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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.

Cognitive and behavioral disorders are subgroups of mental health disorders. Both cognitive and behavioral disorders can occur in people of different ages, genders, and social backgrounds and they can cause serious physical, mental or social problems. The risk factors for these diseases are numerous, with a range from genetic and epigenetic factors to physical factors. In most cases, the appearance of such a disorder in an individual is a combination of his genetic profile and environmental stimuli. To date, researchers have not been able to identify the specific causes of these disorders and as such, there is urgent need for innovative study approaches. The aim of the present study was to identify the genetic factors which seem to be more directly responsible for the occurrence of a cognitive and/or behavioral disorder. More specifically, through bioinformatics tools and software as well as analytical methods such as systemic data and text mining, semantic analysis, and scoring functions, we extracted the most relevant single nucleotide polymorphisms (SNPs) and genes connected to these disorders. All the extracted SNPs were filtered, annotated, classified, and evaluated in order to create the “genomic grammar” of these diseases. The identified SNPs guided the search for top suspected genetic factors, dopamine receptors D and Neurotrophic Factor BDNF, for which regulatory networks were built. The identification of the “genomic grammar” and underlying factors connected to cognitive and behavioral disorders can aid the successful disease profiling, the establishment of novel pharmacological targets and provide the basis for personalized medicine, which takes into account the patient’s genetic background as well as epigenetic factors.

Mental disorders are strongly connected with several psychiatric conditions including depression, bipolar disorder, schizophrenia, eating disorder and suicides. There are many biological conditions and pathways that define these complicated illnesses. For example, eating disorders are complex mental health conditions that require the intervention of geneticists, psychiatrists and medical experts in order to alleviate their symptoms. A patient with suicidal ideation should first be identified and consequently monitored by a similar team of specialists. Both genetics and epigenetics can shed light on eating disorders and suicides as they are found in the main core of such investigations. In the present study, an analysis has been performed on two specific members of the GPCR family towards drawing conclusions regarding their functionality and implementation in mental disorders. Specifically, evolutionary and structural studies on the adrenoceptor alpha 2b (ADRA2B) and the 5-hydroxytryptamine receptor 1A (HTR1A) have been carried out. Both receptors are classified in the biogenic amine receptors sub-cluster of the GPCRs and have been connected in many studies with mental diseases and malnutrition conditions. The major goal of this study is the investigation of conserved motifs among biogenic amine receptors that play an important role in this family signaling pathway, through an updated evolutionary analysis and the correlation of this information with the structural features of the HTR1A and ADRA2B. Furthermore, structural comparison of ADRA2B, HTR1A, and other members of GPCRs related with mental disorders is performed.

All living organisms have been programmed to maintain a complex inner equilibrium called homeostasis, despite numerous adversities during their lifespan. Any threatening or perceived as such stimuli for homeostasis is termed a stressor, and a highly conserved response system called the stress response system has been developed to cope with these stimuli and maintain or reinstate homeostasis. The glucocorticoid receptor, a transcription factor belonging to the nuclear receptors protein superfamily, has a major role in the stress response system, and research on its’ interactome may provide novel information regarding the mechanisms underlying homeostasis maintenance. A list of 149 autosomal genes which have an essential role in GR function or are prime examples of GRE-containing genes was composed in order to gain a comprehensive view of the GR interactome. A search for SNPs on those particular genes was conducted on a dataset of 3.554 Japanese individuals, with mentioned polymorphisms being annotated with relevant information from the ClinVar, LitVar, and dbSNP databases. Forty-two SNPs of interest and their genomic locations were identified. These SNPs have been associated with drug metabolism and neuropsychiatric, metabolic, and immune system disorders, while most of them were located in intronic regions. The frequencies of those SNPs were later compared with a dataset consisting of 1465 Korean individuals in order to find population-specific characteristics based on some of the identified SNPs of interest. The results highlighted that rs1043618 frequencies were different in the two populations, with mentioned polymorphism having a potential role in chronic obstructive pulmonary disease in response to environmental stressors. This SNP is located in the HSPA1A gene which codes for an essential GR co-chaperone, and such information showcases that similar gene may be novel genomic targets for managing or combatting stress-related pathologies.

TANK binding kinase 1 protein (TBK1) is a kinase that belongs to the IκB (IKK) family. TBK1, also known as T2K, FTDALS4, NAK, IIAE8 and NF-κB, is responsible for the phosphorylation of the amino acid residues Serine and Threonine. This enzyme is involved in various key biological processes, including interferon activation and production, homeostasis, cell growth, autophagy, insulin production and the regulation of TNF-α, IFN-β and IL-6. Mutations in the TBK1 gene alter the protein’s normal function and may lead to an array of pathological conditions, including disorders of the Central Nervous System. The present study sought to elucidate the role of the TBK1 protein in Amyotrophic Lateral Sclerosis (ALS), a human neurodegenerative disorder. A broad evolutionary and phylogenetic analysis of TBK1 was performed across numerous organisms to distinguish conserved regions important for the protein’s function. Subsequently, mutations and SNPs were explored and their potential effect on the enzyme’s function was investigated. These analytical steps, in combination with the study of the secondary, tertiary, and quaternary structure of TBK1, enabled the identification of conserved motifs, which can function as novel pharmacological targets and inform therapeutic strategies for Amyotrophic Lateral Sclerosis.

