Tumor markers for lipid metabolism-related genes: Based on small cell lung cancer and bronchial asthma dual analysis.

Numerous studies have elucidated the intricate relationship between bronchial asthma and small cell lung cancer (SCLC), as well as the role lipid metabolism genes play in transitioning from bronchial asthma to SCLC. Despite this, the predictive power of single gene biomarkers remains insufficient and necessitates the development of more accurate prognostic models. In our study, we downloaded and preprocessed scRNA-seq of SCLC from the GEO database GSE164404 and severe asthma scRNA-seq from GSE145013 using the Seurat package. Using the MSigDB database and geneCard database, we selected lipid metabolism-related genes and performed scRNA-seq data analysis from the gene expression GEO database, aiming to uncover potential links between immune signaling pathways in bronchial asthma and SCLC. Our investigations yielded differentially expressed genes based on the scRNA-seq dataset related to lipid metabolism. We executed differential gene analysis, gene ontology, and Kyoto Encyclopedia of Genes and Genomes analyses. In-depth GSEA pathway activation analysis, crucial target gene predictions via protein-protein interactions, and key cluster gene evaluations for differential and diagnostic ROC values correlation analysis confirmed that key cluster genes are significant predictors for the progression of bronchial asthma to SCLC. To validate our findings, we performed wet laboratory experiments using real-time quantitative PCR to assess the expression of these relevant genes in SCLC cell lines. In conclusion, this research proposes a novel lipid metabolism-related gene marker that can offer comprehensive insights into the pathogenesis of bronchial asthma leading to SCLC. Although this study does not directly focus on senescence-associated molecular alterations, our findings in the lipid metabolism genes associated with inflammation and cancer progression offer valuable insights for further research targeting senescence-related changes in treating inflammatory diseases.

Towards In Silico Identification of Genes Contributing to Similarity of Patients' Multi-Omics Profiles: A Case Study of Acute Myeloid Leukemia.

We propose a computational framework for selecting biologically plausible genes identified by clustering of multi-omics data that reveal patients' similarity, thus giving researchers a more comprehensive view on any given disease. We employ spectral clustering of a similarity network created by fusion of three similarity networks, based on mRNA expression of immune genes, miRNA expression and DNA methylation data, using SNF_v2.1 software. For each cluster, we rank multi-omics features, ensuring the best separation between clusters, and select the top-ranked features that preserve clustering. To find genes targeted by DNA methylation and miRNAs found in the top-ranked features, we use chromosome-conformation capture data and miRNet2.0 software, respectively. To identify informative genes, these combined sets of target genes are analyzed in terms of their enrichment in somatic/germline mutations, GO biological processes/pathways terms and known sets of genes considered to be important in relation to a given disease, as recorded in the Molecular Signature Database from GSEA. The protein-protein interaction (PPI) networks were analyzed to identify genes that are hubs of PPI networks. We used data recorded in The Cancer Genome Atlas for patients with acute myeloid leukemia to demonstrate our approach, and discuss our findings in the context of results in the literature.

EnCPdock: a web-interface for direct conjoint comparative analyses of complementarity and binding energetics in inter-protein associations.

CONTEXT: Protein-protein interaction (PPI) is a key component linked to virtually all cellular processes. Be it an enzyme catalysis ('classic type functions' of proteins) or a signal transduction ('non-classic'), proteins generally function involving stable or quasi-stable multi-protein associations. The physical basis for such associations is inherent in the combined effect of shape and electrostatic complementarities (Sc, EC) of the interacting protein partners at their interface, which provides indirect probabilistic estimates of the stability and affinity of the interaction. While Sc is a necessary criterion for inter-protein associations, EC can be favorable as well as disfavored (e.g., in transient interactions). Estimating equilibrium thermodynamic parameters (∆Gbinding, Kd) by experimental means is costly and time consuming, thereby opening windows for computational structural interventions. Attempts to empirically probe ∆Gbinding from coarse-grain structural descriptors (primarily, surface area based terms) have lately been overtaken by physics-based, knowledge-based and their hybrid approaches (MM/PBSA, FoldX, etc.) that directly compute ∆Gbinding without involving intermediate structural descriptors. METHODS: Here, we present EnCPdock ( https://www.scinetmol.in/EnCPdock/ ), a user-friendly web-interface for the direct conjoint comparative analyses of complementarity and binding energetics in proteins. EnCPdock returns an AI-predicted ∆Gbinding computed by combining complementarity (Sc, EC) and other high-level structural descriptors (input feature vectors), and renders a prediction accuracy comparable to the state-of-the-art. EnCPdock further locates a PPI complex in terms of its {Sc, EC} values (taken as an ordered pair) in the two-dimensional complementarity plot (CP). In addition, it also generates mobile molecular graphics of the interfacial atomic contact network for further analyses. EnCPdock also furnishes individual feature trends along with the relative probability estimates (Prfmax) of the obtained feature-scores with respect to the events of their highest observed frequencies. Together, these functionalities are of real practical use for structural tinkering and intervention as might be relevant in the design of targeted protein-interfaces. Combining all its features and applications, EnCPdock presents a unique online tool that should be beneficial to structural biologists and researchers across related fraternities.

