BackgroundLarge-scale sequencing of entire genomes has ushered in a new age in biology. One of the next grand challenges is to dissect the cellular networks consisting of many individual functional modules. Defining co-expression networks without ambiguity based on genome-wide microarray data is difficult and current methods are not robust and consistent with different data sets. This is particularly problematic for little understood organisms since not much existing biological knowledge can be exploited for determining the threshold to differentiate true correlation from random noise. Random matrix theory (RMT), which has been widely and successfully used in physics, is a powerful approach to distinguish system-specific, non-random properties embedded in complex systems from random noise. Here, we have hypothesized that the universal predictions of RMT are also applicable to biological systems and the correlation threshold can be determined by characterizing the correlation matrix of microarray profiles using random matrix theory.ResultsApplication of random matrix theory to microarray data of S. oneidensis, E. coli, yeast, A. thaliana, Drosophila, mouse and human indicates that there is a sharp transition of nearest neighbour spacing distribution (NNSD) of correlation matrix after gradually removing certain elements insider the matrix. Testing on an in silico modular model has demonstrated that this transition can be used to determine the correlation threshold for revealing modular co-expression networks. The co-expression network derived from yeast cell cycling microarray data is supported by gene annotation. The topological properties of the resulting co-expression network agree well with the general properties of biological networks. Computational evaluations have showed that RMT approach is sensitive and robust. Furthermore, evaluation on sampled expression data of an in silico modular gene system has showed that under-sampled expressions do not affect the recovery of gene co-expression network. Moreover, the cellular roles of 215 functionally unknown genes from yeast, E. coli and S. oneidensis are predicted by the gene co-expression networks using guilt-by-association principle, many of which are supported by existing information or our experimental verification, further demonstrating the reliability of this approach for gene function prediction.ConclusionOur rigorous analysis of gene expression microarray profiles using RMT has showed that the transition of NNSD of correlation matrix of microarray profile provides a profound theoretical criterion to determine the correlation threshold for identifying gene co-expression networks.

The medical community has expressed significant interest in the development of new types of artificial bones that mimic natural bones. In this study, computed tomography (CT)-guided fused deposition modeling (FDM) was employed to fabricate polycaprolactone (PCL)/hydroxyapatite (HA) and PCL 3D artificial bones to mimic natural goat femurs. The in vitro mechanical properties, in vitro cell biocompatibility, and in vivo performance of the artificial bones in a long load-bearing goat femur bone segmental defect model were studied. All of the results indicate that CT-guided FDM is a simple, convenient, relatively low-cost method that is suitable for fabricating natural bonelike artificial bones. Moreover, PCL/HA 3D artificial bones prepared by CT-guided FDM have more close mechanics to natural bone, good in vitro cell biocompatibility, biodegradation ability, and appropriate in vivo new bone formation ability. Therefore, PCL/HA 3D artificial bones could be potentially be of use in the treatment of patients with clinical bone defects.

DNA self-assembling nanostructure has been considered as a promising candidate as a drug delivery vehicle because of its compactness, mechanical stability, and noncytotoxicity. In this work, we developed functional, multiform DNA nanostructures by appending a tumor-penetrating peptide to tetrahedral DNA nanostructure (p-TDN). This functional structure is able to efficiently increase the rate of uptake of glioblastoma cell U87MG compared with the DNA tetrahedron and the double-stranded DNA structures. We found that the DNA tetrahedron plays the main role in the endocytosis of U87MG cells, whereas the tumor-penetrating peptide could also bind to transmembrane glycoprotein neuropilin-1 and mediate the endocytosis of the p-TDN nanostructure. Moreover, given the high efficiency of the growth inhibitory effect of the p-TDN loading doxorubicin hydrochloride, the p-TDN distinguishes itself as a promising candidate as an effective delivery carrier.

The present study demonstrates that podocyte-restricted expression of HIV-1 gene products is sufficient for the development of collapsing glomerulosclerosis in the setting of susceptible genetic background.

Chronic kidney disease (CKD) will progress to end stage without treatment, but the decline of renal function may not be linear. Compared with glomerular filtration rate and proteinuria, new surrogate markers, such as kidney injury molecule-1, neutrophil gelatinase-associated protein, apolipoprotein A-IV, and soluble urokinase receptor, may allow potential intervention and treatment in the earlier stages of CKD, which could be useful for clinical trials. New omic-based technologies reveal potential new genomic and epigenomic mechanisms that appear different from those causing the initial disease. Various clinical studies also suggest that acute kidney injury is a major risk for progressive CKD. To ameliorate the progression of CKD, the first step is optimizing renin-angiotensin-aldosterone system blockade. New drugs targeting endothelin, transforming growth factor-β, oxidative stress, and inflammatory- and cell-based regenerative therapy may have add-on benefit.