SARS-CoV-2 is a coronavirus responsible for one of the most serious, modern worldwide pandemics, with lasting and multi-faceted effects. By late 2021, SARS-CoV-2 has infected more than 180 million people and has killed more than 3 million. The virus gains entrance to human cells through binding to ACE2 via its surface spike protein and causes a complex disease of the respiratory system, termed COVID-19. Vaccination efforts are being made to hinder the viral spread and therapeutics are currently under development. Towards this goal, scientific attention is shifting towards variants and SNPs that affect factors of the disease such as susceptibility and severity. This genomic grammar, tightly related to the dark part of our genome, can be explored through the use of modern methods such as natural language processing. We present a semantic analysis of SARS-CoV-2 related publications, which yielded a repertoire of SNPs, genes and disease ontologies. Population data from the 100Genomes Project were subsequently integrated into the pipeline. Data mining approaches of this scale have the potential to elucidate the complex interaction between COVID-19 pathogenesis and host genetic variation; the resulting knowledge can facilitate the management of high-risk groups and aid the efforts towards precision medicine.

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.

Resistance to stress is a feature of cancer cells. Cellular stress includes oxidative, metabolic and genotoxic stress conditions, which under normal conditions lead to cell death. However, in contrast to normal cells, cancer cells overcome the checkpoints that normally restrict growth, and are able to resist cellular stress and subsequent cell death through a variety of mechanisms, which include several non‑coding RNAs (ncRNAs). Within this context, long ncRNAs (lncRNAs) and microRNAs (miRNAs/miRs) are the main categories of ncRNAs that have been shown in the literature to function as regulators of stress resistance pathways in cancer. miRNAs play a key role in the majority of biological pathways, as they regulate the expression of hundreds of target genes, including genes involved in stress response and cell death, oncogenes, or tumor suppressor genes, by inhibiting protein translation or promoting the degradation of mRNAs. Respectively, lncRNAs are epigenetic regulators, which are also involved in cancer progression, stress response and metabolic pathways by promoting or inhibiting the transcription, splicing, translation and modulation of protein function. Thus, the present review summarizes recent knowledge related to the role of these molecules in the cancer response to stress, highlighting the ability of these non‑coding molecules to be effective drug targets and biomarkers in cancer treatment.

RA is an inflammatory joint disease of an autoimmune nature, with a complex mode of inheritance characterized by chronic and destructive inflammation in the peripheral joints of the hands and feet and irreversible disability. This disorder occurs more often in women, and reproductive and hormonal factors have been shown to be related to increased risk. Endometriosis is a chronic, complex, oestrogen-dependent and progressive gynaecological disorder characterized by the growth of endometrial tissue outside the uterine cavity. Thus far, substantial abnormalities in the immune system of women with endometriosis have been demonstrated. Epidemiological data have suggested a link between endometriosis and the risk of incident RA. The similarities between molecular and cellular pathways of endometriosis and RA may implicate a partially shared genetic background. In this review we present an overview of the shared genetic factors known thus far that are associated with the development of both disorders.

Genome wide association studies (GWAS) have identified autoimmune disease-associated loci, a number of which are involved in numerous disease-associated pathways. However, much of the underlying genetic and pathophysiological mechanisms remain to be elucidated. Systemic lupus erythematosus (SLE) is a chronic, highly heterogeneous auto-immune disease, characterized by differences in autoantibody profile, serum cytokines and a multi-system involvement. This study presents the Epione application, an integrated bioinformatics web-toolkit, designed to assist medical experts and researchers in more accurately diagnosing SLE. The application aims to identify the most credible gene variants and single nucleotide polymorphisms (SNPs) associated with SLE susceptibility, by using patient's genomic data to aid the medical expert in SLE diagnosis. The application contains useful knowledge of >70,000 SLE-related publications that have been analyzed, using data mining and semantic techniques, towards extracting the SLE-related genes and the corresponding SNPs. Probable genes associated with the patient's genomic profile are visualized with several graphs, including chromosome ideograms, statistic bars and regulatory networks through data mining studies with relative publications, to obtain a representative number of the most credible candidate genes and biological pathways associated with the SLE. Furthermore, an evaluation study was performed on a patient diagnosed with SLE and is presented herein. Epione has also been expanded in family-related candidate patients to evaluate its predictive power. All the recognized gene variants that were previously considered to be associated with SLE were accurately identified in the output profile of the patient, and by comparing the results, novel findings have emerged. The Epione application may assist and facilitate in early stage diagnosis by using the patients' genomic profile to compare against the list of the most predictable candidate gene variants related to SLE. Its diagnosis-oriented output presents the user with a structured set of results on variant association, position in genome and links to specific bibliography and gene network associations. The overall aim of the present study was to provide a reliable tool for the most effective study of SLE. This novel and accessible webserver tool of SLE is available at http://geneticslab.aua.gr/epione/.

