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Previous studies have associated certain variations in genes encoding factors of renin-angiotensin system (RAS), indirectly leading to higher angiotensin II (AngII) levels, with greater risk for basal cell carcinoma (BCC) development. Chymase (CMA1) is the main regulator of the RAS-independent AngII generation pathway and numerous studies have shown its oncogenic potential in several cancer types including BCC. In this study, we investigated the possible association between BCC pathogenesis and the functional DNA polymorphism A1903G (rs1800875) that affects expression of the CMA1 gene.We genotyped 199 DNA samples, isolated from 100 BCC patients and 99 age, sex, and ethnicity-matched healthy controls for the CMA1 A1903G polymorphism. Genotyping was performed with PCR amplification, followed by MboI enzyme digestion and agarose gel electrophoresis of the resulted DNA fragments.The variant G allele that possibly increases CMA1 gene expression was not detected at a significantly different frequency between the groups of BCC patients and healthy controls. However, the AG heterozygous genotype was significantly increased in BCC patients compared with controls (p0.001).The high expression CMA1 G allele carriers have an increased risk for BCC and elevated levels of chymase in the skin may have a carcinogenic effect.

Financial stress can negatively affect a couple's relationship. The Dyadic Coping Inventory for Financial Stress (DCIFS) instrument assesses the way couples cope with financial stress. This study sought to validate the Dyadic Coping Inventory for Financial Stress (DCIFS) in Greek. The sample included 152 Greek couples (mean age: 42.82 ± 11.94). Confirmatory factor analyses provided support for delegated dyadic coping and evaluation of dyadic coping. Confirmatory Factor Analysis results supported a 33‐item version consisting of the following subscales for both men and women: Stress Communication by Oneself and by Partner, Emotion and Problem‐Focused Supportive Dyadic Coping (DC) by Oneself and by Partner, Negative DC by Oneself and by Partner, Emotion and Problem‐Focused Common DC, and Evaluation of DC. The Dyadic Coping Inventory questionnaire and Perceived Stress Scale were used to assess the criterion validity of DCIFS.

Over the past few decades, research at the molecular level has focused on the part of the genome that does not encode protein sequences. Since the discovery of transcriptional evidence from the hitherto considered 'junk' DNA, this region of the genome, which is currently termed dark DNA, is constantly gaining interest. The term borrows an analogy from the corresponding eminent fields of dark matter and dark energy in physics and cosmology. In fact, an increasing number of attempts are being made to enhance the current understanding of the non‑coding RNA (ncRNA) transcripts produced by such regions. Although the base‑pair length and gene number appear to be very diverse between species, it appears that the amount of the non‑coding regions of the genome of an organism is a sign of evolutional superiority. ncRNA molecules are able to orchestrate the expression of genetic information in the most complex, rapid and reversible manner, participating in almost every major biological process. A prime example of such a process is the maintenance of homeostasis, the internal physiological balance, despite internal and external stressful stimuli. These molecules have been shown to be excellent regulators of gene expression, with marked spatiotemporal specificity, rendering them ideal tools for regulating stress responses. Herein, an attempt is made to extract and fuse information from a repertoire of studies, which have demonstrated that the expression of a number of these molecules was modified following exposure to acute and chronic stress, as well as in patients with anxiety disorders and their respective animal models. All in all, ncRNAs have the potential to be used either as biomarkers or as therapeutic targets for disorders resulting from the loss of equilibrium, the disruption of homeostasis and the destabilization of the hypothalamic‑pituitary‑adrenal axis.

Alzheimer's disease (AD) is a neurodegenerative disorder that has a significant association with age. Despite its increasing incidence in the population, the etiology of the disease remains poorly understood, and there are currently no effective treatments readily available. The main genes that are associated with AD are the amyloid precursor protein, presenilin‑1 and presenilin‑2, as well as the apolipoprotein E gene. In addition to genetic factors, a wide range of environmental and lifestyle factors are equally characterized as risk factors for the development of AD, while non‑coding RNAs (ncRNAs) and other epigenetic mechanisms play a key role in their detrimental effects. Multiple types of ncRNAs, such as microRNAs, circular RNAs, Piwi‑interacting RNAs and long non‑coding RNAs are being increasingly implicated in AD. Alterations in ncRNAs can be detected in cerebrospinal fluid, as well in as the brain, highlighting these as promising biomarkers for the detection and treatment of AD. Developments in high‑throughput technologies have led to the so‑called 'omics' era, which involves the collection of big data and information at both molecular and protein levels, while combining the development of novel computational and statistical tools capable of analyzing and filtering such data. The present review discusses the role of ncRNAs and their use as biomarkers for AD, and summarizes the findings from the application of omics technologies in AD.

Huntington's disease (HD) is a neurodegenerative disorder characterized by severe motor, cognitive and psychiatric symptoms. Patients of all ages can present with a dysfunction of the nervous system, which leads to the progressive loss of movement control and disabilities in speech, swallowing, communications, etc. The molecular basis of the disease is well-known, as HD is related to a mutated gene, a trinucleotide expansion, which encodes to the huntingtin protein. This protein is linked to neurogenesis and the loss of its function leads to neurodegenerative disorders. Although the genetic cause of the disorder has been known for decades, no effective treatment is yet available to prevent onset or to eliminate the progression of symptoms. Thus, the present review focused on the development of novel methods for the timely and accurate diagnosis of HD in an aim to aid the development of therapies which may reduce the severity of the symptoms and control their progression. The majority of the therapies include gene-silencing mechanisms of the mutated huntingtin gene aiming to suppress its expression, and the use of various substances as drugs with highly promising results. In the present review, the latest approaches on the diagnosis of HD are discussed along with the need for genetic counseling and an up-to-date presentation of the applied treatments.

