The Size of the Human Proteome: The Width and Depth

Alternative polyadenylation alters protein dosage by switching between intronic and 3'UTR sites

Alternative polyadenylation (APA) creates distinct transcripts from the same gene by cleaving the pre-mRNA at poly(A) sites that can lie within the 3' untranslated region (3'UTR), introns, or exons. Most studies focus on APA within the 3'UTR; however, here, we show that CPSF6 insufficiency alters protein levels and causes a developmental syndrome by deregulating APA throughout the transcript. In neonatal humans and zebrafish larvae, CPSF6 insufficiency shifts poly(A) site usage between the 3'UTR and internal sites in a pathway-specific manner. Genes associated with neuronal function undergo mostly intronic APA, reducing their expression, while genes associated with heart and skeletal function mostly undergo 3'UTR APA and are up-regulated. This suggests that, under healthy conditions, cells toggle between internal and 3'UTR APA to modulate protein expression.

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.

Gene-centric coverage of the human liver transcriptome: QPCR, Illumina, and Oxford Nanopore RNA-Seq

It has been shown that the best coverage of the HepG2 cell line transcriptome encoded by genes of a single chromosome, chromosome 18, is achieved by a combination of two sequencing platforms, Illumina RNA-Seq and Oxford Nanopore Technologies (ONT), using cut-off levels of FPKM > 0 and TPM > 0, respectively. In this study, we investigated the extent to which the combination of these transcriptomic analysis methods makes it possible to achieve a high coverage of the transcriptome encoded by the genes of other human chromosomes. A comparative analysis of transcriptome coverage for various types of biological material was carried out, and the HepG2 cell line transcriptome was compared with the transcriptome of liver tissue cells. In addition, the contribution of variability in the coverage of expressed genes in human transcriptomes to the creation of a draft human transcriptome was evaluated. For human liver tissues, ONT makes an extremely insignificant contribution to the overall coverage of the transcriptome. Thus, to ensure maximum coverage of the liver tissue transcriptome, it is sufficient to apply only one technology: Illumina RNA-Seq (FPKM > 0).

Investigation of native and aggregated therapeutic proteins in human plasma with asymmetrical flow field-flow fractionation and mass spectrometry

Physiochemical degradation of therapeutic proteins in vivo during plasma circulation after administration can have a detrimental effect on their efficacy and safety profile. During drug product development, in vivo animal studies are necessary to explore in vivo protein behaviour. However, these studies are very demanding and expensive, and the industry is working to decrease the number of in vivo studies. Consequently, there is considerable interest in the development of methods to pre-screen the behaviour of therapeutic proteins in vivo using in vitro analysis. In this work, asymmetrical flow field-flow fractionation (AF4) and liquid chromatography-mass spectrometry (LC-MS) were combined to develop a novel analytical methodology for predicting the behaviour of therapeutic proteins in vivo. The method was tested with two proteins, a monoclonal antibody and a serum albumin binding affibody. After incubation of the proteins in plasma, the method was successfully used to investigate and quantify serum albumin binding, analyse changes in monoclonal antibody size, and identify and quantify monoclonal antibody aggregates.

The Utilization of Tunable Transducer Elements Formed by the Manipulation of Magnetic Beads with Different Sizes via Optically Induced Dielectrophoresis (ODEP) for High Signal-to-Noise Ratios (SNRs) and Multiplex Fluorescence-Based Biosensing Applications

Magnetic beads improve biosensing performance by means of their small volume and controllability by magnetic force. In this study, a new technique composed of optically induced dielectrodphoresis (ODEP) manipulation and image processing was used to enhance the signal-to-noise ratio of the fluorescence for stained magnetic beads. According to natural advantages of size-dependent particle isolation by ODEP manipulation, biomarkers in clinical samples can be easily separated by different sizes of magnetic beads with corresponding captured antibodies, and rapidly distinguished by separated location of immunofluorescence. To verify the feasibility of the concept, magnetic beads with three different diameters, including 21.8, 8.7, and 4.2 μm, were easily separated and collected into specific patterns in the defined target zone treated as three dynamic transducer elements to evaluate fluorescence results. In magnetic beads with diameter of 4.2 μm, the lowest signal-to-noise ratio between stained and nonstained magnetic beads was 3.5. With the help of ODEP accumulation and detection threshold setting of 32, the signal-to-noise ratio was increased to 77.4, which makes this method more reliable. With the further optimization of specific antibodies immobilized on different-size magnetic beads in the future, this platform can be a potential candidate for a high-efficiency sensor array in clinical applications.

Proteomic Applications in Aquatic Environment Studies

Genome determines the unique individualities of organisms; however, proteins play significant roles in the generation of the colorful life forms below water. Aquatic systems are usually complex and multifaceted and can take on unique modifications and adaptations to environmental changes by altering proteins at the cellular level. Proteomics is an essential strategy for exploring aquatic ecosystems due to the diverse involvement of proteins, proteoforms, and their complexity in basic and advanced cellular functions. Proteomics can expedite the analysis of molecular mechanisms underlying biological processes in an aquatic environment. Previous proteomic studies on aquatic environments have mainly focused on pollution assessments, ecotoxicology, their role in the food industry, and extraction and identification of natural products. Aquatic protein biomarkers have been comprehensively reported and are currently extensively applied in the pharmaceutical and medical industries. Cellular- and molecular-level responses of organisms can be used as indicators of environmental changes and stresses. Conversely, environmental changes are expedient in predicting aquatic health and productivity, which are crucial for ecosystem management and conservation. Recent advances in proteomics have contributed to the development of sustainable aquaculture, seafood safety, and high aquatic food production. Proteomic approaches have expanded to other aspects of the aquatic environment, such as protein fingerprinting for species identification. In this review, we encapsulated current proteomic applications and evaluated the potential strengths, weaknesses, opportunities, and threats of proteomics for future aquatic environmental studies. The review identifies both pros and cons of aquatic proteomics and projects potential challenges and recommendations. We postulate that proteomics is an emerging, powerful, and integrated omics approach for aquatic environmental studies.