Τα φάρμακα αποτελούν πολύτιμα αγαθά για την πρόληψη και αντιμετώπιση των ασθενειών από τις οποίες απειλείται διαρκώς ο άνθρωπος στη διάρκεια της ζωής του. Από αρχαιοτάτων χρόνων ο αγώνας για επιβίωση εκτός από την εύρεση τροφής περιελάβανε και την προστασία από τις κακουχίες και την αρρώστια. Στην αρχαία Ελλάδα ο Ιπποκράτης δίδασκε «Η τροφή πρέπει είναι το φάρμακό σου και το φάρμακο να είναι η τροφή σου». Παρά τις μεγάλες προσπάθειες που γίνονται τα τελευταία χρόνια για την αντιμετώπιση διαφόρων ασθενειών και παθήσεων, παρατηρείται μια σημαντική μείωση στις νέες φαρμακευτικές ενώσεις. Το πρόβλημα αυτό εστιάζεται στην έλλειψη γνώσης σε νέους φαρμακολογικούς στόχους. Οι φαρμακολογικοί στόχοι εντοπίζονται σε πρωτεΐνες του οργανισμού, κυρίως ένζυμα, υποδοχείς και τα αντισώματα. Σε πολλές άλλες περιπτώσεις όμως φαρμακολογικοί στόχοι είναι δυνατό να εξαχθούν από πρωτεΐνες από διάφορους ιούς ή ακόμη και παράσιτα που εισβάλουν στον ανθρώπινο οργανισμό. Οι πρωτεΐνες γενικά φέρουν καθορισμένο ενεργό κέντρο για τη σύνδεση των ενδογενών προσδεμάτων ή συγκεκριμένα σημεία αλληλεπίδρασης με άλλες πρωτεΐνες. Η στόχευση των φαρμακομορίων αφορά πρωτίστως στο ενεργό κέντρο των μακρομορίων, αν και είναι δυνατόν η σύνδεση να γίνεται και σε άλλες θέσεις. Η ανίχνευση νέων φαρμακολογικών μορίων συνδέεται άμεσα με την μελέτη της τρισδιάστατης δομής των μοριακών στόχων. Σήμερα ωστόσο, η εξέλιξη της τεχνολογίας και της βιοπληροφορικής, επιτρέπει τον προσδιορισμό της τρισδιάστατης δομής και άλλων πρωτεϊνών καθώς και συμπλόκων πρωτεϊνών-προσδεμάτων / πρωτεϊνών- πρωτεϊνών προκειμένου να εντοπιστούν νέοι φαρμακολογικοί στόχοι. Στην παρούσα διδακτορική διατριβή αναπτύχθηκε συγκεκριμένη διαδικασία για την ανίχνευση νέων φαρμακολογικών στόχων μέσω υπολογιστικής μελέτης και ανάλυσης της τρισδιάστατης δομής των πρωτεϊνών. Οι αμινοξικές ακολουθίες των πρωτεϊνών είναι χαρακτηριστικό της γενετικής ποικιλομορφίας τους, οι τρισδιάστατες δομές τους προσδιορίζουν την λειτουργία τους και την μοριακή αρχιτεκτονική τους, και τα τρισδιάστατα πρωτεϊνικά σύμπλοκα παρουσιάζουν τον τύπο αλληλεπιδράσεων που σχηματίζουν. Οι τρείς αυτοί διαφορετικοί τύποι πληροφορίας είναι σημαντικοί για την καλύτερη κατανόηση των δομικών και των λειτουργικών χαρακτηριστικών των πρωτεϊνών, καθώς και για την ανίχνευση νέων φαρμακολογικών στόχων. Πολλές προσπάθειες έχουν γίνει μέχρι σήμερα προκειμένου να συσχετιστεί η πληροφορία από τις αμινοξικές ακολουθίες, τρισδιάστατες δομές και τρισδιάστατα πρωτεϊνικά σύμπλοκα προκειμένου να κατανοηθούν οι άγνωστοι μηχανισμοί που κατευθύνουν τη λειτουργία τους. Στην παρούσα διδακτορική διατριβή εξετάζονται τέσσερα διαφορετικά σύνολα βιολογικών δεδομένων από διαφορετικής κατηγορίας πρωτεΐνες προκειμένου να σχεδιάσουμε σωστά μια ολοκληρωμένη διαδικασία ανίχνευσης νέων φαρμακολογικών στόχων. Τα τέσσερα πρωτεϊνικά σύνολα που εξετάστηκαν στην παρούσα διδακτορική διατριβή αφορούν τα αντισώματα, τους πυρηνικούς υποδοχείς, τα ιικά ένζυμα της οικογένειας ιών Flaviviridae και τέλος την RNA πολυμεράση ΙΙ του παρασίτου που προκαλεί την Ελονοσία.Τα παραγόμενα αποτελέσματα στην παρούσα διδακτορική διατριβή είναι πρωτότυπα και σημαντικά. Στα πλαίσια ανάπτυξης της περιγραφόμενης υπολογιστικής διαδικασίας ανίχνευσης νέων φαρμακολογικών στόχων μέσω προτύπων σήματος έχουμε αναπτύξει τέσσερις νέες μεθοδολογίες προκειμένου να μελετήσουμε και να αναλύσουμε την βιολογική πληροφορία που φέρουν οι αμινοξικές ακολουθίες και πρωτεϊνικές δομές. Η πρώτη διαδικασία περιγράφει ένα νέο τρόπο εύρεσης υποψήφιων φαρμακολογικών στόχων μέσω συντηρημένων μοτίβων / προτύπων σήματος σε αμινοξικές ακολουθίες. Η δεύτερη διαδικασία περιγράφει ένα νέο τρόπο ομαδοποίησης και συσχέτισης των πρωτεϊνών με αντιπροσωπευτικά πρότυπα φυσικοχημικών ιδιοτήτων και την χρήση μη εποπτευόμενων τεχνικών μάθησης, συμπεριλαμβανομένου του αλγόριθμου k-means και των νευρωνικών δικτύων για την επέκταση των προτύπων σήματος. Αντίστοιχα, η τρίτη διαδικασία αναπτύχθηκε για την ανάλυση των τρισδιάστατων δομών και περιγράφει ένα νέο τρόπο συσχέτισης και ομαδοποίησης των τρισδιάστατων δομών με την χρήση υβριδικών πινάκων απόστασης και ιεραρχικών τεχνικών ομαδοποίησης όπως η τεχνική neighbor-joining και UPGMA. Τελευταίο αλλά εξίσου σημαντικό, στην μελέτη και ανάλυση των τρισδιάστατων δομών των πρωτεϊνικών συμπλόκων προτείνεται μια νέα διαδικασία ψηφιακής κωδικοποίησης του τρόπου αλληλεπίδρασης των πρωτεϊνών, σε μοτίβα αλληλεπίδρασης / πρότυπα σήματος, και ομαδοποίηση τους σε ομάδες από μοτίβα αλληλεπίδρασης για τα αντισώματα. Μία νέα εφαρμογή έχει αναπτυχθεί στα πλαίσια της περιγραφόμενης διαδικασίας, η εφαρμογή «antisoma» και αφορά το πρωτεϊνικό σύνολο δεδομένων για τα αντισώματα. Αποτελεί μια ολοκληρωμένη πλατφόρμα μεταβλητών περιοχών αντισωμάτων, η οποία παρέχει σημαντική πληροφορία που σχετίζεται με τις πρωτεϊνικές τους ακολουθίες. Ο χρήστης έχει την δυνατότητα να εξάγει διαφορετικά σύνολα δεδομένων από αμινοξικές ακολουθίες, καθώς και να εκτελέσει διάφορα φίλτρα με βάση των σύστημα αρίθμησης των αντισωμάτων, και να υπολογίσει περισσότερες από 300 φυσικοχημικές ιδιότητες για τις τμηματικές περιοχές που περιλαμβάνονται στις μεταβλητές περιοχές ως πρότυπα σήματος. Τα παραγόμενα συμπεράσματα από τα διαφορετικά στάδια της υπολογιστικής ανάλυσης είναι πολλά και συνοψίζονται στο τέλος του κάθε κεφαλαίου. Σημαντικό είναι να αναφερθεί ότι με βάση τις μεθοδολογίες που αναπτύχθηκαν καταφέραμε να ομαδοποιήσουμε την βιολογική πληροφορία των συνόλων δεδομένων από πρωτεΐνες σε διακριτές ομάδες ανάλογα με το στάδιο επεξεργασίας που τις εφαρμόζαμε κάθε φορά. Συνολικά, η προτεινόμενη υπολογιστική μελέτη έχει εντοπίσει ένα σημαντικό αριθμό νέων φαρμακολογικών στόχων με βάση τα βιολογικά σύνολα πρωτεϊνών εφαρμογής.