Viral infections constitute a fundamental and continuous challenge for the global scientific and medical community, as highlighted by the ongoing COVID-19 pandemic. In combination with prophylactic vaccines, the development of safe and effective antiviral drugs remains a pressing need for the effective management of rare and common pathogenic viruses. The design of potent antivirals can be informed by the study of the three-dimensional structure of viral protein targets. Structure-based design of antivirals

Multiple sclerosis (MS) is an autoimmune neurodegenerative disease whose prevalence has increased worldwide. The resultant symptoms may be debilitating and can substantially reduce the of patients. Computational biology, which involves the use of computational tools to answer biomedical questions, may provide the basis for novel healthcare approaches in the context of MS. The rapid accumulation of health data, and the ever-increasing computational power and evolving technology have helped to modernize and refine MS research. From the discovery of novel biomarkers to the optimization of treatment and a number of quality-of-life enhancements for patients, computational biology methods and tools are shaping the field of MS diagnosis, management and treatment. The final goal in such a complex disease would be personalized medicine, i.e., providing healthcare services that are tailored to the individual patient, in accordance to the particular biology of their disease and the environmental factors to which they are subjected. The present review article summarizes the current knowledge on MS, modern computational biology and the impact of modern computational approaches of MS.

The synthesis and release of glucocorticoids in living organisms are related to their response to unfavorable stressful conditions in order to maintain homeostatic functions and survive. One such hormone in humans is cortisol, which is produced by the hypothalamic‑pituitary‑adrenal cortex axis and binds with the glucocorticoid receptor (GR) following its secretion. GR controls a number of distinct gene networks. Non‑coding RNAs (ncRNAs), such as microRNAs (miRNAs) and long non‑coding RNAs (lncRNAs), regulate the expression and function of GR, having a considerable impact on various biological processes and treatment approaches for numerous disorders. In the present review, the GR pathways and signaling as part of the stress response system are discussed. A detailed report on the role of miRNAs and lncRNAs in glucocorticoid signaling is also presented.

Healthy eating has gained ground in people's daily lives in modern society. However, an overwhelming preoccupation with healthy eating can lead to a pathological form setting the ground for orthorexia nervosa. This study aimed to validate the Greek version of the Eating Habits Questionnaire (EHQ) in adults 18 to 65 years old. The EHQ evaluates orthorexia nervosa traits. An online survey was conducted among adults of the general Greek population by administrating a battery of self-report instruments. The IPIP Big-Five personality questionnaire, Beck’s Depression Inventory, the Obsessive-Compulsive Inventory-Revised, the Bulimic Investigatory Test, the Edinburg BITE, and the Eating Attitudes Test-13 were used. Internal consistency, test-retest reliability, and convergent and criterion validity were examined. A total of 551 adults (92.2% females) voluntarily participated in the study. Results suggest that the Greek version of the instrument has good psychometric properties. Analysis revealed a 3-factor model explaining 48.20% of the total variance. Cronbach’s alphas ranged between 0.80 to 0.82, indicating good internal consistency. The test-retest reliability analysis showed no statistically significant difference between the measurements of the first and the post-2 weeks. Correlations with other eating disorder-related constructs were found to be weak to moderate. Body mass index was not significantly correlated with neither of the three EHQ subscales. The Greek version of EHQ is a robust instrument that could be used in clinical practice and research in the field of eating disorders in Greece.

Many people are worried about their social appearance. The fear of negative evaluation and judgment regarding one’s look in social circumstances is referred to as social appearance anxiety. Social appearance anxiety belongs to social anxiety. The aim of the present study was to validate the Social Appearance Anxiety Scale (SAAS) in the Greek language and to examine its psychometric properties. An online survey was conducted in a Greek population sample of adolescents and young adults aged 18 to 35 years. The survey instruments included the Social Appearance Anxiety Scale, the Social Physique Anxiety Scale (SPAS), 2 subscales of Multidimensional Body-Self Relations Questionnaire Appearance Scale (MBSRQ), the Appearance Schemas Inventory-Revised Scale (ASI-R) and the Depression Anxiety Stress Scale (DASS). A total of 429 respondents participated in this research. The statistical analysis showed that the Greek version of the SAAS has good psychometric properties. The internal consistency of questions within the SAAS was 0.942. Positive correlations were found between SAAS and SPAS, the overweight preoccupation subscale of MBSRQ, the ASI-R and the DASS, while negative correlations were observed between SAAS and the appearance evaluation subscale of MBSRQ and age. The results of this study suggest that the Greek version of SAAS can be used as a reliable and valid instrument in the Greek population.

