Differentially Expressed MicroRNAs Act As Inhibitors of BDNF in Prefrontal Cortex - Implications for Schizophrenia: A Dissertation

During my thesis work I studied the expression and potential function of brain expressed microRNAs (miRNAs) in human prefrontal cortex (PFC). Initially, I used combinatorial computational analysis and microarray data to identify miRNAs that are predicted with high probability to target the human Brain Derived Neurotrophic Factor (BDNF) 3’ Untranslated Region (3’UTR) and are expressed in moderate to high levels in adult human prefrontal cortex. A subset of 10 miRNAs segregating into 5 different miRNA families (miR-30a-d, miR-103/107, miR-16/195, miR-191 and miR-495) met the above criteria. I then designed a protocol to detect these miRNAs with Locked Nucleic Acid (LNA) in situ hybridization in human prefrontal cortex and determine their layer and cellular expression patterns. LNA in situ revealed differential lamina and cellular enrichment of BDNF-related miRNAs. As an example, miR-30a-5p was found to be enriched in large pyramidal neurons of layer 3, which was verified using laser capture microdissection of layer 3 pyramidal neurons and quantitative Real Time Polymerase Chain Reaction (qRT-PCR) following dissection of upper and deeper layers of human PFC. Parallel to this, I used miRNA qRT-PCR to determine the developmental expression of miRNAs using postmortem PFC tissues ranging from embryonic age to old adulthood and compared miRNA to BDNF protein levels. My results revealed a robust inverse correlation between BDNF-related miRNAs and BDNF protein during late maturation and aging of human prefrontal cortex. In vitro luciferase assays and/or lentivirus mediated neuronal miRNA overexpression experiments validated that at least two miRNAs, miR-30a-5p and miR-195, target human BDNF 3’UTR and mediate its translational repression. In the second part of my thesis work I measured levels of miR-30a and miR-195 in the prefrontal cortex of patients with schizophrenia and compared them with levels of BDNF protein and BDNF-related GABAergic mRNAs. According to my results differences in miR-195 levels in a subset of subjects diagnosed with schizophrenia were found to be associated with disease related changes in BDNF protein levels and deficits in BDNF dependent GABAergic gene expression. In the last part of my work I focused on miR-30b, another member of the miR-30 family, which I found to be reduced in the prefrontal cortex of female but not male subjects with schizophrenia. More importantly, disease related changes in miR-30b levels were strongly associated with the age of onset of the disease. Additional experiments in mouse cortex and hippocampus revealed a gender dimorphic expression pattern of this miRNA with higher expression in female brain. Collectively, my results suggest that miRNAs could participate in novel molecular pathways that play an important role during cortical development and maturation and are potentially linked to the pathophysiology of neuropsychiatric disease

The Emerging Role of microRNAs in Schizophrenia and Autism Spectrum Disorders

MicroRNAs (miRNAs) are small non-coding RNAs conserved throughout evolution whose perceived importance for brain development and maturation is increasingly being understood. Although a plethora of new discoveries have provided novel insights into miRNA-mediated molecular mechanisms that influence brain plasticity, their relevance for neuropsychiatric diseases with known deficits in synaptic plasticity, such as schizophrenia and autism, has not been adequately explored. In this review we discuss the intersection between current and old knowledge on the role of miRNAs in brain plasticity and function with a focus in the potential involvement of brain expressed miRNAs in the pathophysiology of neuropsychiatric disorders

Machine learning approaches for predicting health risk of cyanobacterial blooms in Northern European Lakes

Cyanobacterial blooms are considered a major threat to global water security with documented impacts on lake ecosystems and public health. Given that cyanobacteria possess highly adaptive traits that favor them to prevail under different and often complicated stressor regimes, predicting their abundance is challenging. A dataset from 822 Northern European lakes is used to determine which variables better explain the variation of cyanobacteria biomass (CBB) by means of stepwise multiple linear regression. Chlorophyll-a (Chl-a) and total nitrogen (TN) provided the best modelling structure for the entire dataset, while for subsets of shallow and deep lakes, Chl-a, mean depth, TN and TN/TP explained part of the variance in CBB. Path analysis was performed and corroborated these findings. Finally, CBB was translated to a categorical variable according to risk levels for human health associated with the use of lakes for recreational activities. Several machine learning methods, namely Decision Tree, K-Nearest Neighbors, Support-vector Machine and Random Forest, were applied showing a remarkable ability to predict the risk, while Random Forest parameters were tuned and optimized, achieving a 95.81% accuracy, exceeding the performance of all other machine learning methods tested. A confusion matrix analysis is performed for all machine learning methods, identifying the potential of each method to correctly predict CBB risk levels and assessing the extent of false alarms; random forest clearly outperforms the other methods with very promising results.publishedVersio

miR-132, an experience-dependent microRNA, is essential for visual cortex plasticity