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/.

Myasthenia Gravis (MG) is an autoimmune disease that affects neuromuscular junctions and is characterized by muscle weakness as a result of autoantibodies against certain proteins. As a heterogeneous disorder, MG presents with different types, including neonatal, ocular and generalized in both juveniles and adults. Different types of antibodies serve a role in how MG presents. The main biological characteristic of MG is the production of antibodies against the muscular acetylcholine receptor; however, other types of antibody have been associated with the disorder. The role of the thymus gland has been established and thymectomy is a possible treatment of the disease, along with traditional medication such as pyridostigmine bromide (Mestinon) and immunosuppresants. In recent years, steps have been made towards developing more sensitive diagnostic methods. Additionally, novel treatments have demonstrated promising results. Developing new assays may lead to an increased understanding of the disease and to unravelling the genetic pathway that leads to the development of neuromuscular diseases.

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.

Antibodies are proteins that are the first line of defense in the adaptive immune response of vertebrates. Thereby, they are involved in a multitude of biochemical mechanisms and clinical manifestations with significant medical interest, such as autoimmunity, the regulation of infection, and cancer. An emerging field in antibody science that is of huge medicinal interest is the development of novel antibody-interacting drugs. Such entities are the antibody-drug conjugates (ADCs), which are a new type of targeted therapy, which consist of an antibody linked to a payload drug. Overall, the underlying principle of ADCs is the discerning delivery of a drug to a target, hoping to increase the potency of the original drug. Drugena suite is a pioneering platform that employs state-of-the-art computational biology methods in the fight against neurodegenerative diseases using ADCs. Drugena encompasses an up-to-date structural database of specialized antibodies for neurological disorders and the NCI database with over 96 million entities for the in silico development of ADCs. The pipeline of the Drugena suite has been divided into several steps and modules that are closely related with a synergistic fashion under a user-friendly graphical user interface.