Micronutrients constitute an adjuvant treatment for respiratory viral infections. Since there is no effective antiviral therapy for COVID-19 yet, adjuvant intervention for the survival of critically ill patients may be significant. Search of the PubMed, CINAHL and Cochrane databases was carried out to find human studies investigating the prognostic role of micronutrient status and the effects of micronutrient supplementation intervention in COVID-19 outcomes of adult patients. Patients with certain comorbidities (diabetes mellitus type 2, obesity, renal failure, liver dysfunction etc.) or pregnant women were excluded. 31 studies (27 observational studies and 4 clinical trials) spanning the years 2020-2021, pertaining to 8624 COVID-19 patients (mean age±SD, 61 ± 9 years) were included in this systematic review. Few studies provided direct evidence on the association of serum levels of vitamin D, calcium, zinc, magnesium, phosphorus and selenium to patients' survival or death. Vitamin D and calcium were the most studied micronutrients and those with a probable promising favorable impact on patients. This review highlights the importance of a balanced nutritional status for a favorable outcome in COVID-19. Micronutrients' deficiency on admission to hospital seems to be related to a high risk for ICU admission, intubation and even death. Nevertheless, evidence for intervention remains unclear.

Severe early-onset obesity is mainly attributed to single gene variations of the hypothalamic leptin-melanocortin system, which is critical for controlling the balance between appetite and energy expenditure. Adenylate cyclase 3 (ADCY3), a transmembrane enzyme localized in primary neuronal cilia, is a key genetic candidate, which appears to have an essential role in regulating body weight. The present study aimed to identify ADCY3 genetic variants in severely obese young patients of Greek-Cypriot origin by genomic sequencing. Apart from previously reported variants, the novel and probably pathogenic variant c.349T>A, causing a p.Leu117Met substitution within one of the two pseudo-symmetric halves of the transmembrane part of the protein, was reported. Molecular modelling analysis used to delineate bonding interactions within the mutated protein structure strongly suggested a change in interactive forces and energy levels affecting the pseudo-twofold symmetry of the transmembrane domain of the protein and probably its catalytic function. These results support the involvement of ADCY3 in the pathology of the disease and point towards the requirement of defining protein function and evaluating the clinical significance of the detected variants.

RNA modifications have recently become the focus of attention due to their extensive regulatory effects in a vast array of cellular networks and signaling pathways. Just as epigenetics is responsible for the imprinting of environmental conditions on a genetic level, epitranscriptomics follows the same principle at the RNA level, but in a more dynamic and sensitive manner. Nevertheless, its impact in the field of cardiovascular disease (CVD) remains largely unexplored. CVD and its associated pathologies remain the leading cause of death in Western populations due to the limited regenerative capacity of the heart. As such, maintenance of cardiac homeostasis is paramount for its physiological function and its capacity to respond to environmental stimuli. In this context, epitranscriptomic modifications offer a novel and promising therapeutic avenue, based on the fine‑tuning of regulatory cascades, necessary for cardiac function. This review aimed to provide an overview of the most recent findings of key epitranscriptomic modifications in both coding and non‑coding RNAs. Additionally, the methods used for their detection and important associations with genetic variations in the context of CVD were summarized. Current knowledge on cardiac epitranscriptomics, albeit limited still, indicates that the impact of epitranscriptomic editing in the heart, in both physiological and pathological conditions, holds untapped potential for the development of novel targeted therapeutic approaches in a dynamic manner.

Breast milk is the ideal food for infants and undoubtedly has immediate and long‑term benefits. Breast milk contains extracellular vesicles (EVs) i.e., exosomes secreted by maternal breast cells. Exosomes carry genetic material, such as long non‑coding RNAs (lncRNAs), which possibly participate in cell‑to‑cell communications, as they are known to regulate critical gene pathways. The aim of the present study was to screen human breastmilk exosomes for their lncRNA cargo and to examine exosomal lncRNA levels associated with milk obtained from mothers that gave birth at term or prematurely (

Demetra Application is a holistic integrated and scalable bioinformatics web‑based tool designed to assist medical experts and researchers in the process of diagnosing endometriosis. The application identifies the most prominent gene variants and single nucleotide polymorphisms (SNPs) causing endometriosis using the genomic data provided for the patient by a medical expert. The present study analyzed >28.000 endometriosis‑related publications using data mining and semantic techniques aimed towards extracting the endometriosis‑related genes and SNPs. The extracted knowledge was filtered, evaluated, annotated, classified, and stored in the Demetra Application Database (DAD). Moreover, an updated gene regulatory network with the genes implements in endometriosis was established. This was followed by the design and development of the Demetra Application, in which the generated datasets and results were included. The application was tested and presented herein with whole‑exome sequencing data from seven related patients with endometriosis. Endometriosis‑related SNPs and variants identified in genome‑wide association studies (GWAS), whole‑genome (WGS), whole‑exome (WES), or targeted sequencing information were classified, annotated and analyzed in a consolidated patient profile with clinical significance information. Probable genes associated with the patient's genomic profile were visualized using several graphs, including chromosome ideograms, statistic bars and regulatory networks through data mining studies with relative publications, in an effort to obtain a representative number of the most credible candidate genes and biological pathways associated with endometriosis. An evaluation analysis was performed on seven patients from a three‑generation family with endometriosis. All the recognized gene variants that were previously considered to be associated with endometriosis were properly identified in the output profile per patient, and by comparing the results, novel findings emerged. This novel and accessible webserver tool of endometriosis to assist medical experts in the clinical genomics and precision medicine procedure is available at http://geneticslab.aua.gr/.