On the Prediction of In Vitro Arginine Glycation of Short Peptides Using Artificial Neural Networks

One of the hallmarks of diabetes is an increased modification of cellular proteins. The most prominent type of modification stems from the reaction of methylglyoxal with arginine and lysine residues, leading to structural and functional impairments of target proteins. For lysine glycation, several algorithms allow a prediction of occurrence; thus, making it possible to pinpoint likely targets. However, according to our knowledge, no approaches have been published for predicting the likelihood of arginine glycation. There are indications that arginine and not lysine is the most prominent target for the toxic dialdehyde. One of the reasons why there is no arginine glycation predictor is the limited availability of quantitative data. Here, we used a recently published high-quality dataset of arginine modification probabilities to employ an artificial neural network strategy. Despite the limited data availability, our results achieve an accuracy of about 75% of correctly predicting the exact value of the glycation probability of an arginine-containing peptide without setting thresholds upon whether it is decided if a given arginine is modified or not. This contribution suggests a solution for predicting arginine glycation of short peptides.

A second chance for protein targeting/folding: Ubiquitination and deubiquitination of nascent proteins

Molecular chaperones in cells constantly monitor and bind to exposed hydrophobicity in newly synthesized proteins and assist them in folding or targeting to cellular membranes for insertion. However, proteins can be misfolded or mistargeted, which often causes hydrophobic amino acids to be exposed to the aqueous cytosol. Again, chaperones recognize exposed hydrophobicity in these proteins to prevent nonspecific interactions and aggregation, which are harmful to cells. The chaperone-bound misfolded proteins are then decorated with ubiquitin chains denoting them for proteasomal degradation. It remains enigmatic how molecular chaperones can mediate both maturation of nascent proteins and ubiquitination of misfolded proteins solely based on their exposed hydrophobic signals. In this review, we propose a dynamic ubiquitination and deubiquitination model in which ubiquitination of newly synthesized proteins serves as a "fix me" signal for either refolding of soluble proteins or retargeting of membrane proteins with the help of chaperones and deubiquitinases. Such a model would provide additional time for aberrant nascent proteins to fold or route for membrane insertion, thus avoiding excessive protein degradation and saving cellular energy spent on protein synthesis. Also see the video abstract here: https://youtu.be/gkElfmqaKG4.

Nanoparticle Biomolecular Corona-Based Enrichment of Plasma Glycoproteins for N-Glycan Profiling and Application in Biomarker Discovery

Biomolecular corona formation has emerged as a recurring and important phenomenon in nanomedicine that has been investigated for potential applications in disease diagnosis. In this study, we have combined the "personalized protein corona" with the N-glycosylation profiling that has recently gained considerable interest in human plasma biomarker discovery as a powerful early warning diagnostic and patient stratification tool. We envisioned that the protein corona formation could be exploited as an enrichment step that is critically important in both proteomic and proteoglycomic workflows. By using silica nanoparticles, plasma fibrinogen was enriched to a level in which its proteomic and glycomic "fingerprints" could be traced with confidence. Despite being a more simplified glycan profile compared to full plasma, the corona glycan profile revealed a fibrinogen-derived glycan peak that was found to potentially distinguish lung cancer patients from controls in a pilot study.

Using genetic programming to predict and optimize protein function

Protein engineers conventionally use tools such as Directed Evolution to find new proteins with better functionalities and traits. More recently, computational techniques and especially machine learning approaches have been recruited to assist Directed Evolution, showing promising results. In this article, we propose POET, a computational Genetic Programming tool based on evolutionary computation methods to enhance screening and mutagenesis in Directed Evolution and help protein engineers to find proteins that have better functionality. As a proof-of-concept, we use peptides that generate MRI contrast detected by the Chemical Exchange Saturation Transfer contrast mechanism. The evolutionary methods used in POET are described, and the performance of POET in different epochs of our experiments with Chemical Exchange Saturation Transfer contrast are studied. Our results indicate that a computational modeling tool like POET can help to find peptides with 400% better functionality than used before.

The emerging role of mass spectrometry-based proteomics in drug discovery

Proteins are the main targets of most drugs; however, system-wide methods to monitor protein activity and function are still underused in drug discovery. Novel biochemical approaches, in combination with recent developments in mass spectrometry-based proteomics instrumentation and data analysis pipelines, have now enabled the dissection of disease phenotypes and their modulation by bioactive molecules at unprecedented resolution and dimensionality. In this Review, we describe proteomics and chemoproteomics approaches for target identification and validation, as well as for identification of safety hazards. We discuss innovative strategies in early-stage drug discovery in which proteomics approaches generate unique insights, such as targeted protein degradation and the use of reactive fragments, and provide guidance for experimental strategies crucial for success.

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.