Neurodegenerative diseases lead to the death of nerve cells in the brain or the spinal cord. A wide range of diseases are included within the group of neurodegenerative disorders, with the most common ones being dementia, Alzheimer's, and Parkinson's diseases. Millions of older people are suffering from such pathologies. The global increase of life expectancy unavoidably leads to a consequent increase in the number of people who will be at some degree affected by neurodegenerative-related diseases. At this moment, there is no effective therapy or treatment that can reverse the loss of neurons. A growing number of studies highlight the value of the consumption of medical foods, and in particular olive oil, as one of the most important components of the Mediterranean diet. A diet based on extra virgin olive oil seems to contribute toward the lowering of risk of age-related pathologies due to high phenol concentration. The link of a polyphenol found in extra virgin olive oil, namely, tyrosol, with the protein tyrosinase, associated to Parkinson's disease is underlined as a paradigm of affiliation between polyphenols and neurodegenerative disorders.

Herein, we deploy an in silico pipeline of structural bioinformatics, thermodynamics, and molecular dynamics to investigate the role of cortisol in circadian rhythms, biorhythms, stress response, and even sleep disorders. Our study shows that high concentrations of cortisol intercalate in the minor groove of DNA. This phenomenon widens the adjacent major grooves and provides the Clock/Bmal1 complex with more space to dock and interact with DNA. Then, the strong charges of cortisol pull the alpha helices of the Clock/Bmal1 complex and bend it inward, thus establishing stronger interactions and prolonged signaling. Our results indicate that elevated cortisol levels play an important role in stress, inflammation, and sleep disorders as a result of prolonged and stronger dsDNA - Clock/Bmal1 interactions.

Pesticides are necessary to fight agricultural pests, yet they are often nonspecific, and their widespread use is a hazard to the environment and human health. The genomic era allows for new approaches to specifically target agricultural pests, based on analysis of their genome and their microbiome. We present such an approach, to combat Bactrocera oleae, a widespread pest whose impact is devastating on olive production. To date, there is no specific pesticide to control it. Herein, we propose a novel strategy to manage this pest via identifying novel pharmacological targets on the genome of its obligate endosymbiotic bacterium Candidatus Erwinia dacicola. Three genes were selected as pharmacological targets. The 3D models of the Helicase, Polymerase, and Protease-C gene products were designed and subsequently optimized by means of molecular dynamics simulations. Successively, a series of structure-based pharmacophore models were elucidated in an effort to pave the way for the efficient high-throughput virtual screening of libraries of low molecular weight compounds and thus the discovery of novel modulating agents. Our methodology provides the means to design, test, and identify highly specific pest control substances that minimize the impact of toxic chemicals on health, economy, and the environment.

BACKGROUND/AIM Pesticides have little, if any specificity, to the pathogen they target in most cases. Wide spectrum toxic chemicals are being used to remove pestcides and salvage crops and economies linked to agriculture. The burden on the environment, public health and economy is huge. Traditional pestcide control is based on administering heavy loads of highly toxic compounds and elements that essentially strip all life from the field. Those chemicals are a leading cause of increased cancer related deaths in countryside. Herein, the Trojan horse of endosymbiosis was used, in an effort to control pests using high specificity compounds in reduced quantities. MATERIALS AND METHODS Our pipeline has been applied on the case of Otiorhynchus singularis, which is a very widespread pest, whose impact is devastating on a repertoire of crops. To date, there is no specific pesticide nor agent to control it. The deployed strategy involves the inhibition of the key DSB-A enzyme of its endosymbiotic Wolbachia pipientis bacterial strain. RESULTS Our methodology, provides the means to design, test and identify highly specific pestcide control substances that minimize the impact of toxic chemicals on health, economy and the environment. CONCLUSION All in all, in this study a radical computer-based pipeline is proposed that could be adopted under many other similar scenarios and pave the way for precision agriculture via optimized pest control.

With the emerging Zika virus (ZIKV) epidemic, serologic diagnosis relies on a labor-intensive IgM antibody capture enzyme-linked immunosorbent assay (MAC-ELISA) and confirmation by a plaque reduction neutralization test (PRNT). To streamline serologic testing, several commercial assays have been developed. Our aim was to compare the commercial Euroimmun anti-ZIKV IgM and IgG assays to the reference MAC-ELISA and PRNT currently in use. Serum specimens submitted to Public Health Ontario Laboratory, Canada, were tested for IgM and IgG using the Euroimmun assays and the results were compared with those from MAC-ELISA. The PRNT was performed on positive or equivocal specimens using either MAC-ELISA or Euroimmun assays, MAC-ELISA-inconclusive specimens, and a convenience sample of specimens negative by both assays (cohort 1). Another set of specimens selected on the basis of PRNT results was subsequently tested by the Euroimmun assays (cohort 2). MAC-ELISA was positive, equivocal, negative, and inconclusive in 57/223, 15/223, 147/223, and 4/223 specimens, respectively. Among the 76 specimens that were MAC-ELISA positive, equivocal, or inconclusive, 30 (39.5%) were Euroimmun IgM and/or IgG positive or equivocal. Among the 147 MAC-ELISA-negative specimens, 136 (92.5%) were Euroimmun IgM and IgG negative. The sensitivity of the combined Euroimmun IgM/IgG against the PRNT was 83% (cohort 1) and 92% (cohort 2), whereas the specificity was 81% (cohort 1) and 65% (cohort 2). The combined Euroimmun IgM/IgG showed good specificity (92.5%) but suboptimal sensitivity (39.5%) compared with that of the MAC-ELISA. However, the sensitivity of the combined Euroimmun IgM/IgG against the PRNT was significantly higher (83 to 92%). More studies are needed before commercial assays are implemented for routine ZIKV serologic diagnosis.