Regulation of IL-24/IL-20R2 complex formation using photocaged tyrosines and UV light.

Human interleukin 24 (IL-24) is a multifunctional cytokine that represents an important target for autoimmune diseases and cancer. Since the biological functions of IL-24 depend on interactions with membrane receptors, on-demand regulation of the affinity between IL-24 and its cognate partners offers exciting possibilities in basic research and may have applications in therapy. As a proof-of-concept, we developed a strategy based on recombinant soluble protein variants and genetic code expansion technology to photocontrol the binding between IL-24 and one of its receptors, IL-20R2. Screening of non-canonical ortho-nitrobenzyl-tyrosine (NBY) residues introduced at several positions in both partners was done by a combination of biophysical and cell signaling assays. We identified one position for installing NBY, tyrosine70 of IL-20R2, which results in clear impairment of heterocomplex assembly in the dark. Irradiation with 365-nm light leads to decaging and reconstitutes the native tyrosine of the receptor that can then associate with IL-24. Photocaged IL-20R2 may be useful for the spatiotemporal control of the JAK/STAT phosphorylation cascade.

The dual role of p63 in cancer.

The p53 family is made up of three transcription factors: p53, p63, and p73. These proteins are well-known regulators of cell function and play a crucial role in controlling various processes related to cancer progression, including cell division, proliferation, genomic stability, cell cycle arrest, senescence, and apoptosis. In response to extra- or intracellular stress or oncogenic stimulation, all members of the p53 family are mutated in structure or altered in expression levels to affect the signaling network, coordinating many other pivotal cellular processes. P63 exists as two main isoforms (TAp63 and ΔNp63) that have been contrastingly discovered; the TA and ΔN isoforms exhibit distinguished properties by promoting or inhibiting cancer progression. As such, p63 isoforms comprise a fully mysterious and challenging regulatory pathway. Recent studies have revealed the intricate role of p63 in regulating the DNA damage response (DDR) and its impact on diverse cellular processes. In this review, we will highlight the significance of how p63 isoforms respond to DNA damage and cancer stem cells, as well as the dual role of TAp63 and ΔNp63 in cancer.

Comparative Transcriptomic Analyses for the Optimization of Thawing Regimes during Conventional Cryopreservation of Mature and Immature Human Testicular Tissue.

Cryopreservation of human testicular tissue, as a key element of anticancer therapy, includes the following stages: saturation with cryoprotectants, freezing, thawing, and removal of cryoprotectants. According to the point of view existing in "classical" cryobiology, the thawing mode is the most important consideration in the entire process of cryopreservation of any type of cells, including cells of testicular tissue. The existing postulate in cryobiology states that any frozen types of cells must be thawed as quickly as possible. The technologically maximum possible thawing temperature is 100 °C, which is used in our technology for the cryopreservation of testicular tissue. However, there are other points of view on the rate of cell thawing, according to how thawing should be carried out at physiological temperatures. In fact, there are morphological and functional differences between immature (from prepubertal patients) and mature testicular tissue. Accordingly, the question of the influence of thawing temperature on both types of tissues is relevant. The purpose of this study is to explore the transcriptomic differences of cryopreserved mature and immature testicular tissue subjected to different thawing methods by RNA sequencing. Collected and frozen testicular tissue samples were divided into four groups: quickly (in boiling water at 100 °C) thawed cryopreserved mature testicular tissue (group 1), slowly (by a physiological temperature of 37 °C) thawed mature testicular tissue (group 2), quickly thawed immature testicular tissue (group 3), and slowly thawed immature testicular tissue (group 4). Transcriptomic differences were assessed using differentially expressed genes (DEG), the Kyoto Encyclopedia of Genes and Genomes (KEGG), gene ontology (GO), and protein-protein interaction (PPI) analyses. No fundamental differences in the quality of cells of mature and immature testicular tissue after cryopreservation were found. Generally, thawing of mature and immature testicular tissue was more effective at 100 °C. The greatest difference in the intensity of gene expression was observed in ribosomes of cells thawed at 100 °C in comparison with cells thawed at 37 °C. In conclusion, an elevated speed of thawing is beneficial for frozen testicular tissue.