Cataloguing the proteome: Current developments in single-molecule protein sequencing

The cellular proteome is complex and dynamic, with proteins playing a critical role in cell-level biological processes that contribute to homeostasis, stimuli response, and disease pathology, among others. As such, protein analysis and characterization are of extreme importance in both research and clinical settings. In the last few decades, most proteomics analysis has relied on mass spectrometry, affinity reagents, or some combination thereof. However, these techniques are limited by their requirements for large sample amounts, low resolution, and insufficient dynamic range, making them largely insufficient for the characterization of proteins in low-abundance or single-cell proteomic analysis. Despite unique technical challenges, several single-molecule protein sequencing (SMPS) technologies have been proposed in recent years to address these issues. In this review, we outline several approaches to SMPS technologies and discuss their advantages, limitations, and potential contributions toward an accurate, sensitive, and high-throughput platform.

Instabilities of complex fluids with partially structured and partially random interactions

We develop a theory for thermodynamic instabilities of complex fluids composed of many interacting chemical species organised in families. This model includes partially structured and partially random interactions and can be solved exactly using tools from random matrix theory. The model exhibits three kinds of fluid instabilities: one in which the species form a condensate with a local density that depends on their family (family condensation); one in which species demix in two phases depending on their family (family demixing); and one in which species demix in a random manner irrespective of their family (random demixing). We determine the critical spinodal density of the three types of instabilities and find that the critical spinodal density is finite for both family condensation and family demixing, while for random demixing the critical spinodal density grows as the square root of the number of species. We use the developed framework to describe phase-separation instability of the cytoplasm induced by a change in pH.

Yuel: Improving the Generalizability of Structure-Free Compound-Protein Interaction Prediction

Predicting binding affinities between small molecules and the protein target is at the core of computational drug screening and drug target identification. Deep learning-based approaches have recently been adapted to predict binding affinities and they claim to achieve high prediction accuracy in their tests; we show that these approaches do not generalize, that is, they fail to predict interactions between unknown proteins and unknown small molecules. To address these shortcomings, we develop a new compound-protein interaction predictor, Yuel, which predicts compound-protein interactions with a higher generalizability than the existing methods. Upon comprehensive tests on various data sets, we find that out of all the deep-learning approaches surveyed, Yuel manifests the best ability to predict interactions between unknown compounds and unknown proteins.

Nanopore-Based Protein Identification

The implementation of a reliable, rapid, inexpensive, and simple method for whole-proteome identification would greatly benefit cell biology research and clinical medicine. Proteins are currently identified by cleaving them with proteases, detecting the polypeptide fragments with mass spectrometry, and mapping the latter to sequences in genomic/proteomic databases. Here, we demonstrate that the polypeptide fragments can instead be detected and classified at the single-molecule limit using a nanometer-scale pore formed by the protein aerolysin. Specifically, three different water-soluble proteins treated with the same protease, trypsin, produce different polypeptide fragments defined by the degree by which the latter reduce the nanopore's ionic current. The fragments identified with the aerolysin nanopore are consistent with the predicted fragments that trypsin could produce.

Proximity labeling for investigating protein-protein interactions

The study of protein complexes and protein-protein interactions is of great importance due to their fundamental roles in cellular function. Proximity labeling, often coupled with mass spectrometry, has become a powerful and versatile tool for studying protein-protein interactions by enriching and identifying proteins in the vicinity of a specified protein-of-interest. Here, we describe and compare traditional approaches to investigate protein-protein interactions to current day state-of-the-art proximity labeling methods. We focus on the wide array of proximity labeling strategies and underscore studies using diverse model systems to address numerous biological questions. In addition, we highlight current advances in mass spectrometry-based technology that exhibit promise in improving the depth and breadth of the data acquired in proximity labeling experiments. In all, we show the diversity of proximity labeling strategies and emphasize the broad range of applications and biological inquiries that can be addressed using this technology.

Anti-Coagulant and Antimicrobial Recombinant Heparin-Binding Major Ampullate Spidroin 2 (MaSp2) Silk Protein

Governed by established structure–property relationships, peptide motifs comprising major ampullate spider silk confer a balance of strength and extensibility. Other biologically inspired small peptide motifs correlated to specific functionalities can be combined within these units to create designer silk materials with new hybrid properties. In this study, a small basic peptide, (ARKKAAKA) known to both bind heparin and mimic an antimicrobial peptide, was genetically linked to a protease-resistant, mechanically robust silk-like peptide, MaSp2. Purified fusion proteins (four silk domains and four heparin-binding peptide repeats) were expressed in E. coli. Successful fusion of a MaSp2 spider silk peptide with the heparin-binding motif was shown using a variety of analytical assays. The ability of the fusion peptide to bind heparin was assessed with ELISA and was further tested for its anticoagulant property using aPTT assay. Its intrinsic property to inhibit bacterial growth was evaluated using zone of inhibition and crystal violet (CV) assays. Using this strategy, we were able to link the two types of genetic motifs to create a designer silk-like protein with improved hemocompatibility and antimicrobial properties.