Using quantitative analyses, we identified microRNAs (miRNAs) that were abundantly expressed in visual cortex and that responded to dark rearing and/or monocular deprivation. The most substantially altered miRNA, miR-132, was rapidly upregulated after eye opening and was delayed by dark rearing. In vivo inhibition of miR-132 in mice prevented ocular dominance plasticity in identified neurons following monocular deprivation and affected the maturation of dendritic spines, demonstrating its critical role in the plasticity of visual cortex circuits.National Eye Institute (Ruth L. Kirschstein Postdoctoral Fellowship 1F32EY020066-01)Simons Foundation (Postdoctoral Fellowship)National Institutes of Health (U.S.) (EY017098)National Institutes of Health (U.S.) (EY007023

Machine Learning Approaches for Predicting Health Risk of Cyanobacterial Blooms in Northern European Lakes

Cyanobacterial blooms are considered a major threat to global water security with documented impacts on lake ecosystems and public health. Given that cyanobacteria possess highly adaptive traits that favor them to prevail under different and often complicated stressor regimes, predicting their abundance is challenging. A dataset from 822 Northern European lakes is used to determine which variables better explain the variation of cyanobacteria biomass (CBB) by means of stepwise multiple linear regression. Chlorophyll-a (Chl-a) and total nitrogen (TN) provided the best modelling structure for the entire dataset, while for subsets of shallow and deep lakes, Chl-a, mean depth, TN and TN/TP explained part of the variance in CBB. Path analysis was performed and corroborated these findings. Finally, CBB was translated to a categorical variable according to risk levels for human health associated with the use of lakes for recreational activities. Several machine learning methods, namely Decision Tree, K-Nearest Neighbors, Support-vector Machine and Random Forest, were applied showing a remarkable ability to predict the risk, while Random Forest parameters were tuned and optimized, achieving a 95.81% accuracy, exceeding the performance of all other machine learning methods tested. A confusion matrix analysis is performed for all machine learning methods, identifying the potential of each method to correctly predict CBB risk levels and assessing the extent of false alarms; random forest clearly outperforms the other methods with very promising results

Tensile Performance of Headed Anchors in Steel Fiber Reinforced and Conventional Concrete in Uncracked and Cracked State

Steel fiber reinforced concrete (SFRC) is currently the material of choice for a broad range of structural components. Through the use of SFRC, the entire, or a large portion of, conventional rebar reinforcement can be replaced, in order to improve the load-bearing behavior but also the serviceability and durability characteristics of engineering structures. The use of fiber reinforcement therefore plays a vital role in acute current and future construction industry objectives, these being a simultaneous increase in the service life of structures and the reduction of their environmental impact, in addition to resilience to extreme loads and environmental actions. Next to the extended use of SFRC, modern construction relies heavily on structural connections and assembly technologies, typically by use of bolt-type cast-in and post-installed concrete anchors. This paper addresses the influence of fiber reinforcement on the structural performance of such anchors in SFRC and, particularly, the load bearing behavior of single headed anchors under axial static loads in uncracked and cracked concrete. Along with a presentation of background information on previous studies of SFRC with a focus on anchor concrete breakout failure, the experimental investigations are described, and their results are presented and elaborated on by consideration of various research parameters. A comparison with current design approaches is also provided. The conclusions are deemed useful for structural engineering research and practice

Περιβαλλοντική πληροφορική για τη μοντελοποίηση οικοσυστημάτων γλυκού νερού: δυναμική και επιπτώσεις του ευτροφισμού και βιώσιμη αγροτική χρήση νερού

