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.

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.

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.

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.

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.

Background: Comparative transcriptomic analysis is a powerful approach for investigating the molecular mechanisms underlying various physiological and pathological processes, including liver disease. The liver is a vital organ with diverse functions, including metabolism and detoxification. In vitro models of liver cells, such as HepG2, Huh7, and Hep3B, have been widely used to study liver biology and pathology. However, there is limited information on the heterogeneity of these cell lines at the transcriptomic level. Objective: This study aimed to conduct a comparative transcriptomic analysis of three common liver cell lines (HepG2, Huh7, and Hep3B) using publicly available RNA-sequencing data. In addition, we compared these cell lines to primary hepatocytes, cells isolated directly from liver tissue and considered the gold standard for studying liver function and disease. Methods: Our study included sequencing data with the following criteria: total number of reads over 20,000,000, average read length of over 60 base pairs, Illumina sequencing, and non-treated cells. The data for the three cell lines were compiled: HepG2 (97 samples), Huh7 (39 samples), and Hep3B (16 samples). We performed differential gene expression analysis using the DESeq2 package, principal component analysis, hierarchical clustering on principal components, and correlation analysis to explore the heterogeneity within each cell line. Results: We identified numerous genes and pathways differentially expressed between HepG2, Huh7, and Hep3B, such as oxidative phosphorylation, cholesterol metabolism, and DNA damage. We report that the expression levels of important genes differ significantly between primary hepatocytes and liver cell lines. Conclusion: Our study provides new insights into the transcriptional heterogeneity of commonly used liver cell lines and highlights the importance of considering specific cell line. Consequently, transferring results without considering the heterogeneity of cell lines is impractical and may lead to inaccurate or distorted conclusions.