Short Linear Motifs Orchestrate Functioning of Human Proteins during Embryonic Development, Redox Regulation, and Cancer.

Short linear motifs (SLiMs) are evolutionarily conserved functional modules of proteins that represent amino acid stretches composed of 3 to 10 residues. The biological activities of two short peptide segments of human alpha-fetoprotein (AFP), a major embryo-specific and cancer-related protein, have been confirmed experimentally. This is a heptapeptide segment LDSYQCT in domain I designated as AFP14-20 and a nonapeptide segment EMTPVNPGV in domain III designated as GIP-9. In our work, we searched the UniprotKB database for human proteins that contain SLiMs with sequence similarity to the both segments of human AFP and undertook gene ontology (GO)-based functional categorization of retrieved proteins. Gene set enrichment analysis included GO terms for biological process, molecular function, metabolic pathway, KEGG pathway, and protein-protein interaction (PPI) categories. We identified the SLiMs of interest in a variety of non-homologous proteins involved in multiple cellular processes underlying embryonic development, cancer progression, and, unexpectedly, the regulation of redox homeostasis. These included transcription factors, cell adhesion proteins, ubiquitin-activating and conjugating enzymes, cell signaling proteins, and oxidoreductase enzymes. They function by regulating cell proliferation and differentiation, cell cycle, DNA replication/repair/recombination, metabolism, immune/inflammatory response, and apoptosis. In addition to the retrieved genes, new interacting genes were identified. Our data support the hypothesis that conserved SLiMs are incorporated into non-homologous proteins to serve as functional blocks for their orchestrated functioning.

Predictive biomarkers for systemic therapy of hepatocellular carcinoma.

Introduction: Hepatocellular carcinoma (HCC) is the most common primary liver cancer and the third cancer-related cause of death worldwide. In recent years, several systemic therapy drugs including sorafenib, lenvatinib, regorafenib, cabozantinib, ramucicurab, nivilumab, and pembrolizumab have been approved by FDA for advanced HCC. However, their insufficient efficacy, toxicity, and drug resistance require clinically applicable and validated predictive biomarkers.Areas covered: Our review covers the recent advancements in the identification of proteomic/genomic/epigenomic/transcriptomic biomarkers for predicting HCC treatment efficacy with the use of multi-kinase inhibitors (MKIs), CDK4/6 inhibitors, and immune checkpoint inhibitors (ICIs). Alpha-fetoprotein, des-carboxyprothrombin, vascular endothelial growth factor, angiopoietin-2, and dysregulated MTOR, VEGFR2, c-KIT, RAF1, PDGFRß have the potential of proteomic/genomic biomarkers for sorafenib treatment. Alanine aminotransferase, aspartate aminotransferase, and albumin-bilirubin grade can predict the efficacy of other MKIs. Rb, p16, and Ki-67, and genes involved in cell cycle regulation, CDK1-4, CCND1, CDKN1A, and CDKN2A have been proposed for CD4/6 inhibitors, while dysregulated TERT, CTNNB1, TP53 FGF19, and TP53 are found to be predictors for ICI efficacy.Expert opinion: There are still limited clinically applicable and validated predictive biomarkers to identify HCC patients who benefit from systemic therapy. Further prospective biomarker validation studies for HCC personalized systemic therapy are required.

Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine.