Monocarboxylate transporters (MCTs) are of great research interest for their role in cancer cell metabolism and their potential ability to transport pharmacologically relevant compounds across the membrane. Each member of the MCT family could potentially provide novel therapeutic approaches to various diseases. The major differences among MCTs are related to each of their specific metabolic roles, their relative substrate and inhibitor affinities, the regulation of their expression, their intracellular localization, and their tissue distribution. MCT4 is the main mediator for the efflux of L-lactate produced in the cell. Thus, MCT4 maintains the glycolytic phenotype of the cancer cell by supplying the molecular resources for tumor cell proliferation and promotes the acidification of the extracellular microenvironment from the co-transport of protons. A promising therapeutic strategy in anti-cancer drug design is the selective inhibition of MCT4 for the glycolytic suppression of solid tumors. A small number of studies indicate molecules for dual inhibition of MCT1 and MCT4, however, no selective inhibitor with high-affinity for MCT4 has been identified. In this study, we attempt to approach the structural characteristics of MCT4 through an in silico pipeline for molecular modelling and pharmacophore elucidation towards the identification of specific inhibitors as a novel anti-cancer strategy.

The novel coronavirus SARS-CoV-2 has emerged as a severe threat against public health and global economies. COVID-19, the disease caused by this virus, is highly contagious and has led to an ongoing pandemic. SARS-CoV-2 affects, mainly, the respiratory system, with most severe cases primarily showcasing acute respiratory distress syndrome. Currently, no targeted therapy exists, and since the number of infections and death toll keeps rising, it has become a necessity to study possible therapeutic targets. Antiviral drugs can target various stages of the viral infection, and in the case of SARS-CoV-2, both structural and non-structural proteins have been proposed as potential drug targets. This review focuses on the most researched SARS-CoV-2 proteins, their structure, function, and possible therapeutic approaches., Highlights • Structural and non-structural proteins of SARS-CoV-2 are potential drug targets. • Spike glycoprotein and viral proteases may be promising targets against COVID-19. • Multi-stage viral entry/replication/viral vesicle formation are therapeutic targets. • Toxicity is a critical component in the development of safe and effective drugs.

Notch family proteins play a key role in a variety of developmental processes by controlling cell fate decisions and operating in a great number of biological processes in several organ systems, such as hematopoiesis, somatogenesis, vasculogenesis, neurogenesis and homeostasis. The Notch signaling pathway is crucial for the majority of developmental programs and regulates multiple pathogenic processes. Notch family receptors’ activation has been largely related to its multiple effects in sustaining oncogenesis. The Notch signaling pathway constitutes an ancient and conserved mechanism for cell to cell communication. Much of what is known about Notch family proteins function comes from studies done inCaenorhabditis ElegansandDrosophila Melanogaster. Although,humanNotch homologs had also been identified, the molecular mechanisms which modulate the Notch signaling pathway remained substantially unknown. In this study, an updated evolutionary analysis of the Notch family members among 603 different organisms of all kingdoms, frombacteriatohumans, was performed in order to discover key regions that have been conserved throughout evolution and play a major role in the Notch signaling pathway. The major goal of this study is the presentation of a novel updated phylogenetic tree for the Notch family as a reliable phylogeny “map”, in order to correlate information of the closely related members and identify new possible pharmacological targets that can be used in pathogenic cases, including cancer.

Erwinia carotovora, a widespread plant pathogen that causes soft rot disease in many plants, is considered a major threat in agriculture. Bacterial glutathione transferases (GSTs) play important roles in a variety of metabolic pathways and processes, such as the biodegradation of xenobiotics, protection against abiotic stress, and resistance against antimicrobial drugs. The GST family of canonical soluble enzymes from Erwinia carotovora subsp. atroseptica strain SCRI1043 (EcaGSTs) was investigated. Genome analysis showed the presence of six putative canonical cytoplasmic EcaGSTs, which were revealed by phylogenetic analysis to belong to the well-characterized GST classes beta, nu, phi, and zeta. The analysis also revealed the presence of two isoenzymes that were phylogenetically close to the omega class of GSTs, but formed a distinct class. The EcaGSTs were cloned and expressed in Escherichia coli, and their catalytic activity toward different electrophilic substrates was elucidated. The EcaGSTs catalyzed different types of reactions, although all enzymes were particularly active in reactions involving electrophile substitution. Gene and protein expression profiling conducted under normal culture conditions as well as in the presence of the herbicide alachlor and the xenobiotic 1-chloro-2,4-dinitrobenzene (CDNB) showed that the isoenzyme EcaGST1, belonging to the omega-like class, was specifically induced at both the protein and mRNA levels. EcaGST1 presumably participates in counteracting the xenobiotic toxicity and/or abiotic stress conditions, and may therefore represent a novel molecular target in the development of new chemical treatments to control soft rot diseases.

