violation, which can lead to the various pathological processes and develop principles for the correction of these disorders. One of the therapy and prevention directions is the search for new medicines. In recent years, new derivatives of pyrimidine bases have been synthesized and studied. Pyrimidine-based medicines have a membrane-stabilizing and immunomodulatory effect and can normalize metabolic disorders and increase the oxidative activity of leukocytes. Disruption of the free radical oxidation processes, the generation of reactive oxygen species and lipid peroxidation, including in whole blood and bone marrow, has gained importance in recent years. METHODS: Each reaction was monitored by thin layer chromatography. 1H, 13C, and 15N NMR spectra were recorded (chemical shifts were expressed as δ-values). We studied the effect of 6-methyl-3-(thietan- 3-yl)pyrimidine-2,4(1H,3H)-dione on the generation of reactive oxygen species (ROS) in the whole blood and bone marrow using the study of whole blood spontaneous and stimulated chemiluminescence (CL). CL methods make it possible to quickly and easily assess the studied material (whole blood, bone marrow) effect on free radical oxidation. Using CL methods, it is possible to reveal the presence of medicines' pro- or antioxidant properties, opening up new possibilities in the search for substances with antioxidant properties and comparing their activity. RESULTS: Alkylation of 6-methylpyrimidine-2,4(1H,3H)-dione by 2-chloromethylthiirane in protic solvents in the presence of alkali leads to the formation of an N-thietane derivative. NMR spectroscopy showed that 6-methylpyrimidine-2,4(1H,3H)-dione was alkylated at position 3. The oxidation reactions of N-(thietan-3-yl)pyrimidine-2,4(1H,3H)-dione were studied, and it was determined that, depending on the excess of the oxidizing agent and the duration of the process, N-(1-oxothietan-3-yl)- or N-(1,1-- dioxothietan-3-yl)pyrimidine-2,4(1H,3H)-diones were formed. The effects of free radical oxidation processes of new biologically active pyrimidine-2,4(1H,3H)-diones were studied. CONCLUSION: New pyrimidine-2,4(1H,3H)-diones increase the general adaptive capabilities of the body and have protective effects in extreme conditions.

MS Identification of Blood Plasma Proteins Concentrated on a Photocrosslinker-Modified Surface.

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 (102X, 104X, and 106X). 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.

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.

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.

Evolution of Protein Functional Annotation: Text Mining Study.

Within the Human Proteome Project initiative framework for creating functional annotations of uPE1 proteins, the neXt-CP50 Challenge was launched in 2018. In analogy with the missing-protein challenge, each command deciphers the functional features of the proteins in the chromosome-centric mode. However, the neXt-CP50 Challenge is more complicated than the missing-protein challenge: the approaches and methods for solving the problem are clear, but neither the concept of protein function nor specific experimental and/or bioinformatics protocols have been standardized to address it. We proposed using a retrospective analysis of the key HPP repository, the neXtProt database, to identify the most frequently used experimental and bioinformatic methods for analyzing protein functions, and the dynamics of accumulation of functional annotations. It has been shown that the dynamics of the increase in the number of proteins with known functions are greater than the progress made in the experimental confirmation of the existence of questionable proteins in the framework of the missing-protein challenge. At the same time, the functional annotation is based on the guilty-by-association postulate, according to which, based on large-scale experiments on API-MS and Y2H, proteins with unknown functions are most likely mapped through "handshakes" to biochemical processes.

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.

Early viral versus late antibiotic-associated diarrhea in novel coronavirus infection.

ABSTRACT: Diarrhea is one of the manifestations of the novel coronavirus disease (COVID-19), but it also develops as a complication of massive antibiotic therapy in this disease. This study aimed to compare these types of diarrhea.We included patients with COVID-19 in a cohort study and excluded patients with chronic diarrhea, laxative use, and those who died during the first day of hospitalization.There were 89 (9.3%), 161 (16.7%), and 731 (75.7%) patients with early viral, late antibiotic-associated, and without diarrhea, respectively. Late diarrhea lasted longer (6 [4-10] vs 5 [3-7] days, P < .001) and was more severe. Clostridioides difficile was found in 70.5% of tested patients with late diarrhea and in none with early diarrhea. Presence of late diarrhea was associated with an increased risk of death after 20 days of disease (P = .009; hazard ratio = 4.7). Patients with late diarrhea had a longer hospital stay and total disease duration, and a higher proportion of these patients required intensive care unit admission. Oral amoxicillin/clavulanate (odds ratio [OR] = 2.23), oral clarithromycin (OR = 3.79), and glucocorticoids (OR = 4.41) use was a risk factor for the development of late diarrhea, while ceftriaxone use (OR = 0.35) had a protective effect. Before the development of late diarrhea, decrease in C-reactive protein levels and increase in lymphocyte count stopped but the white blood cell and neutrophil count increased. An increase in neutrophils by >0.6 × 109 cells/L predicted the development of late diarrhea in the coming days (sensitivity 82.0%, specificity 70.8%, area under the curve = 0.791 [0.710-0.872]).Diarrhea in COVID-19 is heterogeneous, and different types of diarrhea require different management.

Human Chr18 transcriptome dataset combined from the Illumina HiSeq, ONT MinION, and qPCR data.

The chromosome-centric dataset was created by applying several technologies of transcriptome profiling. The described dataset is available at NCBI repository (BioProject ID PRJNA635536). The dataset referred to the same type of tissue, cell lines, transcriptome sequencing technologies, and was accomplished in a period of 8 years (the first data were obtained in 2013 while the last ones - in 2020). The high-throughput sequencing technologies were employed along with the quantitative PCR (qPCR) approach, for data generation using the gene expression level assessment. qPCR was performed for a limited group of genes, encoded on human chromosome 18, for the Russian part of the Chromosome-Centric Human Proteome Project. The data of high-throughput sequencing are provided as Excel spreadsheets, where the data on FPKM and TMP values were evaluated for the whole transcriptome with both Illumina HiSeq and Oxford Nanopore Technologies MinION sequencing.

Does Proteomic Mirror Reflect Clinical Characteristics of Obesity?

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.

Defining Blood Plasma and Serum Metabolome by GC-MS.

Metabolomics uses advanced analytical chemistry methods to analyze metabolites in biological samples. The most intensively studied samples are blood and its liquid components: plasma and serum. Armed with advanced equipment and progressive software solutions, the scientific community has shown that small molecules' roles in living systems are not limited to traditional "building blocks" or "just fuel" for cellular energy. As a result, the conclusions based on studying the metabolome are finding practical reflection in molecular medicine and a better understanding of fundamental biochemical processes in living systems. This review is not a detailed protocol of metabolomic analysis. However, it should support the reader with information about the achievements in the whole process of metabolic exploration of human plasma and serum using mass spectrometry combined with gas chromatography.