Hepatocellular carcinoma (HCC) is the most common primary cancer of the liver with high morbidity and mortality rates worldwide. Since 1963, when alpha-fetoprotein (AFP) was discovered as a first HCC serum biomarker, several other protein biomarkers have been identified and introduced into clinical practice. However, insufficient specificity and sensitivity of these biomarkers dictate the necessity of novel biomarker discovery. Remarkable advancements in integrated multiomics technologies for the identification of gene expression and protein or metabolite distribution patterns can facilitate rising to this challenge. Current multiomics technologies lead to the accumulation of a huge amount of data, which requires clustering and finding correlations between various datasets and developing predictive models for data filtering, pre-processing, and reducing dimensionality. Artificial intelligence (AI) technologies have an enormous potential to overcome accelerated data growth, complexity, and heterogeneity within and across data sources. Our review focuses on the recent progress in integrative proteomic profiling strategies and their usage in combination with machine learning and deep learning technologies for the discovery of novel biomarker candidates for HCC early diagnosis and prognosis. We discuss conventional and promising proteomic biomarkers of HCC such as AFP, lens culinaris agglutinin (LCA)-reactive L3 glycoform of AFP (AFP-L3), des-gamma-carboxyprothrombin (DCP), osteopontin (OPN), glypican-3 (GPC3), dickkopf-1 (DKK1), midkine (MDK), and squamous cell carcinoma antigen (SCCA) and highlight their functional significance including the involvement in cell signaling such as Wnt/ß-catenin, PI3K/Akt, integrin αvß3/NF-κB/HIF-1α, JAK/STAT3 and MAPK/ERK-mediated pathways dysregulated in HCC. We show that currently available computational platforms for big data analysis and AI technologies can both enhance proteomic profiling and improve imaging techniques to enhance the translational application of proteomics data into precision medicine.

Metabolic Heterogeneity of Cancer Cells: An Interplay between HIF-1, GLUTs, and AMPK.

It has been long recognized that cancer cells reprogram their metabolism under hypoxia conditions due to a shift from oxidative phosphorylation (OXPHOS) to glycolysis in order to meet elevated requirements in energy and nutrients for proliferation, migration, and survival. However, data accumulated over recent years has increasingly provided evidence that cancer cells can revert from glycolysis to OXPHOS and maintain both reprogrammed and oxidative metabolism, even in the same tumor. This phenomenon, denoted as cancer cell metabolic plasticity or hybrid metabolism, depends on a tumor micro-environment that is highly heterogeneous and influenced by an intensity of vasculature and blood flow, oxygen concentration, and nutrient and energy supply, and requires regulatory interplay between multiple oncogenes, transcription factors, growth factors, and reactive oxygen species (ROS), among others. Hypoxia-inducible factor-1 (HIF-1) and AMP-activated protein kinase (AMPK) represent key modulators of a switch between reprogrammed and oxidative metabolism. The present review focuses on cross-talks between HIF-1, glucose transporters (GLUTs), and AMPK with other regulatory proteins including oncogenes such as c-Myc, p53, and KRAS; growth factor-initiated protein kinase B (PKB)/Akt, phosphatydyl-3-kinase (PI3K), and mTOR signaling pathways; and tumor suppressors such as liver kinase B1 (LKB1) and TSC1 in controlling cancer cell metabolism. The multiple switches between metabolic pathways can underlie chemo-resistance to conventional anti-cancer therapy and should be taken into account in choosing molecular targets to discover novel anti-cancer drugs.

Oxidative Stress and Advanced Lipoxidation and Glycation End Products (ALEs and AGEs) in Aging and Age-Related Diseases.

Oxidative stress is a consequence of the use of oxygen in aerobic respiration by living organisms and is denoted as a persistent condition of an imbalance between the generation of reactive oxygen species (ROS) and the ability of the endogenous antioxidant system (AOS) to detoxify them. The oxidative stress theory has been confirmed in many animal studies, which demonstrated that the maintenance of cellular homeostasis and biomolecular stability and integrity is crucial for cellular longevity and successful aging. Mitochondrial dysfunction, impaired protein homeostasis (proteostasis) network, alteration in the activities of transcription factors such as Nrf2 and NF-κB, and disturbances in the protein quality control machinery that includes molecular chaperones, ubiquitin-proteasome system (UPS), and autophagy/lysosome pathway have been observed during aging and age-related chronic diseases. The accumulation of ROS under oxidative stress conditions results in the induction of lipid peroxidation and glycoxidation reactions, which leads to the elevated endogenous production of reactive aldehydes and their derivatives such as glyoxal, methylglyoxal (MG), malonic dialdehyde (MDA), and 4-hydroxy-2-nonenal (HNE) giving rise to advanced lipoxidation and glycation end products (ALEs and AGEs, respectively). Both ALEs and AGEs play key roles in cellular response to oxidative stress stimuli through the regulation of a variety of cell signaling pathways. However, elevated ALE and AGE production leads to protein cross-linking and aggregation resulting in an alteration in cell signaling and functioning which causes cell damage and death. This is implicated in aging and various age-related chronic pathologies such as inflammation, neurodegenerative diseases, atherosclerosis, and vascular complications of diabetes mellitus. In the present review, we discuss experimental data evidencing the impairment in cellular functions caused by AGE/ALE accumulation under oxidative stress conditions. We focused on the implications of ALEs/AGEs in aging and age-related diseases to demonstrate that the identification of cellular dysfunctions involved in disease initiation and progression can serve as a basis for the discovery of relevant therapeutic agents.