Nucleosomes and their complexes in the cryoEM era: Trends and limitations.

Twenty-five years have passed since the appearance of the first atomistic model of the nucleosome structure, and since then the number of new structures has gradually increased. With the advent of cryo-microscopy, the rate of accumulation of models has increased significantly. New structures are emerging with different histone variants and a variety of proteins that bind to nucleosomes. At the moment, there are more than four hundred structures containing nucleosomes in the Protein Data Bank. Many of these structures represent similar complexes, others differ in composition, conformation and quality. In this perspective, we investigate the diversity of known nucleosome structures, analyze data and model quality, variations in histone/DNA content of nucleosomes and spectrum of their interactors. We outline those parts of the nucleosome "structurome" that are already explored and those awaiting further exploration.

A dynamical view of protein-protein complexes: Studies by molecular dynamics simulations.

Protein-protein interactions are at the basis of many protein functions, and the knowledge of 3D structures of protein-protein complexes provides structural, mechanical and dynamical pieces of information essential to understand these functions. Protein-protein interfaces can be seen as stable, organized regions where residues from different partners form non-covalent interactions that are responsible for interaction specificity and strength. They are commonly described as a peripheral region, whose role is to protect the core region that concentrates the most contributing interactions, from the solvent. To get insights into the dynamics of protein-protein complexes, we carried out all-atom molecular dynamics simulations in explicit solvent on eight different protein-protein complexes of different functional class and interface size by taking into account the bound and unbound forms. On the one hand, we characterized structural changes upon binding of the proteins, and on the other hand we extensively analyzed the interfaces and the structural waters involved in the binding. Based on our analysis, in 6 cases out of 8, the interfaces rearranged during the simulation time, in stable and long-lived substates with alternative residue-residue contacts. These rearrangements are not restricted to side-chain fluctuations in the periphery but also affect the core interface. Finally, the analysis of the waters at the interface and involved in the binding pointed out the importance to take into account their role in the estimation of the interaction strength.

Cytosolic sequestration of spatacsin by Protein Kinase A and 14-3-3 proteins.

Mutations in SPG11, encoding spatacsin, constitute the major cause of autosomal recessive Hereditary Spastic Paraplegia (HSP) with thinning of the corpus callosum. Previous studies showed that spatacsin orchestrates cellular traffic events through the formation of a coat-like complex and its loss of function results in lysosomal and axonal transport impairments. However, the upstream mechanisms that regulate spatacsin trafficking are unknown. Here, using proteomics and CRISPR/Cas9-mediated tagging of endogenous spatacsin, we identified a subset of 14-3-3 proteins as physiological interactors of spatacsin. The interaction is modulated by Protein Kinase A (PKA)-dependent phosphorylation of spatacsin at Ser1955, which initiates spatacsin trafficking from the plasma membrane to the intracellular space. Our study provides novel insight in understanding spatacsin physio-pathological roles with mechanistic dissection of its associated pathways.

Electrochemical biosensors based on peptide-kinase interactions at the kinase docking site.