Label-Free Physical Techniques and Methodologies for Proteins Detection in Microfluidic Biosensor Structures

Proteins in biological fluids (blood, urine, cerebrospinal fluid) are important biomarkers of various pathological conditions. Protein biomarkers detection and quantification have been proven to be an indispensable diagnostic tool in clinical practice. There is a growing tendency towards using portable diagnostic biosensor devices for point-of-care (POC) analysis based on microfluidic technology as an alternative to conventional laboratory protein assays. In contrast to universally accepted analytical methods involving protein labeling, label-free approaches often allow the development of biosensors with minimal requirements for sample preparation by omitting expensive labelling reagents. The aim of the present work is to review the variety of physical label-free techniques of protein detection and characterization which are suitable for application in micro-fluidic structures and analyze the technological and material aspects of label-free biosensors that implement these methods. The most widely used optical and impedance spectroscopy techniques: absorption, fluorescence, surface plasmon resonance, Raman scattering, and interferometry, as well as new trends in photonics are reviewed. The challenges of materials selection, surfaces tailoring in microfluidic structures, and enhancement of the sensitivity and miniaturization of biosensor systems are discussed. The review provides an overview for current advances and future trends in microfluidics integrated technologies for label-free protein biomarkers detection and discusses existing challenges and a way towards novel solutions.

Recent Developments in Clinical Plasma Proteomics—Applied to Cardiovascular Research

The human plasma proteome mirrors the physiological state of the cardiovascular system, a fact that has been used to analyze plasma biomarkers in routine analysis for the diagnosis and monitoring of cardiovascular diseases for decades. These biomarkers address, however, only a very limited subset of cardiovascular diseases, such as acute myocardial infarct or acute deep vein thrombosis, and clinical plasma biomarkers for the diagnosis and stratification cardiovascular diseases that are growing in incidence, such as heart failure and abdominal aortic aneurysm, do not exist and are urgently needed. The discovery of novel biomarkers in plasma has been hindered by the complexity of the human plasma proteome that again transforms into an extreme analytical complexity when it comes to the discovery of novel plasma biomarkers. This complexity is, however, addressed by recent achievements in technologies for analyzing the human plasma proteome, thereby facilitating the possibility for novel biomarker discoveries. The aims of this article is to provide an overview of the recent achievements in technologies for proteomic analysis of the human plasma proteome and their applications in cardiovascular medicine.

Phylogeny, Structure, Functions, and Role of AIRE in the Formation of T-Cell Subsets

It is well known that the most important feature of adaptive immunity is the specificity that provides highly precise recognition of the self, altered-self, and non-self. Due to the high specificity of antigen recognition, the adaptive immune system participates in the maintenance of genetic homeostasis, supports multicellularity, and protects an organism from different pathogens at a qualitatively different level than innate immunity. This seemingly simple property is based on millions of years of evolution that led to the formation of diversification mechanisms of antigen-recognizing receptors and later to the emergence of a system of presentation of the self and non-self antigens. The latter could have a crucial significance because the presentation of nearly complete diversity of auto-antigens in the thymus allows for the "calibration" of the forming repertoires of T-cells for the recognition of self, altered-self, and non-self antigens that are presented on the periphery. The central role in this process belongs to promiscuous gene expression by the thymic epithelial cells that express nearly the whole spectrum of proteins encoded in the genome, meanwhile maintaining their cellular identity. This complex mechanism requires strict control that is executed by several transcription factors. One of the most important of them is AIRE. This noncanonical transcription factor not only regulates the processes of differentiation and expression of peripheral tissue-specific antigens in the thymic medullar epithelial cells but also controls intercellular interactions in the thymus. Besides, it participates in an increase in the diversity and transfer of presented antigens and thus influences the formation of repertoires of maturing thymocytes. Due to these complex effects, AIRE is also called a transcriptional regulator. In this review, we briefly described the history of AIRE discovery, its structure, functions, and role in the formation of antigen-recognizing receptor repertoires, along with other transcription factors. We focused on the phylogenetic prerequisites for the development of modern adaptive immunity and emphasized the importance of the antigen presentation system.

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.

Mass Spectrometry as a Crucial Analytical Basis for Omics Sciences

This review is devoted to the consideration of mass spectrometric platforms as applied to omics sciences. The most significant attention is paid to omics related to life sciences (genomics, proteomics, meta-bolomics, lipidomics, glycomics, plantomics, etc.). Mass spectrometric approaches to solving the problems of petroleomics, polymeromics, foodomics, humeomics, and exosomics, related to inorganic sciences, are also discussed. The review comparatively presents the advantages of various principles of separation and mass spectral techniques, complementary derivatization, used to obtain large arrays of various structural and quantitative information in the mentioned omics sciences.

In-silico selection of cancer blood plasma proteins by integrating genomic and proteomic databases

Blood protein markers have been studied for the clinical management of cancer. Due to the large number of the proteins existing in blood, it is often necessary to pre-select potential protein markers before experimental studies. However, to date there is a lack of automated method for in-silico selection of cancer blood proteins that integrates the information from both genetic and proteomic studies in a cancer-specific manner. In this work, we synthesized both genomic and proteomic information from several open access databases and established a bioinformatic pipeline for in-silico selection of blood plasma proteins overexpressed in specific type of cancer. We demonstrated the workflow of this pipeline with an example of breast cancer, while the methodology was applicable for other cancer types. With this pipeline we obtained 10 candidate biomarkers for breast cancer. The proposed pipeline provides a useful and convenient tool for in-silico selection of candidate blood protein biomarkers for a variety of cancer research.

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.

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.

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.

A protein-centric view of in vitro biological model systems for schizophrenia

Schizophrenia (SCZ) is a severe brain disorder, characterized by psychotic, negative, and cognitive symptoms, affecting 1% of the population worldwide. The precise etiology of SCZ is still unknown; however, SCZ has a high heritability and is associated with genetic, environmental, and social risk factors. Even though the genetic contribution is indisputable, the discrepancies between transcriptomics and proteomics in brain tissues are consistently challenging the field to decipher the disease pathology. Here we provide an overview of the state of the art of neuronal two-dimensional and three-dimensional model systems that can be combined with proteomics analyses to decipher specific brain pathology and detection of alternative entry points for drug development.