Human EGF-derived direct and reverse short linear motifs: conformational dynamics insight into the receptor-binding residues.

Short linear motifs (SLiMs) have been recognized to perform diverse functions in a variety of regulatory proteins through the involvement in protein-protein interactions, signal transduction, cell cycle regulation, protein secretion, etc. However, detailed molecular mechanisms underlying their functions including roles of definite amino acid residues remain obscure. In our previous studies, we demonstrated that conformational dynamics of amino acid residues in oligopeptides derived from regulatory proteins such as alpha-fetoprotein (AFP), carcino-embryonic antigen (CEA), and pregnancy specific ß1-glycoproteins (PSGs) contributes greatly to their biological activities. In the present work, we revealed the 22-member linear modules composed of direct and reverse AFP14-20-like heptapeptide motifs linked by CxxGY/FxGx consensus motif within epidermal growth factor (EGF), growth factors of EGF family and numerous regulatory proteins containing EGF-like modules. We showed, first, the existence of similarity in amino acid signatures of both direct and reverse motifs in terms of their physicochemical properties. Second, molecular dynamics (MD) simulation study demonstrated that key receptor-binding residues in human EGF in the aligned positions of the direct and reverse motifs may have similar distribution of conformational probability densities and dynamic behavior despite their distinct physicochemical properties. Third, we found that the length of a polypeptide chain (from 7 to 53 residues) has no effect, while disulfide bridging and backbone direction significantly influence the conformational distribution and dynamics of the residues. Our data may contribute to the atomic level structure-function analysis and protein structure decoding; additionally, they may provide a basis for novel protein/peptide engineering and peptide-mimetic drug design.

Pregnancy-Specific ß1-Glycoproteins: Combined Biomarker Roles, Structure/Function Relationships and Implications for Drug Design.

BACKGROUND: Pregnancy specific ß1-glycoproteins (PSGs) have long been recognized as trophoblast quality and embryo viability markers. However, biological roles of PSGs remain obscure, and structure/function relationships to other feto-placental proteins as well as implications for drug design have not been reviewed. This review summarizes and discusses advances in 45-year studies of PSGs with focus on the latest achievements and the challenges for future investigations. METHODS: Literature search was performed to review the majority of recent PSG studies with emphasis on usage of high-throughput integrated proteomic profiling technologies, systems biology and bioinformatics approaches that enhance novel biomarker and drug target discovery as well as protein structure/activity analysis. RESULTS: Clinical significance and screening performance improved when PSG measurements were combined with those of other placenta-derived proteins: hCG, hPL, PAPP-A, and proMBP. Nevertheless, analysis of protein co-expression and co-localization data and the involvement of PSGs in protein interaction networks are being introduced to discover novel, specific and high-sensitive, gestational/cancer biomarkers. Despite biological roles of PSGs are not fully understood, there are evidences of that they exhibit immunomodulatory, antiinflammatory and proangiogenic effects. Investigation of structure/function relationships showed that PSGs may function in cooperative/coordinated manner with numerous regulatory proteins including alpha-fetoprotein and transforming growth factors-ß; this is provided by the presence of conserved short linear motifs (SLiMs) such as RGD, PXXP and AFP14-20-like (YXCX) ones. CONCLUSION: PSG-derived peptides may be used as a rationale to design novel drugs that mimic SLiMs involved in protein-protein interactions to inhibit domain-motif binding and to block cell signaling, and/or exert immunomodulatory, anti-inflammatory and proangiogenic effects.