Kinases are important cancer biomarkers and are conventionally detected based on their catalytic activity. Kinases regulate cellular activities by phosphorylation of motif-specific multiple substrate proteins, resulting in a lack of selectivity of activity-based kinase biosensors. We present an alternative approach of sensing kinases based on the interactions of their allosteric docking sites with a specific partner protein. The new approach was demonstrated for the ERK2 kinase and its substrate ELK-1. A peptide derived from ELK-1 was bound to a gold electrode and ERK2 sensing was performed by electrochemical impedance spectroscopy. We performed a detailed analysis of the interaction between the ELK-1 peptide and the kinase on gold surfaces. Atomic force microscopy, variable angle spectroscopic ellipsometry, X-ray Photoelectron Spectroscopy, and polarization modulation IR reflection-absorption spectroscopy analysis of the gold surface revealed the adsorbed layer of the ERK2 on the peptide monolayer. The sensors showed a high level of target selectivity for ERK2 compared to the p38γ kinase and BSA. ERK2 was detected in its cellular concentration range, 0.5-2.0 µM, and the limit of detection was calculated to be 0.35 µM. Using the flexibility of peptide design, our method is generic for developing sensitive and substrate-specific biosensors and other disease-related enzymes based on their interactions.

Generation of Peptides for Highly Efficient Proximity Utilizing Site-Specific Biotinylation in Cells.

Protein tags are peptide sequences genetically embedded into a recombinant protein for various purposes, such as affinity purification, Western blotting, and immunofluorescence. Another recent application of peptide tags is in vivo labeling and analysis of protein-protein interactions (PPI) by proteomics methods. One of the common workflows involves site-specific in vivo biotinylation of an AviTag-fused protein in the presence of the biotin ligase BirA. However, due to the rapid kinetics of labeling, this tag is not ideal for analysis of PPI. Here we describe the rationale, design, and protocol for the new biotin acceptor peptides BAP1070 and BAP1108 using modular assembling of biotin acceptor fragments, DNA sequencing, transient expression of proteins in cells, and Western blotting methods. These tags were used in the Proximity Utilizing Biotinylation (PUB) method, which is based on coexpression of BAP-X and BirA-Y in mammalian cells, where X or Y are candidate interacting proteins of interest. By changing the sequence of these peptides, a low level of background biotinylation is achieved, which occurs due to random collisions of proteins in cells. Over 100 plasmid constructs, containing genes of transcription factors, histones, gene repressors, and other nuclear proteins were obtained during implementation of projects related to this method.

Structural property, molecular regulation, and functional diversity of glutamine synthetase in higher plants: a data-mining bioinformatics approach.

Glutamine synthetase (GS; E.C.6.3.1.2) is a key enzyme in higher plants with two isozymes, cytosolic GS1 and plastidic GS2, and involves in the assimilation and recycling of NH4+ ions and maintenance of complex traits such as crop nitrogen-use efficiency and yield. Our present understanding of crop nitrogen-use efficiency and its correlation with the functional role of the GS family genes is inadequate, which delays harnessing the benefit of this key enzyme in crop improvement. In this report, we performed a comprehensive investigation on the phylogenetic relationship, structural properties, complex multilevel gene regulation, and expression patterns of the GS genes to enrich present understanding about the enzyme. Our Gene Ontology and protein-protein interactions analysis revealed the functional aspects of GS isozymes in stress mitigation, aging, nucleotide biosynthesis/transport, DNA repair and response to metals. The insight gained here contributes to the future research strategies in developing climate-smart crops for global sustainability.

Mechanisms of Functional Pleiotropy of p73 in Cancer and Beyond.

