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.

Chromosome Three-Dimensional Structure Reconstruction: An Iterative ShRec3D Algorithm.

The three-dimensional (3D) structure of chromosomes is of great significance to ensure that the genome performs various functions (e.g., gene expression) correctly and replicates and separates correctly in mitosis. Since the emergence of Hi-C in 2009, a new experimental technique in molecular biology, researchers have been paying more and more attention to the reconstruction of chromosome 3D structure. To reconstruct the 3D structure of chromosomes based on Hi-C experimental data, many algorithms have been proposed, among which ShRec3D is one of the most outstanding. In this article, an iterative ShRec3D algorithm is presented to greatly improve the native ShRec3D algorithm. Experimental results show that our algorithm can significantly promote the performance of ShRec3D, and this improvement is applicable to almost all data noise range and signal coverage range, so it is universal.

Three-dimensional reconstruction and quantitative analysis of the brain stem nuclei based on fast centroid auto-registration.

This paper introduces a three-dimensional (3D) reconstruction algorithm of the brain stem nuclei based on fast centroid auto-registration. The research is based on methods and theories of computer stereo vision, and by image information processing three-point pattern local search, registration and auto-tracing for the centroids of the brain stem nuclei were accomplished. We adopt two-peak threshold, edge detection and grayscale image enhancement to extract contours of the nuclei's structures. The experimental results obtain the spatial structure information and 3D image of the brain stem nuclei, show spatial relationship between 14 pairs of nuclei, and quantitate morphological parameters of each type of nuclei's 3D structure. This work is significant to neuroanatomy research and clinic applications. Furthermore, a software system named BRAIN.HUK is established.

Studies on quantitative analysis and automatic recognition of cell types of lung cancer.

Recognition of lung cancer cells is very important to the clinical diagnosis of lung cancer. In this paper we present a novel method to extract the structure characteristics of lung cancer cells and automatically recognize their types. Firstly soft mathematical morphology methods are used to enhance the grayscale image, to improve the definition of images, and to eliminate most of disturbance, noise and information of subordinate images, so the contour of target lung cancer cell and biological shape characteristic parameters can be extracted accurately. Then the minimum distance classifier is introduced to realize the automatic recognition of different types of lung cancer cells. A software system named "CANCER.LUNG" is established to demonstrate the efficiency of this method. The clinical experiments show that this method can accurately and objectively recognize the type of lung cancer cells, which can significantly improve the pathology research on the pathological changes of lung cancer and clinical assistant diagnoses.

Development of the aza-crown ether metal complexes as artificial hydrolase.

Hydrolases play a crucial role in the biochemical process, which can catalyze the hydrolysis of various compounds like carboxylic esters, phosphoesters, amides, nucleic acids, peptides, and so on. The design of artificial hydrolases has attracted extensive attention due to their scientific significance and potential applications in the field of gene medicine and molecular biology. Numerous macrocyclic metal complexes have been used as artificial hydrolase in the catalytic hydrolysis of the organic substrate. Aza-crown ether for this comment is a special class of the macrocyclic ligand containing both the nitrogen atoms and oxygen atoms in the ring. The studies showed that the aza-crown complexes exhibited high activity of hydrolytic enzyme. However, the aza-crown ether metal complex as artificial hydrolase is still very limited because of its difficulty in synthesis. This review summarizes the development of the aza-crown ether metal complexes as the artificial hydrolase, including the synthesis and catalysis of the transition metal complexes and lanthanide metal complexes of aza-crown ethers. The purpose of this review is to highlight: (1) the relationship between the structure and hydrolytic activity of synthetic hydrolase; (2) the synergistic effect of metal sites and ligands in the course of organic compound hydrolysis; and (3) the design strategies of the aza-crown ethers as hydrolase.

The synthesis and activities of novel mononuclear or dinuclear cyclen complexes bearing azole pendants as antibacterial and antifungal agents.

A series of novel compounds containing 1,4,7,10-tetraazacyclododecane and azoles were synthesized and characterized by (1)H NMR, MS and elemental analysis. Bioactive assay manifested that some target compounds, such as 11a, 11b and 11d, displayed good and broad spectrum antimicrobial activities with relative low MIC values against most of tested strains. These dinuclear complexes gave comparable or even better antimicrobial efficiencies than the reference drugs Fluconazole and Chloromycin. The result showed that the metal ions were the key factors to enhance the antimicrobial activities for mononuclear or dinuclear complexed in varying degrees. The interaction evaluation of compound 11b with bovine serum albumin (BSA) as an example was tested by fluorescence method. The thermodynamic parameters indicated that the hydrogen bonds and van der waals forces played the major roles in the strong association between dinuclear compound and BSA. The CCK-8 tests also confirmed the safeties of these dinuclear compounds in vitro.

Protein-protein networks construction and their relevance measurement based on multi-epitope-ligand-kartographie and gene ontology data of T-cell surface proteins for polymyositis.

Polymyositis is an inflammatory myopathy characterized by muscle invasion of T-cells penetrating the basal lamina and displacing the plasma membrane of normal muscle fibers. In order to understand the different adhesive mechanisms at the T-cell surface, Schubert randomly selected 19 proteins expressed at the T-cell surface and studied them using MELK technique [4], among which 15 proteins are picked up for further study by us. Two types of functional similarity networks are constructed for these proteins. The first type is MELK similarity network, which is constructed based on their MELK data by using the McNemar's test [24]. The second type is GO similarity network, which is constructed based on their GO annotation data by using the RSS method to measuring functional similarity. Then the subset surprisology theory is employed to measure the degree of similarity between two networks. Our computing results show that these two types of networks are high related. This conclusion added new values on MELK technique and expanded its applications greatly.