In the present study, a structure-based approach was exploited for the in silico design of a nucleotide coenzyme mimetic ligand. The enzyme formate dehydrogenase (FDH) was employed as a model in our study. The biomimetic ligand was designed and synthesized based on a tryptamine/3-aminopropylphosphonic acid bi-substituted 1,3,5-triazine (Trz) scaffold (Tra-Trz-3APP), which potentially mimics the interactions of NAD+-FDH complex. Molecular docking studies of the biomimetic ligand predicted that it can occupy the same binding site as the natural coenzyme. Molecular modeling and dynamics simulations revealed that the ligand binds in an energetically more stable pose in the FDH binding site, as it adopts a more twisty conformation, compared to the natural coenzyme. Study of the FDH/Tra-Trz-3APP-Sepharose interaction, through adsorption equilibrium studies and site-directed mutagenesis of selected FDH coenzyme binding residues, provided additional experimental evidences of the specificity of the interaction. The Tra-Trz-3APP-Sepharose biomimetic adsorbent was further evaluated towards a range of different dehydrogenases and was exploited for the development of a single-step purification protocol for FDH. The protocol afforded enzyme with high yield and purity, suitable for analytical and industrial purposes.

Amyotrophic lateral sclerosis (ALS) belongs to the ALS-frontotemporal dementia (FTD) spectrum and is hallmarked by upper and lower motor neuron degeneration. Here, we present a patient with a cytoplasmic dynein 1 heavy chain 1 (DYNC1H1) pathogenic variant who fulfilled the ALS El Escorial criteria, and we review relevant literature. Using whole-exome sequencing, we identified a deleterious point variant in DYNC1H1 [c.4106A>G (p. Q1369R))] as a likely contributor to the ALS phenotype. In silico structural analysis, molecular dynamics simulation, and protein stability analysis predicted that this variant may increase DYNC1H1 protein stability. Moreover, this variant may disrupt binding of the transcription factor TFAP4, thus potentially acting as duon. Since a) DYNC1H1 forms part of a ubiquitous eukaryotic motor protein complex, and b) disruption of dynein function by perturbation of the dynein-dynactin protein complex is implicated in other motor neuron degenerative conditions, this variant could disrupt processes like retrograde axonal transport, neuronal migration, and protein recycling. Our findings expand the heterogenous spectrum of DYNC1H1 pathogenic variants−associated phenotype and prompt further investigations of the role of this gene in ALS.

The treatment of complex and multifactorial diseases constitutes a big challenge in day-to-day clinical practice. As many parameters influence clinical phenotypes, accurate diagnosis and prompt therapeutic management is often difficult. Significant research and investment focuses on state-of-the-art genomic and metagenomic analyses in the burgeoning field of Precision (or Personalized) Medicine with genome-wide-association-studies (GWAS) helping in this direction by linking patient genotypes at specific polymorphic sites (single-nucleotide polymorphisms, SNPs) to the specific phenotype. The generation of polygenic risk scores (PRSs) is a relatively novel statistical method that associates the collective genotypes at many of a person’s SNPs to a trait or disease. As GWAS sample sizes increase, PRSs may become a powerful tool for prevention, early diagnosis and treatment. However, the complexity and multidimensionality of genetic and environmental contributions to phenotypes continue to pose significant challenges for the clinical, broad-scale use of PRSs. To improve the value of PRS measures, we propose a novel pipeline which might better utilize GWAS results and improve the utility of PRS when applied to Alzheimer’s Disease (AD), as a paradigm of multifactorial disease with existing large GWAS datasets that have not yet achieved significant clinical impact. We propose a refined approach for the construction of AD PRS improved by (1), taking into consideration the genetic loci where the SNPs are located, (2) evaluating the post-translational impact of SNPs on coding and non-coding regions by focusing on overlap with open chromatin data and SNPs that are expression quantitative trait loci (QTLs), and (3) scoring and annotating the severity of the associated clinical phenotype into the PRS. Open chromatin and eQTL data need to be carefully selected based on tissue/cell type of origin (e.g., brain, excitatory neurons). Applying such filters to traditional PRS on GWAS studies of complex diseases like AD, can produce a set of SNPs weighted according to our algorithm and a more useful PRS. Our proposed methodology may pave the way for new applications of genomic machine and deep learning pipelines to GWAS datasets in an effort to identify novel clinically useful genetic biomarkers for complex diseases like AD.

Antibodies, also called immunoglobulins, are large Y-shaped proteins which are produced and used by the immune system in order to identify and neutralize foreign substances called antigens. The study and research of antibodies is a critical issue in science and has led in remarkable results with medical applications such as diseases diagnosis, therapies and antibodies related drugs. Due to the importance of antibodies, a comprehensive database of full length protein sequences or crystal structures of immunoglobulin (IG) and T cell receptor (TR) V-like protein sequences from human and other vertebrate species has been created. In this article, we study in detail the unique numbering system for immunoglobulins of IMGT, we correlate the database antibody entries and we provide a more in detailed approach into the data. Finally, due to the deviation of the expected from the real data, a user friendly application has been created. This powerful and flexible tool manipulates the antibodies V-like protein sequences with the approach described below and gives the opportunity to the user to extract and study the results and its physicochemical properties. The need for new therapeutic targets and the development of more potent drugs is enormous, despite the efforts and the investments all these years for the development of novel next generation drugs, the discovery and the characterization of new therapeutic targets using immunoglobulins or antibodies. The final outcome from this project will support the development of novel therapies. Antisoma can be freely downloaded from http://www.dimvl.com/antisoma.