The transcription factor p73 is a structural and functional homolog of TP53, the most famous and frequently mutated tumor-suppressor gene. The TP73 gene can synthesize an overwhelming number of isoforms via splicing events in 5' and 3' ends and alternative promoter usage. Although it originally came into the spotlight due to the potential of several of these isoforms to mimic p53 functions, it is now clear that TP73 has its own unique identity as a master regulator of multifaceted processes in embryonic development, tissue homeostasis, and cancer. This remarkable functional pleiotropy is supported by a high degree of mechanistic heterogeneity, which extends far-beyond the typical mode of action by transactivation and largely relies on the ability of p73 isoforms to form protein-protein interactions (PPIs) with a variety of nuclear and cytoplasmic proteins. Importantly, each p73 isoform carries a unique combination of functional domains and residues that facilitates the establishment of PPIs in a highly selective manner. Herein, we summarize the expanding functional repertoire of TP73 in physiological and oncogenic processes. We emphasize how TP73's ability to control neurodevelopment and neurodifferentiation is co-opted in cancer cells toward neoneurogenesis, an emerging cancer hallmark, whereby tumors promote their own innervation. By further exploring the canonical and non-canonical mechanistic patterns of p73, we apprehend its functional diversity as the result of a sophisticated and coordinated interplay of: (a) the type of p73 isoforms (b) the presence of p73 interaction partners in the cell milieu, and (c) the architecture of target gene promoters. We suppose that dysregulation of one or more of these parameters in tumors may lead to cancer initiation and progression by reactivating p73 isoforms and/or p73-regulated differentiation programs thereof in a spatiotemporally inappropriate manner. A thorough understanding of the mechanisms supporting p73 functional diversity is of paramount importance for the efficient and precise p73 targeting not only in cancer, but also in other pathological conditions where TP73 dysregulation is causally involved.

Peroxiredoxins wear many hats: Factors that fashion their peroxide sensing personalities.

Peroxiredoxins (Prdxs) sense and assess peroxide levels, and signal through protein interactions. Understanding the role of the multiple structural and post-translational modification (PTM) layers that tunes the peroxiredoxin specificities is still a challenge. In this review, we give a tabulated overview on what is known about human and bacterial peroxiredoxins with a focus on structure, PTMs, and protein-protein interactions. Armed with numerous cellular and atomic level experimental techniques, we look at the future and ask ourselves what is still needed to give us a clearer view on the cellular operating power of Prdxs in both stress and non-stress conditions.

In Vivo Quantitative Estimation of DNA-Dependent Interaction of Sox2 and Oct4 Using BirA-Catalyzed Site-Specific Biotinylation.

Protein-protein interactions of core pluripotency transcription factors play an important role during cell reprogramming. Cell identity is controlled by a trio of transcription factors: Sox2, Oct4, and Nanog. Thus, methods that help to quantify protein-protein interactions may be useful for understanding the mechanisms of pluripotency at the molecular level. Here, a detailed protocol for the detection and quantitative analysis of in vivo protein-protein proximity of Sox2 and Oct4 using the proximity-utilizing biotinylation (PUB) method is described. The method is based on the coexpression of two proteins of interest fused to a biotin acceptor peptide (BAP)in one case and a biotin ligase enzyme (BirA) in the other. The proximity between the two proteins leads to more efficient biotinylation of the BAP, which can be either detected by Western blotting or quantified using proteomics approaches, such as a multiple reaction monitoring (MRM) analysis. Coexpression of the fusion proteins BAP-X and BirA-Y revealed strong biotinylation of the target proteins when X and Y were, alternatively, the pluripotency transcription factors Sox2 and Oct4, compared with the negative control where X or Y was green fluorescent protein (GFP), which strongly suggests that Sox2 and Oct4 come in close proximity to each other and interact.

Targeting Kinase Interaction Networks: A New Paradigm in PPI Based Design of Kinase Inhibitors.

BACKGROUND: Kinases are key modulators in regulating diverse range of cellular activities and are an essential part of the protein-protein interactome. Understanding the interaction of kinases with different substrates and other proteins is vital to decode the cell signaling machinery as well as causative mechanism for disease onset and progression. OBJECTIVE: The objective of this review is to present all studies on the structure and function of few important kinases and highlight the protein-protein interaction (PPI) mechanism of kinases and the kinase specific interactome databases and how such studies could be utilized to develop anticancer drugs. METHODS: The article is a review of the detailed description of the various domains in kinases that are involved in protein-protein interactions and specific inhibitors developed targeting these PPI domains. RESULTS: The review has surfaced in depth the interacting domains in key kinases and their features and the roles of PPI in the human kinome and the various signaling cascades that are involved in certain types of cancer. CONCLUSION: The insight availed into the mechanism of existing peptide inhibitors and peptidomimetics against kinases will pave way for the design and generation of domain specific peptide inhibitors with better productivity and efficiency and the various software and servers available can be of great use for the identification and analysis of protein-protein interactions.

BCL-2 Protein Family Interaction Analysis by Nuclear Magnetic Resonance Spectroscopy.