A Pragmatic Perspective of the Antibacterial Properties of Metal-Based Nanoparticles

A consideration of the antibacterial efficacy of metal-based nanoparticles, from the point of view of their physicochemical properties, suggests that such efficacy arises from the protein coronas that form around them, and that the contents of the coronas depend on the chemical groups found on the nanoparticle surfaces. We offer a new perspective and new insights, making use of our earlier observations of the physicochemical properties of nanoparticle surfaces, to propose that the nanoparticle serves as a mediator for the formation and activation of the protein corona, which attacks the bacterium. That is, the nanoparticle enhances the body’s natural defenses, using proteins present in body fluids.

Electrochemical distinction of neuronal and neuroblastoma cells via the phosphorylation of the cellular extracellular membrane

In this contribution we establish a proof of concept method for monitoring, quantifying and differentiating the extracellular phosphorylation of Human SHSY5Y undifferentiated neuronal cells and neuroblastoma cells by three prominent ectokinases PKA, PKC and Src. Herein it is demonstrated that a combination of different experimental techniques, including fluroesence microscopy, quartz crystal microscopy (QCM) and electrochemistry, can be used to detect extracellular phosphorylation levels of neuronal and neuroblastoma cells. Phosphorylation profiles of the three ectokinases, PKA, PKC and Src, were investigated using fluorescence microscopy and the number of phosphorylation sites per kinase was estimated using QCM. Finally, the phosphorylation of the extracellular membrane was determined using electrochemistry. Our results clearly demonstrate that the extracellular phosphorylation of neuronal cells differs significantly in terms of its phosphorylation profile from diseased neuroblastoma cells and the strength of surface electrochemical techniques in the differentiation process. We reveal that using electrochemistry, the percent compositions of neuronal and neuroblastoma cells can also be identified.

Applications of CRISPR-Cas Technologies to Proteomics

CRISPR-Cas-based genome editing is a revolutionary approach that has provided an unprecedented investigational power for the life sciences. Rapid and efficient, CRISPR-Cas technologies facilitate the generation of complex biological models and at the same time provide the necessary methods required to study these models in depth. The field of proteomics has already significantly benefited from leveraging the power of CRISPR-Cas technologies, however, many potential applications of these technologies in the context of proteomics remain unexplored. In this review, we intend to provide an introduction to the CRISPR-Cas technologies and demonstrate how they can be applied to solving proteome-centric questions. To achieve this goal, we begin with the description of the modern suite of CRISPR-Cas-based tools, focusing on the more mature CRISPR-Cas9 system. In the second part of this review, we highlight both established and potential applications of the CRISPR-Cas technologies to proteomics.

Post-translational Lysine Ac(et)ylation in Bacteria: A Biochemical, Structural, and Synthetic Biological Perspective

Ac(et)ylation is a post-translational modification present in all domains of life. First identified in mammals in histones to regulate RNA synthesis, today it is known that is regulates fundamental cellular processes also in bacteria: transcription, translation, metabolism, cell motility. Ac(et)ylation can occur at the ε-amino group of lysine side chains or at the α-amino group of a protein. Furthermore small molecules such as polyamines and antibiotics can be acetylated and deacetylated enzymatically at amino groups. While much research focused on N-(ε)-ac(et)ylation of lysine side chains, much less is known about the occurrence, the regulation and the physiological roles on N-(α)-ac(et)ylation of protein amino termini in bacteria. Lysine ac(et)ylation was shown to affect protein function by various mechanisms ranging from quenching of the positive charge, increasing the lysine side chains’ size affecting the protein surface complementarity, increasing the hydrophobicity and by interfering with other post-translational modifications. While N-(ε)-lysine ac(et)ylation was shown to be reversible, dynamically regulated by lysine acetyltransferases and lysine deacetylases, for N-(α)-ac(et)ylation only N-terminal acetyltransferases were identified and so far no deacetylases were discovered neither in bacteria nor in mammals. To this end, N-terminal ac(et)ylation is regarded as being irreversible. Besides enzymatic ac(et)ylation, recent data showed that ac(et)ylation of lysine side chains and of the proteins N-termini can also occur non-enzymatically by the high-energy molecules acetyl-coenzyme A and acetyl-phosphate. Acetyl-phosphate is supposed to be the key molecule that drives non-enzymatic ac(et)ylation in bacteria. Non-enzymatic ac(et)ylation can occur site-specifically with both, the protein primary sequence and the three dimensional structure affecting its efficiency. Ac(et)ylation is tightly controlled by the cellular metabolic state as acetyltransferases use ac(et)yl-CoA as donor molecule for the ac(et)ylation and sirtuin deacetylases use NAD⁺ as co-substrate for the deac(et)ylation. Moreover, the accumulation of ac(et)yl-CoA and acetyl-phosphate is dependent on the cellular metabolic state. This constitutes a feedback control mechanism as activities of many metabolic enzymes were shown to be regulated by lysine ac(et)ylation. Our knowledge on lysine ac(et)ylation significantly increased in the last decade predominantly due to the huge methodological advances that were made in fields such as mass-spectrometry, structural biology and synthetic biology. This also includes the identification of additional acylations occurring on lysine side chains with supposedly different regulatory potential. This review highlights recent advances in the research field. Our knowledge on enzymatic regulation of lysine ac(et)ylation will be summarized with a special focus on structural and mechanistic characterization of the enzymes, the mechanisms underlying non-enzymatic/chemical ac(et)ylation are explained, recent technological progress in the field are presented and selected examples highlighting the important physiological roles of lysine ac(et)ylation are summarized.