Obesity and thus, lipotoxicity, is a major health risk factor. Modern exposure to environmental chemicals has contributed significantly to the obesity epidemic. The purpose of this study was to assess, via telemedicine and using bioelectrical impedance analysis (BIA) in schools, the levels of adiposity and other body composition parameters of Greek adolescents in relation with their metabolic syndrome (MetS) characteristics. A representative sample (1575 adolescents, 14.4 ± 1.7 years-old) of the Attica region population, underwent body composition assessment of fat mass (FM), fat-free mass (FFM), and total body water (TBW) and was evaluated for anthropometric and MetS characteristics. Males demonstrated higher FFM% and TBW% but lower FM% than females. Adolescents with abdominal obesity/MetS (n = 149/n = 40) demonstrated significantly (P Findings suggest that early “osteosarcopenic” elements of abdominal obesity/MetS may exist even in adolescence. The application of BIA, incorporated in the new approach methodology of telemedicine in schools, identified adolescents at risk for obesity complications.

During the past decade, with the significant progress of computational power as well as ever-rising data availability, deep learning techniques became increasingly popular due to their excellent performance on computer vision problems. The size of the Protein Data Bank has increased more than 15 fold since 1999, which enabled the expansion of models that aim at predicting enzymatic function via their amino acid composition. Amino acid sequence however is less conserved in nature than protein structure and therefore considered a less reliable predictor of protein function. This paper presents EnzyNet, a novel 3D-convolutional neural networks classifier that predicts the Enzyme Commission number of enzymes based only on their voxel-based spatial structure. The spatial distribution of biochemical properties was also examined as complementary information. The 2-layer architecture was investigated on a large dataset of 63,558 enzymes from the Protein Data Bank and achieved an accuracy of 78.4% by exploiting only the binary representation of the protein shape. Code and datasets are available at https://github.com/shervinea/enzynet., Comment: 11 pages, 6 figures

The number of protein structures in the PDB database has been increasing more than 15-fold since 1999. The creation of computational models predicting enzymatic function is of major importance since such models provide the means to better understand the behavior of newly discovered enzymes when catalyzing chemical reactions. Until now, single-label classification has been widely performed for predicting enzymatic function limiting the application to enzymes performing unique reactions and introducing errors when multi-functional enzymes are examined. Indeed, some enzymes may be performing different reactions and can hence be directly associated with multiple enzymatic functions. In the present work, we propose a multi-label enzymatic function classification scheme that combines structural and amino acid sequence information. We investigate two fusion approaches (in the feature level and decision level) and assess the methodology for general enzymatic function prediction indicated by the first digit of the enzyme commission (EC) code (six main classes) on 40,034 enzymes from the PDB database. The proposed single-label and multi-label models predict correctly the actual functional activities in 97.8% and 95.5% (based on Hamming-loss) of the cases, respectively. Also the multi-label model predicts all possible enzymatic reactions in 85.4% of the multi-labeled enzymes when the number of reactions is unknown. Code and datasets are available athttps://figshare.com/s/a63e0bafa9b71fc7cbd7.

Microalgae are unicellular microorganisms indispensible for environmental stability and life on earth, because they produce approximately half of the atmospheric oxygen, with simultaneously feeding on the harmful greenhouse gas carbon dioxide. Using gene fusion analysis, a series of five fusion/fission events was identified, that provided the basis for critical insights to their evolutionary history. Moreover, the three-dimensional structures of both the fused and the component proteins were predicted, allowing us to envisage putative protein-protein interactions that are invaluable for the efficient usage, handling and exploitation of microalgae. Collectively, our proposed approach on the five fusion/fission alga protein events contributes towards the expansion of the microalgae knowledgebase, bridging protein evolution of the ancient microalgal species and the rapidly evolving, modern, bioinformatics field. (C) 2013 Elsevier B.V. All rights reserved.

Poly(A)-specific ribonuclease (PARN) is a 3′-exoribonuclease that efficiently degrades poly(A) tails and regulates, in part, mRNA turnover rates. We have previously reported that adenosine- and cytosine-based glucopyranosyl nucleoside analogues with adequate tumour-inhibitory effect could effectively inhibit PARN. In the present study we dissect the mechanism of a more drastic inhibition of PARN by novel glucopyranosyl analogues bearing uracil, 5-fluorouracil or thymine as the base moiety. Kinetic analysis showed that three of the compounds are competitive inhibitors of PARN with Ki values in the low μM concentration and significantly lower (11- to 33-fold) compared to our previous studies. Detailed kinetic analysis of the most effective inhibitor, the uracil-based nucleoside analogue (named U1), revealed slow-binding behaviour. Subsequent molecular docking experiments showed that all the compounds which inhibited PARN can efficiently bind into the active site of the enzyme through specific interactions. The present study dissects the inhibitory mechanism of this novel uracil-based compound, which prolongs its inhibitory effect through a slow-binding and slow-release mode at the active site of PARN, thus contributing to a more efficient inhibition. Such analogues could be used as leading compounds for further rationale design and synthesis of efficient and specific therapeutic agents. Moreover, our data reinforce the notion that human PARN can be established as a novel molecular target of potential anti-cancer agents through lowering mRNA turnover rates.

During the past few years, pharmacophore modeling has become one of the key components in computer-aided drug design and in modern drug discovery. DrugOn is a fully interactive pipeline designed to exploit the advantages of modern programming and overcome the command line barrier with two friendly environments for the user (either novice or experienced in the field of Computer Aided Drug Design) to perform pharmacophore modeling through an efficient combination of the PharmACOphore, Gromacs, Ligbuilder and PDB2PQR suites. Our platform features a novel workflow that guides the user through each logical step of the iterative 3D structural optimization setup and drug design process. For the pharmacophore modeling we are focusing on either the characteristics of the receptor or the full molecular system, including a set of selected ligands. DrugOn can be freely downloaded from our dedicated server system at www.bioacademy.gr/bioinformatics/drugon/.

