OPALIN is an LGI1 receptor promoting oligodendrocyte differentiation.

Leucine-rich glioma-inactivated protein 1 (LGI1), a secretory protein in the brain, plays a critical role in myelination; dysfunction of this protein leads to hypomyelination and white matter abnormalities (WMAs). Here, we hypothesized that LGI1 may regulate myelination through binding to an unidentified receptor on the membrane of oligodendrocytes (OLs). To search for this hypothetic receptor, we analyzed LGI1 binding proteins through LGI1-3 × FLAG affinity chromatography with mouse brain lysates followed by mass spectrometry. An OL-specific membrane protein, the oligodendrocytic myelin paranodal and inner loop protein (OPALIN), was identified. Conditional knockout (cKO) of OPALIN in the OL lineage caused hypomyelination and WMAs, phenocopying LGI1 deficiency in mice. Biochemical analysis revealed the downregulation of Sox10 and Olig2, transcription factors critical for OL differentiation, further confirming the impaired OL maturation in Opalin cKO mice. Moreover, virus-mediated re-expression of OPALIN successfully restored myelination in Opalin cKO mice. In contrast, re-expression of LGI1-unbound OPALIN_K23A/D26A failed to reverse the hypomyelination phenotype. In conclusion, our study demonstrated that OPALIN on the OL membrane serves as an LGI1 receptor, highlighting the importance of the LGI1/OPALIN complex in orchestrating OL differentiation and myelination.

Chromatin 3D structure reconstruction with consideration of adjacency relationship among genomic loci.

BACKGROUND: Chromatin 3D conformation plays important roles in regulating gene or protein functions. High-throughout chromosome conformation capture (3C)-based technologies, such as Hi-C, have been exploited to acquire the contact frequencies among genomic loci at genome-scale. Various computational tools have been proposed to recover the underlying chromatin 3D structures from in situ Hi-C contact map data. As connected residuals in a polymer, neighboring genomic loci have intrinsic mutual dependencies in building a 3D conformation. However, current methods seldom take this feature into account. RESULTS: We present a method called ShNeigh, which combines the classical MDS technique with local dependence of neighboring loci modeled by a Gaussian formula, to infer the best 3D structure from noisy and incomplete contact frequency matrices. We validated ShNeigh by comparing it to two typical distance-based algorithms, ShRec3D and ChromSDE. The comparison results on simulated Hi-C dataset showed that, while keeping the high-speed nature of classical MDS, ShNeigh can recover the true structure better than ShRec3D and ChromSDE. Meanwhile, ShNeigh is more robust to data noise. On the publicly available human GM06990 Hi-C data, we demonstrated that the structures reconstructed by ShNeigh are more reproducible between different restriction enzymes than by ShRec3D and ChromSDE, especially at high resolutions manifested by sparse contact maps, which means ShNeigh is more robust to signal coverage. CONCLUSIONS: Our method can recover stable structures in high noise and sparse signal settings. It can also reconstruct similar structures from Hi-C data obtained using different restriction enzymes. Therefore, our method provides a new direction for enhancing the reconstruction quality of chromatin 3D structures.

[A new automated method to identify emission line star from massive spectra].

Stellar spectra are characterized by obvious absorption lines or absorption bands, while those with emission lines are usually special stars such as cataclysmic variable stars (CVs), HerbigAe/Be etc. The further study of this kind of spectra is meaningful. The present paper proposed a new method to identify emission line stars (ELS) spectra automatically. After the continuum normalization is done for the original spectral flux, line detection is made by comparing the normalized flux with the mean and standard deviation of the flux in its neighbor region The results of the experiment on massive spectra from SDSS DR8 indicate that the method can identify ELS spectra completely and accurately. Since no complex transformation and computation are involved in this method, the identifying process is fast and it is ideal for the ELS detection in large sky survey projects like LAMOST and SDSS.

Research on a pulmonary nodule segmentation method combining fast self-adaptive FCM and classification.

The key problem of computer-aided diagnosis (CAD) of lung cancer is to segment pathologically changed tissues fast and accurately. As pulmonary nodules are potential manifestation of lung cancer, we propose a fast and self-adaptive pulmonary nodules segmentation method based on a combination of FCM clustering and classification learning. The enhanced spatial function considers contributions to fuzzy membership from both the grayscale similarity between central pixels and single neighboring pixels and the spatial similarity between central pixels and neighborhood and improves effectively the convergence rate and self-adaptivity of the algorithm. Experimental results show that the proposed method can achieve more accurate segmentation of vascular adhesion, pleural adhesion, and ground glass opacity (GGO) pulmonary nodules than other typical algorithms.