The Single-Cell Transcriptomic Analysis of Prefrontal Pyramidal Cells and Interneurons Reveals the Neuronal Expression of Genes Encoding Antimicrobial Peptides and Immune Proteins

The investigation of the molecular background of direct communication of neurons and immune cells in the brain is an important issue for understanding physiological and pathological processes in the nervous system. Direct contacts between brain-infiltrating immune cells and neurons, and the neuromodulatory effect of immune cell-derived regulatory peptides are well established. Several aspects of the role of immune and glial cells in the direct neuro-immune communication are also well known; however, there remain many questions regarding the molecular details of signaling from neurons to immune cells. Thus, we report here on the neuronal expression of genes encoding antimicrobial and immunomodulatory peptides, as well as proteins of immune cell-specific activation and communication mechanisms. In the present study, we analyzed the single-cell sequencing data of our previous transcriptomic work, obtained from electrophysiologically identified pyramidal cells and interneurons of the murine prefrontal cortex. We filtered out the genes that may be associated with the direct communication between immune cells and neurons and examined their expression pattern in the neuronal transcriptome. The expression of some of these genes by cortical neurons has not yet been reported. The vast majority of antimicrobial (~53%) and immune cell protein (~94%) transcripts was identified in the transcriptome of the 84 cells, owing to the high sensitivity of ultra-deep sequencing. Several of the antimicrobial and immune process-related protein transcripts showed cell type-specific or enriched expression. Individual neurons transcribed only a fraction of the investigated genes with low copy numbers probably due to the bursting kinetics of gene expression; however, the comparison of our data with available transcriptomic datasets from immune cells and neurons suggests the functional relevance of the reported findings. Accordingly, we propose further experimental and in silico studies on the neuronal expression of immune system-related genes and the potential role of the encoded proteins in neuroimmunological processes.

Defining NASH from a Multi-Omics Systems Biology Perspective

Non-alcoholic steatohepatitis (NASH) is a chronic liver disease affecting up to 6.5% of the general population. There is no simple definition of NASH, and the molecular mechanism underlying disease pathogenesis remains elusive. Studies applying single omics technologies have enabled a better understanding of the molecular profiles associated with steatosis and hepatic inflammation—the commonly accepted histologic features for diagnosing NASH, as well as the discovery of novel candidate biomarkers. Multi-omics analysis holds great potential to uncover new insights into disease mechanism through integrating multiple layers of molecular information. Despite the technical and computational challenges associated with such efforts, a few pioneering studies have successfully applied multi-omics technologies to investigate NASH. Here, we review the most recent technological developments in mass spectrometry (MS)-based proteomics, metabolomics, and lipidomics. We summarize multi-omics studies and emerging omics biomarkers in NASH and highlight the biological insights gained through these integrated analyses.

Analysis of pancreatic extracellular matrix protein post-translational modifications via electrostatic repulsion-hydrophilic interaction chromatography coupled with mass spectrometry

The pancreas is a vital organ with digestive and endocrine roles, and diseases of the pancreas affect millions of people yearly. A better understanding of the pancreas proteome and its dynamic post-translational modifications (PTMs) is necessary to engineer higher fidelity tissue analogues for use in transplantation. The extracellular matrix (ECM) has major roles in binding and signaling essential to the viability of insulin-producing islets of Langerhans. To characterize PTMs in the pancreas, native and decellularized tissues from four donors were analyzed. N-Glycosylated and phosphorylated peptides were simultaneously enriched via electrostatic repulsion-hydrophilic interaction chromatography and analyzed with mass spectrometry, maximizing PTM information from one workflow. A modified surfactant and chaotropic agent assisted sequential extraction/on-pellet digestion was used to maximize solubility of the ECM. The analysis resulted in the confident identification of 3650 proteins, including 517 N-glycoproteins and 148 phosphoproteins. We identified 214 ECM proteins, of which 99 were N-glycosylated, 18 were phosphorylated, and 9 were found to have both modifications. Collagens, a major component of the ECM, were the most highly glycosylated of the ECM proteins and several were also heavily phosphorylated, raising the possibility of structural and thus functional changes resulting from these modifications. To our knowledge, this work represents the first characterization of PTMs in pancreatic ECM proteins. This work provides a basal profile of PTMs in the healthy human pancreatic ECM, laying the foundation for future investigations to determine disease-specific changes such as in diabetes and pancreatic cancer, and potentially helping to guide the development of tissue replacement constructs. Data are available via ProteomeXchange with identifier PXD025048.