Antibodies are proteins that are the first line of defense in the adaptive immune response of vertebrates. Thereby, they are involved in a multitude of biochemical mechanisms and clinical manifestations with significant medical interest, such as autoimmunity, the regulation of infection, and cancer. An emerging field in antibody science that is of huge medicinal interest is the development of novel antibody-interacting drugs. Such entities are the antibody-drug conjugates (ADCs), which are a new type of targeted therapy, which consist of an antibody linked to a payload drug. Overall, the underlying principle of ADCs is the discerning delivery of a drug to a target, hoping to increase the potency of the original drug. Drugena suite is a pioneering platform that employs state-of-the-art computational biology methods in the fight against neurodegenerative diseases using ADCs. Drugena encompasses an up-to-date structural database of specialized antibodies for neurological disorders and the NCI database with over 96 million entities for the in silico development of ADCs. The pipeline of the Drugena suite has been divided into several steps and modules that are closely related with a synergistic fashion under a user-friendly graphical user interface.

Herein, we deploy an in silico pipeline of structural bioinformatics, thermodynamics, and molecular dynamics to investigate the role of cortisol in circadian rhythms, biorhythms, stress response, and even sleep disorders. Our study shows that high concentrations of cortisol intercalate in the minor groove of DNA. This phenomenon widens the adjacent major grooves and provides the Clock/Bmal1 complex with more space to dock and interact with DNA. Then, the strong charges of cortisol pull the alpha helices of the Clock/Bmal1 complex and bend it inward, thus establishing stronger interactions and prolonged signaling. Our results indicate that elevated cortisol levels play an important role in stress, inflammation, and sleep disorders as a result of prolonged and stronger dsDNA – Clock/Bmal1 interactions.

Pesticides are necessary to fight agricultural pests, yet they are often nonspecific, and their widespread use is a hazard to the environment and human health. The genomic era allows for new approaches to specifically target agricultural pests, based on analysis of their genome and their microbiome. We present such an approach, to combat Bactrocera oleae, a widespread pest whose impact is devastating on olive production. To date, there is no specific pesticide to control it. Herein, we propose a novel strategy to manage this pest via identifying novel pharmacological targets on the genome of its obligate endosymbiotic bacterium Candidatus Erwinia dacicola. Three genes were selected as pharmacological targets. The 3D models of the Helicase, Polymerase, and Protease-C gene products were designed and subsequently optimized by means of molecular dynamics simulations. Successively, a series of structure-based pharmacophore models were elucidated in an effort to pave the way for the efficient high-throughput virtual screening of libraries of low molecular weight compounds and thus the discovery of novel modulating agents. Our methodology provides the means to design, test, and identify highly specific pest control substances that minimize the impact of toxic chemicals on health, economy, and the environment.

Neurodegenerative diseases lead to the death of nerve cells in the brain or the spinal cord. A wide range of diseases are included within the group of neurodegenerative disorders, with the most common ones being dementia, Alzheimer’s, and Parkinson’s diseases. Millions of older people are suffering from such pathologies. The global increase of life expectancy unavoidably leads to a consequent increase in the number of people who will be at some degree affected by neurodegenerative-related diseases. At this moment, there is no effective therapy or treatment that can reverse the loss of neurons. A growing number of studies highlight the value of the consumption of medical foods, and in particular olive oil, as one of the most important components of the Mediterranean diet. A diet based on extra virgin olive oil seems to contribute toward the lowering of risk of age-related pathologies due to high phenol concentration. The link of a polyphenol found in extra virgin olive oil, namely, tyrosol, with the protein tyrosinase, associated to Parkinson’s disease is underlined as a paradigm of affiliation between polyphenols and neurodegenerative disorders.

Antibody V domain clustering is of paramount importance to a repertoire of immunology-related areas. Although several approaches have been proposed for antibody clustering, still no consensus has been reached. Numerous attempts use information from genes, protein sequences, 3D structures, and 3D surfaces in an effort to elucidate unknown action mechanisms directly related to their function and to either link them directly to diseases or drive the discovery of new medicines, such as antibody drug conjugates (ADC). Herein, we describe a new V domain antibody clustering method based on the comparison of the interaction sites between each antibody and its antigen. A more specific clustering analysis of the antibody's V domain was provided using deep learning and data mining techniques. The multidimensional information was extracted from the structural resolved antibodies when they were captured to interact with other proteins. The available 3D structures of protein antigen-antibody (Ag-Ab) interfaces contain information about how antibody V domains recognize antigens as well as about which amino acids are involved in the recognition. As such, the antibody surface holds information about antigens' folding that reside with the Ab-Ag interface residues and how they interact. In order to gain insight into the nature of such interactions, we propose a new simple philosophy to transform the conserved framework (fragment regions, complementarity-determining regions) of antibody V domain in a binary form using structural features of antibody-antigen interactions, toward identifying new antibody signatures in V domain binding activity. Finally, an advanced three-level hybrid classification scheme has been set for clustering antibodies in subgroups, which can combine the information from the protein sequences, the three-dimensional structures, and specific "key patterns" of recognized interactions. The clusters provide multilevel information about antibodies and antibody-antigen complexes.