Background Natural mutations in the human glucocorticoid receptor (hGR, NR3C1) gene cause Chrousos syndrome, a rare condition characterized by generalized, partial, target-tissue insensitivity to glucocorticoids. Objective To present a new case of Chrousos syndrome caused by a novel mutation in the hGR gene, and to elucidate the molecular mechanisms through which the natural mutant receptor affects glucocorticoid signal transduction. Design and Results The index case presented with hirsutism, acne, alopecia, anxiety, fatigue and irregular menstrual cycles, but no clinical manifestations suggestive of Cushing's syndrome. Endocrinologic evaluation revealed elevated 08:00 h plasma adrenocorticotropic hormone, serum cortisol and androstenedione concentrations and increased urinary free cortisol excretion. The patient harbored a novel A > G transition at nucleotide position 2177, which resulted in histidine (H) to arginine (R) substitution at amino acid position 726 of the receptor (c.2177A > G, p.H726R). Compared with the wild-type receptor, the mutant receptor hGRαH726R demonstrated decreased ability to transactivate glucocorticoid-responsive genes and to transrepress the nuclear factor-κB signalling pathway, displayed 55% lower affinity for the ligand and a four-fold delay in nuclear translocation, and interacted with the glucocorticoid receptor-interacting protein 1 coactivator mostly through its activation function-1 domain. Finally, a 3-dimensional molecular modelling study of the H726R mutation revealed a significant structural shift in the rigidity of helix 10 of the receptor, which resulted in reduced flexibility and decreased affinity of the mutant receptor for binding to the ligand. Conclusions The natural mutant receptor hGRαH726R impairs multiple steps of glucocorticoid signal transduction, thereby decreasing tissue sensitivity to glucocorticoids.

Trypanosoma brucei brucei (TBB) belongs to the unicellular parasitic protozoa organisms, specifically to the Trypanosoma genus of the Trypanosomatidae class. A variety of different vertebrate species can be infected by TBB including humans and animals. Under particular conditions, the TBB can be hosted by wild and domestic animals; thereby an important reservoir of infection always remains available to transmit through the tsetse flies. Although the TBB parasite is one of the leading causes of death in the most underdeveloped countries, to date, there is neither vaccination available nor any drug against TBB infection. The subunit RPB1 of the TBB DNA-directed RNA polymerase II (DdRpII) constitutes an ideal target for the design of novel inhibitors, since it is instrumental role is vital for the parasite’s survival, proliferation, and transmission. A major goal of the described study is to provide insights for novel anti-TBB agents via a state of the art drug discovery approach of the TBB DdRpII RPB1. In an attempt to understand the function and action mechanisms of this parasite enzyme related to its molecular structure, an in-depth evolutionary study has been conducted in parallel to the in silico molecular designing of the 3D enzyme model, based on state of the art comparative modelling and molecular dynamics techniques. Based on theevolutionary studies results nine new invariant, first-time reported, highly conserved regions have been identified within the DdRpII family enzymes. Consequently, those patches have been examined both at the sequence and structural level and have been evaluated in regards to their pharmacological targeting appropriateness. Finally, the pharmacophore elucidation study enabled us to virtually in silico screenhundreds of compounds and evaluate their interaction capabilities with the enzyme. It was found that a series of Chlorine-rich set of compounds were the optimal inhibitors for the TBB DdRpII RPB1 enzyme. All-in-all, herein we present a series of new sites on the TBB DdRpII RPB1 of high pharmacological interest, alongside the construction of the 3D model of the enzyme and the suggestion of a new in silico pharmacophore model for fast screening of potential inhibiting agents.

The Classical Swine Fever virus (CSFV) is a major pathogen of livestock and belongs to the flaviviridae viral family. Even though there aren’t any verified zoonosis cases yet, the outcomes of CSFV epidemics have been devastating to local communities. In an effort to shed light to the molecular mechanisms underlying the structural and drug design potential of the viral helicase, the three dimensional structure of CSFV helicase has been modeled using conventional homology modeling techniques and the crystal structure of the Hepatitis C virus (HCV) as a template. The established structure of the CSFV helicase has been in silico evaluated for its viability using a repertoire of in silico tools. The ultimate goal of this study is to introduce the 3D conformation of the CSFV helicase as a reliable structure that may be used as the designing platform for de novo, structure-based drug design experiments. In this direction using the modeled structure of CSVF helicase, a 3D pharmacophore was designed. The pharmacophore comprises of a series of key characteristics that molecular inhibitors must satisfy in order to achieve maximum predicted affinity for the given enzyme. Overall, invaluable insights and conclusions are drawn from this structural study of the CSFV helicase, which may provide the scientific community with the founding plinth in the fight against CSFV infections through the perspective of the CSFV helicase as a potential pharmacological target. Notably, to date no antiviral agent is available against the CSFV nor is expected soon. Subsequently, there is urgent need for new modern and state-of-the-art antiviral strategies to be developed.