Biomolecular nuclear magnetic resonance (NMR) is a powerful and versatile method for studying both protein-protein interactions (PPIs) and protein-small molecule binding. NMR has been used extensively in the investigation of BCL-2 family proteins revealing the structure of key family members, identifying binding partners and interaction sites, and screening small molecule modulators. In this chapter we discuss the application of NMR to identify interaction sites and structure determination of protein-protein and protein-small molecule complexes using two examples.

Benchmarking the PEPOP methods for mimicking discontinuous epitopes.

BACKGROUND: Computational methods provide approaches to identify epitopes in protein Ags to help characterizing potential biomarkers identified by high-throughput genomic or proteomic experiments. PEPOP version 1.0 was developed as an antigenic or immunogenic peptide prediction tool. We have now improved this tool by implementing 32 new methods (PEPOP version 2.0) to guide the choice of peptides that mimic discontinuous epitopes and thus potentially able to replace the cognate protein Ag in its interaction with an Ab. In the present work, we describe these new methods and the benchmarking of their performances. RESULTS: Benchmarking was carried out by comparing the peptides predicted by the different methods and the corresponding epitopes determined by X-ray crystallography in a dataset of 75 Ag-Ab complexes. The Sensitivity (Se) and Positive Predictive Value (PPV) parameters were used to assess the performance of these methods. The results were compared to that of peptides obtained either by chance or by using the SUPERFICIAL tool, the only available comparable method. CONCLUSION: The PEPOP methods were more efficient than, or as much as chance, and 33 of the 34 PEPOP methods performed better than SUPERFICIAL. Overall, "optimized" methods (tools that use the traveling salesman problem approach to design peptides) can predict peptides that best match true epitopes in most cases.

Peptidomimetics Targeting Protein-Protein Interactions for Therapeutic Development.

BACKGROUND: Interactions between proteins play a key role in nearly all cellular process, and therefore, its dysregulation may lead to many different types of cellular dysfunctions. Hence, pathologic Protein-Protein Interactions (PPIs) constitute highly attractive drug targets and hold great potential for developing novel therapeutic agents for the treatment of incurable human diseases. Unfortunately, the identification of PPI inhibitors is an extremely challenging task, since traditionally used small molecules ligands are mostly unable to cover and anchor on the extensive and flat surfaces that define those binary protein complexes. In contrast, large biomolecules such as proteins or peptides are ideal fits for this so-called "undruggable" sites. However, their poor pharmacokinetic properties have also limited their applications as therapeutics. In this context, peptidomimetic molecules have emerged as an alternative and viable solution to this problem, since they conserve the architectural and structural features of peptides and also exhibit substantially improved pharmacokinetic profiles. CONCLUSION: In the last decades, a wide array of chemical approaches granting access to conformationally constrained peptides with substantially improved pharmacokinetic profiles have been described, with a special focus on those affording stapled peptides and allowing large-scale macrocyclizations. These peptidomimetic molecules have been successfully applied to target a plethora of biological hosts, which highlights their promising future as novel therapeutics for the treatment of incurable human diseases.

Probing Protein-Protein Interactions Using Asymmetric Labeling and Carbonyl-Carbon Selective Heteronuclear NMR Spectroscopy.

Protein-protein interactions (PPIs) regulate a plethora of cellular processes and NMR spectroscopy has been a leading technique for characterizing them at the atomic resolution. Technically, however, PPIs characterization has been challenging due to multiple samples required to characterize the hot spots at the protein interface. In this paper, we review our recently developed methods that greatly simplify PPI studies, which minimize the number of samples required to fully characterize residues involved in the protein-protein binding interface. This original strategy combines asymmetric labeling of two binding partners and the carbonyl-carbon label selective (CCLS) pulse sequence element implemented into the heteronuclear single quantum correlation (¹H-15N HSQC) spectra. The CCLS scheme removes signals of the J-coupled 15N⁻13C resonances and records simultaneously two individual amide fingerprints for each binding partner. We show the application to the measurements of chemical shift correlations, residual dipolar couplings (RDCs), and paramagnetic relaxation enhancements (PRE). These experiments open an avenue for further modifications of existing experiments facilitating the NMR analysis of PPIs.