Background:Genome wide association studies (GWAS) have successfully identified novel autoimmune disease-associated loci, with many of them shared by multiple disease-associated pathways but much of the genetics and pathophysiological mechanisms remain still obscure [1-3]. SLE is a chronic, highly heterogeneous autoimmune disease, characterized by differences in autoantibody profile, serum cytokines, and a multi-system involvement [4]. Epione Application is an integrated bioinformatics web-tool designed to assist medical experts and researchers in the process of diagnosing SLE [5].Objectives:To identify the most credible gene variants and single nucleotide polymorphisms (SNPs), causing SLE using the genomic data provided for the patient and aid the medical expert in SLE diagnosis [5].Methods:In the present study, we have analyzed more than 70.000 SLE-related publications using data mining and semantic techniques towards extracting the SLE -related genes and SNPs [6]. The extracted knowledge has been filtered, evaluated, annotated, classified, and stored in the Epione Application Database (EAD) (Figure 1). Moreover, an updated gene regulatory network with the genes implements in SLE has been estimated [7]. This was followed by the design and development of the Epione application, in which the generated datasets and results were included. The application has been tested and presented here with WES data from several related patients with SLE [8].Results:SLE-related SNPs and variants identified in genome-wide association studies (GWAS), whole-genome (WGS), whole-exome (WES), or targeted sequencing information are classified, annotated, and analyzed in an integrated patient profile with clinical significance information. Probable genes associated with the patient’s genomic profile are visualized with several graphs, including chromosome ideograms, statistic bars, and regulatory networks through data mining studies with relative publications, to obtain a representative number of the most credible candidate genes and biological pathways associated with the SLE. An evaluation study was performed on 7 patients from a three-generation family with SLE [9]. All the recognized gene variants that were previously considered SLE-associated were properly identified in the output profile per patient, and by comparing the results, new findings have emerged.Conclusion:The Epione application was designed to assist medical doctor diagnosis from the early stages by using the patients’ genomic data [5, 8, 10]. Its diagnosis-oriented output presents the patient profile through which the user is provided with a structured set of results in various categories, which are generated based on the list of the most predictable candidate gene variants related to SLE. This novel and accessible webserver tool of SLE to assist medical experts in the clinical genomics and precision medicine procedure is available at http://143.233.188.162/epione/.References:[1]Molineros JE et al. (2017). Hum Mol Genet 26:1205-1216.[2]Sciascia S et al. (2018). F1000 Res, 2018:1-17.[3]Gonzalez-Serna D et al. (2020). Sci Rep 10:1862.[4]Harley JB et al. (2006). Springer Semin Immun 28:119–130.[5]Goulielmos GN et al. (2018). Gene 668:59-72.[6]Zhao Y et al. (2020). Front Genet 11:400.[7]Song YL and Chen S (2009). Interdiscip Sci 1:179-186.[8]Koile D et al. (2018). BMC Bioinformatics 19:25.[9]Albertsen HM et al. (2019). Mol Med Rep 19:1716-1720.[10]Ebrahimiyan H et al. (2018). Meta Gene 16:241-247.Figure 1.The Epione application database (EAD) for SLE.Disclosure of Interests:None declared

Understanding the genetic and molecular basis of the detoxifying mechanism in Chlamydomonas reinhardtii is an important goal towards the development of bioremediation tools for contaminated environments. Glutathione transferases (GSTs, EC 2.5.1.18) are phase II metabolic enzymes that play important role in the detoxification of xenobiotic compounds. GSTs have been characterized extensively in land plants and animals but no evidence has yet been reported for their presence in C. reinhardtii. A genome survey of C. reinhardtii revealed the presence of fifteen GST-like isoenzymes (CrGSTs). Comparison by multiple sequence alignment generated a dendrogram, revealing the phylogenetic relationships of CrGSTs with other well-characterized GST classes. Notably, we identified sequences that are most closely related to the sigma class enzymes which so far have only been found in animals. Two sigma class related isoenzymes (CrGST10 and CrGST7) were cloned, expressed in E. coli and their substrate specificity and kinetic properties were determined towards a range of different xenobiotic substrates. The structural and kinetic features of the enzymes were studied by molecular modelling and site-directed mutagenesis. The catalytic role of active-site residue Tyr7 and the roles of Trp99 in determining substrate specificity and thermostability were investigated. Analysis of GSTome in green algae provides an opportunity to shine light on the roles of GSTs in cellular detoxification mechanism as well as to develop new biotechnological and environmental applications.

