Identification of protein-protein interaction bridges for multiple sclerosis.

MOTIVATION: Identifying and prioritizing disease-related proteins is an important scientific problem to develop proper treatments. Network science has become an important discipline to prioritize such proteins. Multiple sclerosis, an autoimmune disease for which there is still no cure, is characterized by a damaging process called demyelination. Demyelination is the destruction of myelin, a structure facilitating fast transmission of neuron impulses, and oligodendrocytes, the cells producing myelin, by immune cells. Identifying the proteins that have special features on the network formed by the proteins of oligodendrocyte and immune cells can reveal useful information about the disease. RESULTS: We investigated the most significant protein pairs that we define as bridges among the proteins providing the interaction between the two cells in demyelination, in the networks formed by the oligodendrocyte and each type of two immune cells (i.e. macrophage and T-cell) using network analysis techniques and integer programming. The reason, we investigated these specialized hubs was that a problem related to these proteins might impose a bigger damage in the system. We showed that 61%-100% of the proteins our model detected, depending on parameterization, have already been associated with multiple sclerosis. We further observed the mRNA expression levels of several proteins we prioritized significantly decreased in human peripheral blood mononuclear cells of multiple sclerosis patients. We therefore present a model, BriFin, which can be used for analyzing processes where interactions of two cell types play an important role. AVAILABILITY AND IMPLEMENTATION: BriFin is available at https://github.com/BilkentCompGen/brifin.

Combined segmentation and classification-based approach to automated analysis of biomedical signals obtained from calcium imaging.

Automated screening systems in conjunction with machine learning-based methods are becoming an essential part of the healthcare systems for assisting in disease diagnosis. Moreover, manually annotating data and hand-crafting features for training purposes are impractical and time-consuming. We propose a segmentation and classification-based approach for assembling an automated screening system for the analysis of calcium imaging. The method was developed and verified using the effects of disease IgGs (from Amyotrophic Lateral Sclerosis patients) on calcium (Ca2+) homeostasis. From 33 imaging videos we analyzed, 21 belonged to the disease and 12 to the control experimental groups. The method consists of three main steps: projection, segmentation, and classification. The entire Ca2+ time-lapse image recordings (videos) were projected into a single image using different projection methods. Segmentation was performed by using a multi-level thresholding (MLT) step and the Regions of Interest (ROIs) that encompassed cell somas were detected. A mean value of the pixels within these boundaries was collected at each time point to obtain the Ca2+ traces (time-series). Finally, a new matrix called feature image was generated from those traces and used for assessing the classification accuracy of various classifiers (control vs. disease). The mean value of the segmentation F-score for all the data was above 0.80 throughout the tested threshold levels for all projection methods, namely maximum intensity, standard deviation, and standard deviation with linear scaling projection. Although the classification accuracy reached up to 90.14%, interestingly, we observed that achieving better scores in segmentation results did not necessarily correspond to an increase in classification performance. Our method takes the advantage of the multi-level thresholding and of a classification procedure based on the feature images, thus it does not have to rely on hand-crafted training parameters of each event. It thus provides a semi-autonomous tool for assessing segmentation parameters which allows for the best classification accuracy.

Motoneuron expression profiling identifies an association between an axonal splice variant of HDGF-related protein 3 and peripheral myelination.

Disorders that disrupt myelin formation during development or in adulthood, such as multiple sclerosis and peripheral neuropathies, lead to severe pathologies, illustrating myelin's crucial role in normal neural functioning. However, although our understanding of glial biology is increasing, the signals that emanate from axons and regulate myelination remain largely unknown. To identify the core components of the myelination process, here we adopted a microarray analysis approach combined with laser-capture microdissection of spinal motoneurons during the myelinogenic phase of development. We identified neuronal genes whose expression was enriched during myelination and further investigated hepatoma-derived growth factor-related protein 3 (HRP3 or HDGFRP3). HRP3 was strongly expressed in the white matter fiber tracts of the peripheral (PNS) and central (CNS) nervous systems during myelination and remyelination in a cuprizone-induced demyelination model. The dynamic localization of HPR3 between axons and nuclei during myelination was consistent with its axonal localization during neuritogenesis. To study this phenomenon, we identified two splice variants encoded by the HRP3 gene: the canonical isoform HRP3-I and a newly recognized isoform, HRP3-II. HRP3-I remained solely in the nucleus, whereas HRP3-II displayed distinct axonal localization both before and during myelination. Interestingly, HRP3-II remained in the nuclei of unmyelinated neurons and glial cells, suggesting the existence of a molecular machinery that transfers it to and retains it in the axons of neurons fated for myelination. Overexpression of HRP3-II, but not of HRP3-I, increased Schwann cell numbers and myelination in PNS neuron-glia co-cultures. However, HRP3-II overexpression in CNS co-cultures did not alter myelination.