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Objective: Myelin is essential for a healthy nervous system. Myelin formed by oligodendrocytes, accelerates impulse propagation and supports neuronal survival. Demyelination leads to neurodegeneration. In multiple sclerosis (MS) immune attack results in demyelination. Our goal is to dissect interactions among oligodendrocytes, neurons, and immune cells to improve our understanding of myelination and the demyelinating diseases. We aim to discover new targets for remyelination therapies. Methods: We are building tools for analyzing protein-protein and cell-to-cell interactions. To identify genes involved in myelination and MS, we developed a bioinformatics-based strategy, interactome analysis, which combines proteome and gene expression methodologies. Identified genes are evaluated in peripheral blood of MS patients and in mouse models. To accelerate drug discovery, 23 machine learning-based methodologies were assessed for myelin identification in fluorescent microscopy images. Results: Interactome analysis identified hundreds of proteinprotein interactions between pairs of oligodendrocytes, neurons, macrophages, microglia, and T cells. Most significant interactions are further analyzed in vivo and in vitro. Our customizedconvolutional neural network and Boosted Tree methods segmented myelin at over 98% accuracy. Identified myelin can be quantified and visualized in 3D. Conclusion: The interactome analysis yielded novel genes that are likely to be linked to MS. Machine learning-based methodologies are very effective in accelerating myelin quantification and thus, drug screens against demyelinating diseases such as MS. Overall, our innovative analysis strategies employing computer assistance produced novel avenues of exploration for myelination and demyelinating diseases. This study was supported TUBITAK (218S495,316S026), Istanbul Medipol University (BAP2018/06), and Turkish Academy of Sciences (GEBIP). [ABSTRACT FROM AUTHOR]

Pea3 subfamily of the ETS transcription factor superfamily has been implicated in metastasis, particularly in HER2/Neu-positive subclass of breast tumors, and was shown to regulate anchorage-independent growth and epithelial-to-mesenchymal transition of prostate cancer cells. Pea3 proteins were also shown to regulate normal development, including FGFdependent differentiation of retinal cells, and regulate motor neuron circuit selectivity in the spinal cord. Our laboratory has for a long time been working on how such a neural circuit specificity could be generated by Pea3 proteins, and what surface proteins might Pea3 proteins regulate to bring about such a circuit specificity. On the other hand, glial cells, including astrocytes and microglia, are also vital for neural circuitry and have been in the past years shown to be functionally diverse among different brain within compartments, or unique subpopulations of glia that support adult neurogenesis. It is an intriguing question whether this diversity originates during development or acquired later with neuronal activity, and whether glial diversity can be exploited or indeed directed to remedy CNS disorders, and whether Pea3 proteins can be used to that purpose. [ABSTRACT FROM AUTHOR]

Objective: By integrating genomic-scale metabolic networks with gene expression information, it is aimed to elucidate the molecular mechanisms of the disease and to identify candidate drug targets. Methods: In this study, the brain-specific metabolic model, iBrain606, was used to estimate metabolic changes in Parkinson's disease. We use transcriptome datasets from Parkinson's disease patients, obtained from NCBI Gene Expression Omnibus. iBrain606 and the transcriptome data were used as input to a bioinformatic algorithm, which enables the prediction of metabolic reaction rates for healthy and disease cases. This computational approach was used to determine candidate drug targets, the genes whose deletions will bring the activity of metabolism closer to the the healthy case. Results: Simulation results show a decrease in glucose and oxygen uptake rates, a significant increase in lactate secretion, a decrease in ATP production and a significantly low activity for the Krebs cycle rates. Additional simulations to bring the diseased state to healthy state enabled the identification of novel drug targets. Conclusion: Using the molecular crowding constraint with the integration of transcriptome data into the genome-scale metabolic network is a successful approach to understanding the mechanism of Parkinson's disease, finding new drug targets and candidates. [ABSTRACT FROM AUTHOR]