Background/aim The aim of the present study was to examine the relation between understanding of emotions and cardiovascular related diseases, namely coronary heart disease, diabetes mellitus and obesity. The uniqueness of this study lies in the fact that it examined the relationship between the cardiovascular related diseases named above and the understanding of emotions in the context of Emotional Intelligence (EI). Patients and methods The study was conducted in 300 participants during a 3 year period. All participants completed a self-report questionnaire, assessing various aspects of EI, such as self-emotion appraisal, other emotion appraisal, emotion regulation and use of emotions. As hypothesized, coronary heart disease is a prognostic factor of regulation of emotions. Results The present study is an attempt to examine the relation between emotional understanding and cardiovascular related diseases, namely coronary heart disease, diabetes mellitus and obesity. Establishing which diseases are independent risk factors for the understanding of emotions, could have a significant impact on emotional health, through the treatment of these cardiovascular related diseases. Emotions were studied within the theoretical context of Emotional Intelligence (EI), which affects people's physical and mental health. Conclusion The results of this study emphasize on the relationship of cardiovascular related diseases and psychological characteristics, such as anxiety and anger, being aspects of EI. Additionally, this work fills a gap in the relevant Greek literature, as a first attempt to examine the correlation of EI with cardiovascular related diseases.

During the last decades, there is a vast data explosion in bioinformatics. Big data centres are trying to face this data crisis, reaching high storage capacity levels. Although several scientific giants examine how to handle the enormous pile of information in their cupboards, the problem remains unsolved. On a daily basis, there is a massive quantity of permanent loss of extensive information due to infrastructure and storage space problems. The motivation for sequencing has fallen behind. Sometimes, the time that is spent to solve storage space problems is longer than the one dedicated to collect and analyse data. To bring sequencing to the foreground, scientists have to slide over such obstacles and find alternative ways to approach the issue of data volume. Scientific community experiences the data crisis era, where, out of the box solutions may ease the typical research workflow, until technological development meets the needs of Bioinformatics.

Background/Aim: Pesticides have little, if any specificity, to the pathogen they target in most cases. Wide spectrum toxic chemicals are being used to remove pestcides and salvage crops and economies linked to agriculture. The burden on the environment, public health and economy is huge. Traditional pestcide control is based on administering heavy loads of highly toxic compounds and elements that essentially strip all life from the field. Those chemicals are a leading cause of increased cancer related deaths in countryside. Herein, the Trojan horse of endosymbiosis was used, in an effort to control pests using high specificity compounds in reduced quantities. Materials and Methods: Our pipeline has been applied on the case of Otiorhynchus singularis, which is a very widespread pest, whose impact is devastating on a repertoire of crops. To date, there is no specific pesticide nor agent to control it. The deployed strategy involves the inhibition of the key DSB-A enzyme of its endosymbiotic Wolbachia pipientis bacterial strain. Results: Our methodology, provides the means to design, test and identify highly specific pestcide control substances that minimize the impact of toxic chemicals on health, economy and the environment. Conclusion: All in all, in this study a radical computer-based pipeline is proposed that could be adopted under many other similar scenarios and pave the way for precision agriculture via optimized pest control.

The exponential growth of available biological data in recent years coupled with their increasing complexity has made their analysis a computationally challenging process. Traditional central processing unist (CPUs) are reaching their limit in processing power and are not designed primarily for multithreaded applications. Graphics processing units (GPUs) on the other hand are affordable, scalable computer powerhouses that, thanks to the ever increasing demand for higher quality graphics, have yet to reach their limit. Typically high-end CPUs have 8-16 cores, whereas GPUs can have more than 2,500 cores. GPUs are also, by design, highly parallel, multicore and multithreaded, able of handling thousands of threads doing the same calculation on different subsets of a large data set. This ability is what makes them perfectly suited for biological analysis tasks. Lately this potential has been realized by many bioinformatics researches and a huge variety of tools and algorithms have been ported to GPUs, or designed from the ground up to maximize the usage of available cores. Here, we present a comprehensive review of available bioinformatics tools ranging from sequence and image analysis to protein structure prediction and systems biology that use NVIDIA Compute Unified Device Architecture (CUDA) general-purpose computing on graphics processing units (GPGPU) programming language.

The Flaviviridae family of viruses infects vertebrates and is primarily spread through arthropod vectors. The Greek Goat Encephalitis (GGE) flavivirus belongs to the Flaviviridae family and specifically to the genus Flavivirus. GGE virus, which is endemic in Greece, is the causative agent of tick-borne encephalitis (TBE), an infection of the central nervous system that can be transmitted from animals to humans by ticks. Although, there are very limited data regarding the GGE virus and its epidemiology in Greece, there have been few reported cases of GGE viral infection of goats in northern Greece. However, despite the severity of Flaviviridae causing diseases (e.g. Hepatitis C, Dengue fever, Yellow fever, Classical swine fever, Japanese encephalitis), currently there is not any available anti-flaviviridae therapy. Thus, there is a need for the development of effective anti-GGE viral pharmaceutical strategies. It has been shown that RNA helicases, which are involved in duplex unwinding during viral RNA replication, represent promising antiviral targets. Therefore, we suggest that inhibition of the GGE viral helicase would be an effective approach of interrupting the life cycle of the GGE virus. Our proposed research will be directed towards the computer-aided development of a series of drug-like low molecular weight compounds capable of inhibiting the helicase enzyme of GGE virus. Results derived from a repertoire of multi-disciplinary bioinformatics and statistical methods would enhance our understanding of the mechanism of action of the GGE viral helicase enzyme. Our ultimate goal is to design a series of novel anti-helicase compounds as drug candidates against the endemic GGE virus while the inhibitory activity of our novel compounds will be evaluated biologically.