Immunoaffinity Capillary Electrophoresis in the Era of Proteoforms, Liquid Biopsy and Preventive Medicine: A Potential Impact in the Diagnosis and Monitoring of Disease Progression

Over the years, multiple biomarkers have been used to aid in disease screening, diagnosis, prognosis, and response to therapy. As of late, protein biomarkers are gaining strength in their role for early disease diagnosis and prognosis in part due to the advancements in identification and characterization of a distinct functional pool of proteins known as proteoforms. Proteoforms are defined as all of the different molecular forms of a protein derived from a single gene caused by genetic variations, alternative spliced RNA transcripts and post-translational modifications. Monitoring the structural changes of each proteoform of a particular protein is essential to elucidate the complex molecular mechanisms that guide the course of disease. Clinical proteomics therefore holds the potential to offer further insight into disease pathology, progression, and prevention. Nevertheless, more technologically advanced diagnostic methods are needed to improve the reliability and clinical applicability of proteomics in preventive medicine. In this manuscript, we review the use of immunoaffinity capillary electrophoresis (IACE) as an emerging powerful diagnostic tool to isolate, separate, detect and characterize proteoform biomarkers obtained from liquid biopsy. IACE is an affinity capture-separation technology capable of isolating, concentrating and analyzing a wide range of biomarkers present in biological fluids. Isolation and concentration of target analytes is accomplished through binding to one or more biorecognition affinity ligands immobilized to a solid support, while separation and analysis are achieved by high-resolution capillary electrophoresis (CE) coupled to one or more detectors. IACE has the potential to generate rapid results with significant accuracy, leading to reliability and reproducibility in diagnosing and monitoring disease. Additionally, IACE has the capability of monitoring the efficacy of therapeutic agents by quantifying companion and complementary protein biomarkers. With advancements in telemedicine and artificial intelligence, the implementation of proteoform biomarker detection and analysis may significantly improve our capacity to identify medical conditions early and intervene in ways that improve health outcomes for individuals and populations.

Segmentation-based analysis of single-cell immunoblots

From genomics to transcriptomics to proteomics, microfluidic tools underpin recent advances in single-cell biology. Detection of specific proteoforms-with single-cell resolution-presents challenges in detection specificity and sensitivity. Miniaturization of protein immunoblots to single-cell resolution mitigates these challenges. For example, in microfluidic western blotting, protein targets are separated by electrophoresis and subsequently detected using fluorescently labeled antibody probes. To quantify the expression level of each protein target, the fluorescent protein bands are fit to Gaussians; yet, this method is difficult to use with noisy, low-abundance, or low-SNR protein bands, and with significant band skew or dispersion. In this study, we investigate segmentation-based approaches to robustly quantify protein bands from single-cell protein immunoblots. As compared to a Gaussian fitting pipeline, the segmentation pipeline detects >1.5× more protein bands for downstream quantification as well as more of the low-abundance protein bands (i.e., with SNR ∼3). Utilizing deep learning-based segmentation approaches increases the recovery of low-SNR protein bands by an additional 50%. However, we find that segmentation-based approaches are less robust at quantifying poorly resolved protein bands (separation resolution, Rs < 0.6). With burgeoning needs for more single-cell protein analysis tools, we see microfluidic separations as benefitting substantially from segmentation-based analysis approaches.

Application of Artificial Intelligence to Clinical Practice in Inflammatory Bowel Disease - What the Clinician Needs to Know

Artificial intelligence [AI] techniques are quickly spreading across medicine as an analytical method to tackle challenging clinical questions. What were previously thought of as highly complex data sources, such as images or free text, are now becoming manageable. Novel analytical methods merge the latest developments in information technology infrastructure with advances in computer science. Once primarily associated with Silicon Valley, AI techniques are now making their way into medicine, including in the field of inflammatory bowel diseases [IBD]. Understanding potential applications and limitations of these techniques can be difficult, in particular for busy clinicians. In this article, we explain the basic terminologies and provide a particular focus on the foundations behind state-of-the-art AI methodologies in both imaging and text. We explore the growing applications of AI in medicine, with a specific focus on IBD to inform the practising gastroenterologist and IBD specialist. Finally, we outline possible future uses of these technologies in daily clinical practice.

Proteomes Are of Proteoforms: Embracing the Complexity

Proteomes are complex-much more so than genomes or transcriptomes. Thus, simplifying their analysis does not simplify the issue. Proteomes are of proteoforms, not canonical proteins. While having a catalogue of amino acid sequences provides invaluable information, this is the Proteome-lite. To dissect biological mechanisms and identify critical biomarkers/drug targets, we must assess the myriad of proteoforms that arise at any point before, after, and between translation and transcription (e.g., isoforms, splice variants, and post-translational modifications [PTM]), as well as newly defined species. There are numerous analytical methods currently used to address proteome depth and here we critically evaluate these in terms of the current 'state-of-the-field'. We thus discuss both pros and cons of available approaches and where improvements or refinements are needed to quantitatively characterize proteomes. To enable a next-generation approach, we suggest that advances lie in transdisciplinarity via integration of current proteomic methods to yield a unified discipline that capitalizes on the strongest qualities of each. Such a necessary (if not revolutionary) shift cannot be accomplished by a continued primary focus on proteo-genomics/-transcriptomics. We must embrace the complexity. Yes, these are the hard questions, and this will not be easy…but where is the fun in easy?

Newer Methods Drive Recent Insights into Rab GTPase Biology: An Overview

The conserved Ypt/Rab GTPases regulate all major intracellular protein traffic pathways, including secretion, endocytosis and autophagy. These GTPases undergo distinct changes in conformation between their GTP- and GDP-bound forms and cycle between the cytoplasm and membranes with the aid of their upstream regulators. When activated on the membrane in the GTP-bound form, they recruit their downstream effectors, which include components of vesicular transport. Progress in the past 5 years regarding mechanisms of Rab action, functions, and the effects of disruption of these functions on the well-being of cells and organisms has been propelled by advances in methodologies in molecular and cellular biology. Here, we highlight methods used recently to analyze regulation, localization, interactions, and function of Rab GTPases and their roles in human disease. We discuss contributions of these methods to new insights into Rabs, as well as their future use in addressing open questions in the field of Rab biology.