This study aimed to identify the causative gene for HIV-1 associated nephropathy, a paradigmatic podocytopathy. A previous study demonstrated that transgenic expression of nonstructural HIV-1 genes selectively in podocytes in mice with FVB/N genetic background resulted in podocyte injury and glomerulosclerosis. In this study, transgenic mice that expressed individual HIV-1 genes in podocytes were generated. Five of six transgenic mice that expressed vpr developed podocyte damage and glomerulosclerosis. Analysis of an established vpr transgenic line revealed that transgenic mice on FVB/N but not on C57BL/6 genetic background developed podocyte injury by 8 wk of age, with later glomerulosclerosis. Four of 11 transgenic mice that expressed nef also developed podocyte injury. One transgenic line was established from the nef founder mouse with the mildest phenotype. Transgenic mice in this line developed mesangial expansion at 3 wk of age and mild focal podocyte damage at 10 wk of age. Mating with FVB/N mice did not augment nephropathy. None of the transgenic mice that expressed vif, tat, rev, or vpu in podocytes, even with the FVB/N genetic background, developed podocyte injury. For testing effects of simultaneous expression of vpr and nef, these two lines were mated. All nef:vpr double-transgenic mice showed severe podocyte injury and glomerulosclerosis by 4 wk of age. In contrast, all vpr or nef single-transgenic mice in the same litter uniformly showed no or much milder podocyte injury. These findings indicate that vpr and nef each can induce podocyte injury with a prominent synergistic interaction.

We show that spectral fluctuation of interaction matrices of yeast a core protein interaction network and a metabolic network follows the description of the Gaussian orthogonal ensemble (GOE) of random matrix theory (RMT). Furthermore, we demonstrate that while the global biological networks evaluated belong to GOE, removal of interactions between constituents transitions the networks to systems of isolated modules described by the Poisson statistics of RMT. Our results indicate that although biological networks are very different from other complex systems at the molecular level, they display the same statistical properties at large scale. The transition point provides a new objective approach for the identification of functional modules.The cell is a complex system that contains numerous functionally diverse elements, including protein, DNA, RNA and small molecules. Understanding the fundamental principles and behavioral properties of the cell as a system has become a key research activity in the post-genomic era. Research on the topological properties of large scale networks of cell constituents has shown that biological networks share some fundamental topological properties, including scale-free, small-world, hierarchical, modular [1] and self-similar [2] properties, with other complex systems, such as the internet and social networks. Inspired by the electrical engineering paradigm, small gene circuit descriptions combined with mathematical modeling have been utilized to understand small subsystems of cellular processes [3]. Unfortunately, the huge number of constituents and their complex relationships in the cell make the mathematical modeling of large-scale biological systems challenging. It is of significant importance to understand the nature of the structure and interactions of biological networks for achieving quantitative description of their functions.In this Letter, we use RMT to analyze the structure and interactions of biological networks. RMT, initially proposed by Wigner and Dyson in the 1960s for studying the spectrum of complex nuclei [4], is a powerful approach for the identification and modeling of phase transitions and dynamics in physical systems. It has been successfully used to study the behaviors of complex systems, such as spectral properties of large atoms [5], metal insulator transitions in disordered systems [6], spectra of quasiperiodic systems [7,8], chaotic systems [9], brain responses [10], and the stock market [11]. One of the essential statistical properties in the RMT is eigenvalue fluctuation. For real and symmetrical random matrices that represent the time-reversal invariant complex systems, the eigenvalue fluctuations follow two universal laws depending on the correlation property of eigenvalues. Strong correlation of eigenvalues leads to eigenvalue fluctuations described by the GOE. On the other hand, eigenvalue fluctuations follow Poisson statistics if there is no correlation between eigenvalues.In this study we have found that the spectral fluctuation of a yeast protei...

A major hallmark of prion diseases is the cerebral amyloid accumulation of the pathogenic PrP(Sc), an abnormally misfolded, protease-resistant, and beta-sheet rich protein. PrP106-126 is the key domain responsible for the conformational conversion and aggregation of PrP. It shares important physicochemical characteristics with PrP(Sc) and presents similar neurotoxicity as PrP(Sc). By combination of fluorescence polarization, dye release assay and in situ time-lapse atomic force microscopy (AFM), we investigated the PrP106-126 amide interacting with the large unilamellar vesicles (LUVs) and the supported lipid bilayers (SLBs). The results suggest that the interactions involve a poration-mediated process: firstly, the peptide binding results in the formation of pores in the membranes, which penetrate only half of the membranes; subsequently, PrP106-126 amide undergoes the poration-mediated diffusion in the SLBs, represented by the formation and expansion of the flat high-rise domains (FHDs). The possible mechanisms of the interactions between PrP106-126 amide and lipid membranes are proposed based on our observations.