This dissertation explores the potential of ecological modelling under the frame of environmental informatics to define the factors that affect water quality and quantity parameters on lake ecosystems. Karla Reservoir, a Greek hypertrophic constructed lake, suffers from continuous water degradation because of excessive nutrients loading, unaccomplished works, and operational malfunctions. Ecological modelling is conducted to simulate in-lake nutrient dynamics and to reveal the factors affecting its trophic state, while several operational scenarios and a climatic one is applied to estimate the fate of the lake under these hypothetical circumstances. Moreover, cyanotoxins (microcystins) are being modelled through a current artificially intelligence algorithm (ANFIS). Next, cyanobacteria biomass and recreational health risk levels associated to cyanobacterial abundance are modelled on a network of Northern European lakes. Stepwise linear regression, a series of machine learning algorithms and Bayesian hierarchical modelling are applied to test their efficiency in producing reliable results. Lastly, an analysis in terms of which types of crops export the most virtual water through trade, in relation to the benefit in Greek economy, is conducted.Σε αυτή τη διδακτορική διατριβή διερευνάται η δυναμική της οικολογικής μοντελοποίησης υπό το πλαίσιο της περιβαλλοντικής πληροφορικής στον προσδιορισμό των παραγόντων που επηρεάζουν ποιοτικές και ποσοτικές παραμέτρους του νερού σε λιμναία οικοσυστήματα. Η λίμνη Κάρλα, μία ελληνική υπερτροφική λίμνη, αντιμετωπίζει συνεχή υποβάθμιση των υδάτων της εξαιτίας της εκτεταμένης φόρτισης με θρεπτικά, των ανολοκλήρωτων έργων του αρχικού σχεδιασμού του ταμιευτήρα, καθώς και των λειτουργικών αστοχιών. Η οικολογική μοντελοποίηση χρησιμοποιείται ως εργαλείο για την προσομοίωση της δυναμικής των θρεπτικών στη λίμνη, για τον εντοπισμό των παραγόντων που επηρεάζουν την τροφική της κατάσταση, ενώ εφαρμόζονται αρκετά διαχειριστικά σενάρια και ένα σενάριο κλιματικής αλλαγής με στόχο την εκτίμηση των επιπτώσεών τους στην κατάσταση της λίμνης. Επιπλέον, γίνεται μοντελοποίηση των μικροκυστινών μέσω ενός σύγχρονου αλγορίθμου τεχνητής νοημοσύνης (ANFIS). Στη συνέχεια, μοντελοποιούνται η κυανοβακτηριακή βιομάζα και τα επίπεδα κινδύνου για την ανθρώπινη υγεία που σχετίζονται με την κυανοβακτηριακή αφθονία, σε ένα δίκτυο Βόρειων Ευρωπαϊκών λιμνών. Εφαρμόζονται η μέθοδος της σταδιακής γραμμικής παλινδρόμησης, μια σειρά από αλγορίθμους μηχανικής εκμάθησης και η Μπαϋεζιανή ιεραρχική μοντελοποίηση, με στόχο την αξιολόγηση της αποτελεσματικότητάς τους στην πρόβλεψη. Τέλος, πραγματοποιείται ανάλυση σχετικά με το ποιοι τύποι καλλιεργειών εξάγουν το περισσότερο εικονικό νερό μέσω του εμπορίου, σε συνάρτηση με το όφελος στην ελληνική οικονομία

Rett syndrome: insights into genetic, molecular and circuit mechanisms

Rett syndrome (RTT) is a severe neurological disorder caused by mutations in the gene encoding methyl-CpG-binding protein 2 (MeCP2). Almost two decades of research into RTT have greatly advanced our understanding of the function and regulation of the multifunctional protein MeCP2. Here, we review recent advances in understanding how loss of MeCP2 impacts different stages of brain development, discuss recent findings demonstrating the molecular role of MeCP2 as a transcriptional repressor, assess primary and secondary effects of MeCP2 loss and examine how loss of MeCP2 can result in an imbalance of neuronal excitation and inhibition at the circuit level along with dysregulation of activity-dependent mechanisms. These factors present challenges to the search for mechanism-based therapeutics for RTT and suggest specific approaches that may be more effective than others.National Institutes of Health (Grants MH085802 and EY007023