Pea3 proteins are a subfamily of the ETS transcription factor superfamily, consisting of Pea3, Erm and Er81. These proteins, which are expressed in different tissues exhibiting branching, play a role in a variety of events such as the formation of motor neuronal circuits in the nervous system, retinal differentiation, neurite extension. These proteins we have been working for many years in our laboratory soon began to be determined in our group by deciphering neurite extension mechanisms. The purpose of this present study is to understand the mechanisms of neurite extension through Pea3, to identify the genes which are regulated by Pea3. For this, novel target gene expression levels were investigated by microarray analysis and qPCR in Pea3 overexpressed various neural cell lines. The genes were classified by bioinformatic analysis, the pathways associated with neurons (neurotrophin signaling pathway, axon dynamics, etc.) were selected and the relationship between the genes in these pathways was examined and mapped by bioinformatic analysis. Our results showed that the members of Pea3 family regulate the expression of both common and unique genes in neuron-specific pathways at similar and / or different levels. In addition, the interaction mapping was created as a result of the informatics analysis. In order to elucidate which of these identified genes play a role in the selectivity of the motor neuron - sensory neuron circuits in Pea3 - overexpressed cells, studies on the relationship between different Pea3 family members should be conducted in a coculture system and the role of the Pea3 in circuit formation system as well as neuroglial connectivity should be studied. [ABSTRACT FROM AUTHOR]

Objective: In this study, genome-scale transcriptome data derived from Parkinson's disease patients are analyzed using bioinformatics methods to estimate significantly changed cellular pathways. Methods: A brain-specific genome-scale metabolic network model previously developed by our group, called iMS570, is used in the analysis. It is the most comprehensive metabolic network model in the literature. Here, we improved iMS570 by fully compartmenting all the reactions as cytosolic or mitochondrial based on the location of the catalyzing enzymes. The updated network included 799 reactions, a considerable improvement compared to the original model that included 630 reactions. The new metabolic network is called iBrain799. Since the aggregation of the alpha-synuclein protein in Parkinson's disease is a well-known phenomenon, the effect of molecular crowding on the functioning of metabolism was investigated in this study using iBrain799 and disease-specific transcriptome data. A computational approach which allows the prediction of reaction rates, called Flux Balance Analysis, was used for this purpose. Results: As a result of simulations, it was observed that the activity of energy metabolism decreased as the molecular crowding due to protein accumulation increased. Conclusion: The results reveal the effect of molecular crowding on Parkinson's disease as a function of the increase in the level of alpha-synuclein. This result is reasonable because if molecular crowding increases, then it becomes difficult for the enzyme to reach the substrates. For this reason, the rate of metabolism decreases. Low metabolism is a common phenomenon in neurodegenerative diseases, as in Parkinson's disease. When considered from this point of view, the use of the molecular crowding constraint in simulations gives realistic results. This research was financially supported by TUBITAK (Grant Number: 315S302) and TÜBA-GEBİP (2015). [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) is a demyelinating disorder that affects multiple regions of the central nervous system such as the brain, spinal cord, and optic nerves. Susceptibility to MS, as well as disease progression rates, displays marked patient-to-patient variability. To date, biomarkers that forecast differences in clinical phenotypes and outcomes have been limited. In this context, cell-type-specific interactome analyses offer important prospects and hope for novel diagnostics and therapeutics. We report here an original study using bioinformatic analysis of MS data sets that revealed interaction profiles as well as specific hub proteins in white matter (WM) and gray matter (GM) that appear critical for disease mechanisms. First, cell-type-specific interactome analyses suggested that while interactions within the WM were focused on oligodendrocytes, interactions within the GM were mostly neuron centric. Second, hub proteins such as APP, EGLN3, PTEN, and LRRK2 were identified to be differentially regulated in MS data sets. Lastly, a comparison of the brain and peripheral blood samples identified biomarker candidates such as NRGN, CRTC1, CDC42, and IFITM3 to be differentially expressed in different types of MS. These findings offer a unique cell-type-specific cell-to-cell interaction network in MS and identify potential biomarkers by comparative analysis of the brain and the blood transcriptomics. From a study design and methodology perspective, we suggest that the cell-type-specific interactome analysis is an important systems science frontier that might offer new insights on other neurodegenerative and brain disorders as well.