Viral RNA helicases are involved in duplex unwinding during the RNA replication of the virus. It is suggested that these helicases represent very promising antiviral targets. Viruses of the flaviviridae family are the causative agents of many common and devastating diseases, including hepatitis, yellow fever and dengue fever. As there is currently no available anti-Flaviviridae therapy, there is urgent need for the development of efficient anti-viral pharmaceutical strategies. Herein, we report the complete phylogenetic analysis across flaviviridae alongside a more in-depth evolutionary study that revealed a series of conserved and invariant amino acids that are predicted to be key to the function of the helicase. Structural molecular modelling analysis revealed the strategic significance of these residues based on their relative positioning on the 3D structures of the helicase enzymes, which may be used as pharmacological targets. We previously reported a novel series of highly potent HCV helicase inhibitors, and we now re-assess their antiviral potential using the 3D structural model of the invariant helicase residues. It was found that the most active compound of the series, compound C4, exhibited an IC50 in the submicromolar range, whereas its stereoisomer (compound C12) was completely inactive. Useful insights were obtained from molecular modelling and conformational search studies via molecular dynamics simulations. C12 tends to bend and lock in an almost “U” shape conformation, failing to establish vital interactions with the active site of HCV. On the contrary, C4 spends most of its conformational time in a straight, more rigid formation that allows it to successfully block the passage of the oligonucleotide in the ssRNA channel of the HCV helicase. This study paves the way and provides the necessary framework for the in-depth analysis required to enable the future design of new and potent anti-viral agents.

Protein structure is more conserved than sequence in nature. In this direction we developed a novel methodology that significantly improves conventional homology modelling when sequence identity is low, by taking into consideration 3D structural features of the template, such as size and shape. Herein, our new homology modelling approach was applied to the homology modelling of the RNA-dependent RNA polymerase (RdRp) of dengue (type II) virus. The RdRp of dengue was chosen due to the low sequence similarity shared between the dengue virus polymerase and the available templates, while purposely avoiding to use the actual X-ray structure that is available for the dengue RdRp. The novel approach takes advantage of 3D space corresponding to protein shape and size by creating a 3D scaffold of the template structure. The dengue polymerase model built by the novel approach exhibited all features of RNA-dependent RNA polymerases and was almost identical to the X-ray structure of the dengue RdRp, as opposed to the model built by conventional homology modelling. Therefore, we propose that the space-aided homology modelling approach can be of a more general use to homology modelling of enzymes sharing low sequence similarity with the template structures. © 2013 Dimitrios Vlachakis et al.

Antibodies appear to be the first line of defence in the adaptive immune response of vertebrates and thereby are involved in a multitude of biochemical mechanisms, such as regulation of infection, autoimmunity, and cancer. It goes without saying that a full understanding of antibody function is required for the development of novel antibody-interacting drugs. These drugs are the Antibody Drug Conjugates (ADCs), which are a new type of targeted therapy, used for example for cancer. They consist of an antibody (or antibody fragment such as a single-chain variable fragment [scFv]) linked to a payload drug (often cytotoxic). Because of the targeting, the side effects should be lower and give a wider therapeutic window. Overall, the underlying principle of ADCs is to discern the delivery of a drug that is cytotoxic to a target that is cancerous, hoping to increase the antitumoural potency of the original drug by reducing adverse effects and side effects, such as toxicity of the cancer target. This is a pioneering field that employs state-of-the-art computational and molecular biology methods in the fight against cancer using ADCs.

The Greek Goat Encephalitis virus (GGE) belongs to the Flaviviridae family of the genus Flavivirus. The GGE virus constitutes an important pathogen of livestock that infects the goat’s central nervous system. The viral enzymes of GGE, helicase and RNA-dependent RNA polymerase (RdRP), are ideal targets for inhibitor design, since those enzymes are crucial for the virus’ survival, proliferation and transmission. In an effort to understand the molecular structure underlying the functions of those viral enzymes, the three dimensional structures of GGE NS3 helicase and NS5 RdRP have been modelled. The models were constructed in silico using conventional homology modelling techniques and the known 3D crystal structures of solved proteins from closely related species as templates. The established structural models of the GGE NS3 helicase and NS5 RdRP have been evaluated for their viability using a repertoire of in silico tools. The goal of this study is to present the 3D conformations of the GGE viral enzymes as reliable structural models that could provide the platform for the design of novel anti-GGE agents.

We developed a novel method based on the Fourier analysis of protein molecular surfaces to speed up the analysis of the vast structural data generated in the post-genomic era. This method computes the power spectrum of surfaces of the molecular electrostatic potential, whose three-dimensional coordinates have been either experimentally or theoretically determined. Thus we achieve a reduction of the initial three-dimensional information on the molecular surface to the one-dimensional information on pairs of points at a fixed scale apart. Consequently, the similarity search in our method is computationally less demanding and significantly faster than shape comparison methods. As proof of principle, we applied our method to a training set of viral proteins that are involved in major diseases such as Hepatitis C, Dengue fever, Yellow fever, Bovine viral diarrhea and West Nile fever. The training set contains proteins of four different protein families, as well as a mammalian representative enzyme. We found that the power spectrum successfully assigns a unique signature to each protein included in our training set, thus providing a direct probe of functional similarity among proteins. The results agree with established biological data from conventional structural biochemistry analyses., Comment: 9 pages, 10 figures Published in PeerJ (2013), https://peerj.com/articles/185/