From genome scissors to molecular scalpel: evolution of CRISPR systems

From bizarre palindromic repeats to a bacterial defense mechanism, to genome editing tool, and more, Clustered Regularly Interspaced Short Palindromic Repeats or CRISPR has significantly impacted the way we study genome modification in less than a decade. In this review, we would like to highlight some key players over 30 years of research and explain this biotechnological tool's basic mechanisms. We also refer to the evolution of the CRISPR variants and some of the applications derived from them. The understanding and upgrading of this system will be a valuable tool in the years to come to solve some of the challenges in diverse fields from pharmaceuticals to therapeutics, from basic plant genetics to crop improvement, from metabolic engineering to waste management and industrial processing.

Tackling Dysfunction of Mitochondrial Bioenergetics in the Brain

Oxidative phosphorylation (OxPhos) is the basic function of mitochondria, although the landscape of mitochondrial functions is continuously growing to include more aspects of cellular homeostasis. Thanks to the application of -omics technologies to the study of the OxPhos system, novel features emerge from the cataloging of novel proteins as mitochondrial thus adding details to the mitochondrial proteome and defining novel metabolic cellular interrelations, especially in the human brain. We focussed on the diversity of bioenergetics demand and different aspects of mitochondrial structure, functions, and dysfunction in the brain. Definition such as 'mitoexome', 'mitoproteome' and 'mitointeractome' have entered the field of 'mitochondrial medicine'. In this context, we reviewed several genetic defects that hamper the last step of aerobic metabolism, mostly involving the nervous tissue as one of the most prominent energy-dependent tissues and, as consequence, as a primary target of mitochondrial dysfunction. The dual genetic origin of the OxPhos complexes is one of the reasons for the complexity of the genotype-phenotype correlation when facing human diseases associated with mitochondrial defects. Such complexity clinically manifests with extremely heterogeneous symptoms, ranging from organ-specific to multisystemic dysfunction with different clinical courses. Finally, we briefly discuss the future directions of the multi-omics study of human brain disorders.

Step-by-step design of proteins for small molecule interaction: A review on recent milestones

Protein design is the field of synthetic biology that aims at developing de novo custom-made proteins and peptides for specific applications. Despite exploring an ambitious goal, recent computational advances in both hardware and software technologies have paved the way to high-throughput screening and detailed design of novel folds and improved functionalities. Modern advances in the field of protein design for small molecule targeting are described in this review, organized in a step-by-step fashion: from the conception of a new or upgraded active binding site, to scaffold design, sequence optimization, and experimental expression of the custom protein. In each step, contemporary examples are described, and state-of-the-art software is briefly explored.

Automatization and self-maintenance of the O-GlcNAcome catalog: a smart scientific database

Post-translational modifications (PTMs) are ubiquitous and essential for protein function and signaling, motivating the need for sustainable benefit and open models of web databases. Highly conserved O-GlcNAcylation is a case example of one of the most recently discovered PTMs, investigated by a growing community. Historically, details about O-GlcNAcylated proteins and sites were dispersed across literature and in non-O-GlcNAc-focused, rapidly outdated or now defunct web databases. In a first effort to fill the gap, we recently published a human O-GlcNAcome catalog with a basic web interface. Based on the enthusiasm generated by this first resource, we extended our O-GlcNAcome catalog to include data from 42 distinct organisms and released the O-GlcNAc Database v1.2. In this version, more than 14 500 O-GlcNAcylated proteins and 11 000 O-GlcNAcylation sites are referenced from the curation of 2200 publications. In this article, we also present the extensive features of the O-GlcNAc Database, including the user-friendly interface, back-end and client-server interactions. We particularly emphasized our workflow, involving a mostly automatized and self-maintained database, including machine learning approaches for text mining. We hope that this software model will be useful beyond the O-GlcNAc community, to set up new smart, scientific online databases, in a short period of time. Indeed, this database system can be administrated with little to no programming skills and is meant to be an example of a useful, sustainable and cost-efficient resource, which exclusively relies on free open-source software elements (www.oglcnac.mcw.edu).

Alternative RNA Splicing-The Trojan Horse of Cancer Cells in Chemotherapy

Almost all transcribed human genes undergo alternative RNA splicing, which increases the diversity of the coding and non-coding cellular landscape. The resultant gene products might have distinctly different and, in some cases, even opposite functions. Therefore, the abnormal regulation of alternative splicing plays a crucial role in malignant transformation, development, and progression, a fact supported by the distinct splicing profiles identified in both healthy and tumor cells. Drug resistance, resulting in treatment failure, still remains a major challenge for current cancer therapy. Furthermore, tumor cells often take advantage of aberrant RNA splicing to overcome the toxicity of the administered chemotherapeutic agents. Thus, deciphering the alternative RNA splicing variants in tumor cells would provide opportunities for designing novel therapeutics combating cancer more efficiently. In the present review, we provide a comprehensive outline of the recent findings in alternative splicing in the most common neoplasms, including lung, breast, prostate, head and neck, glioma, colon, and blood malignancies. Molecular mechanisms developed by cancer cells to promote oncogenesis as well as to evade anticancer drug treatment and the subsequent chemotherapy failure are also discussed. Taken together, these findings offer novel opportunities for future studies and the development of targeted therapy for cancer-specific splicing variants.