Antioxidant and oxidative stress: a mutual interplay in age-related diseases

Aging is the progressive loss of organ and tissue function over time. Growing older is positively linked to cognitive and biological degeneration such as physical frailty, psychological impairment, and cognitive decline. Oxidative stress is considered as an imbalance between pro- and antioxidant species, which results in molecular and cellular damage. Oxidative stress plays a crucial role in the development of age-related diseases. Emerging research evidence has suggested that antioxidant can control the autoxidation by interrupting the propagation of free radicals or by inhibiting the formation of free radicals and subsequently reduce oxidative stress, improve immune function, and increase healthy longevity. Indeed, oxidation damage is highly dependent on the inherited or acquired defects in enzymes involved in the redox-mediated signaling pathways. Therefore, the role of molecules with antioxidant activity that promote healthy aging and counteract oxidative stress is worth to discuss further. Of particular interest in this article, we highlighted the molecular mechanisms of antioxidants involved in the prevention of age-related diseases. Taken together, a better understanding of the role of antioxidants involved in redox modulation of inflammation would provide a useful approach for potential interventions, and subsequently promoting healthy longevity

Human ecology in Kyrgyzstan: the Soviet legacy and the dynamics of transition

This paper focuses on some aspects of human development in Kyrgyzstan, one of the Newly Independent States (NIS) of the Former Soviet Union (FSU). The Soviet model of industrialisation and the inter-republican division of labour between the 'centre and the 'periphery', with 'ruthless exploitation of the country's abundant mineral resources, such as gold, uranium, antimony and mercury, determined much of the current structure of the Kyrgyz economy. The impacts of this model on human development, analysed from a human ecological perspective, were until recently a 'terra incognito'. However, during the late 1980s, and in particular after independence in 1991 research in this field has begun to develop. The paper first investigates the conditions of 'high mountain environment', to which native inhabitants adapted their human physiology. However, these conditions -at the high altitudes where most of the Kyrgyz mines are located- turned out to be quite hostile for the many new comers (soldiers, miners and other workers), in particular when the environmental pollution caused by the mine exploitation is taken into account. Age-gender data are presented for three regions (oblasts) and three villages, located at different altitudes. It is shown that the 'hidden' environmental disaster of 1964, caused by a heavily polluted mud-slide -originating in the Ak-tuz mine (of polymetal ores)- that swept through the Kichi-Kemin valley, is clearly visible with its demographic echo. Second, the transition period is discussed in light of the negative development of some important indicators of human development, such as birth and death rates, infant mortality and life expectancy. During the transition access to food diminished, public health systems collapsed and rural-urban migration increased unemployment and poverty. While the severe contraction of the economy, in particular in the industrial and mining sectors, caused an initial reduction in environmental pollution, such as in the emission of air pollutants, the current socio-economic crisis dramatically worsened human livelihood conditions in Kyrgyzstan

Human ecology in Kyrgyzstan : the Soviet legacy and the dynamics of transition

pollution;economic conditions;Kyrgyzstan;economies in transition;human ecology

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/β-catenin, PI3K/Akt, integrin αvβ3/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, phosphatidyl-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

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/β-catenin, PI3K/Akt, integrin αvβ3/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

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

Evolutionary Conserved Short Linear Motifs Provide Insights into the Cellular Response to Stress

Short linear motifs (SLiMs) are evolutionarily conserved functional modules of proteins composed of 3 to 10 residues and involved in multiple cellular functions. Here, we performed a search for SLiMs that exert sequence similarity to two segments of alpha-fetoprotein (AFP), a major mammalian embryonic and cancer-associated protein. Biological activities of the peptides, LDSYQCT (AFP14–20) and EMTPVNPGV (GIP-9), have been previously confirmed under in vitro and in vivo conditions. In our study, we retrieved a vast array of proteins that contain SLiMs of interest from both prokaryotic and eukaryotic species, including viruses, bacteria, archaea, invertebrates, and vertebrates. Comprehensive Gene Ontology enrichment analysis showed that proteins from multiple functional classes, including enzymes, transcription factors, as well as those involved in signaling, cell cycle, and quality control, and ribosomal proteins were implicated in cellular adaptation to environmental stress conditions. These include response to oxidative and metabolic stress, hypoxia, DNA and RNA damage, protein degradation, as well as antimicrobial, antiviral, and immune response. Thus, our data enabled insights into the common functions of SLiMs evolutionary conserved across all taxonomic categories. These SLiMs can serve as important players in cellular adaptation to stress, which is crucial for cell functioning