Functions of ULK1 in autophagy and non-autophagy pathways and its implications in human physiology and disease

ULK1 (unc-51 like autophagy activating kinase 1), a mammalian serine/threonine kinase, is a key component of autophagy initiation complex and helps to induce all types of autophagy. Canonical autophagy is a process in which, through the interactions of a series of autophagy-related proteins, damaged organelles or misfolded proteins are engulfed by autophagosomes and then merged with lysosomes to be degraded. Thus, canonical autophagy is an important constituent part of the cellular “quality control.” Besides, accumulating evidence indicates that ULK1 exerts autophagy-independent effects in a cell-specific manner. For example, ULK1 facilitates neurite elongation through the regulation of endoplasmic reticulum (ER)–Golgi trafficking in neurons, stimulates phosphopentose pathway to help NADPH (nicotinamide adenine dinucleotide phosphate hydrogen) production, and acts as a duplex regulator in type I IFN (type I interferon) induced innate immune response. Considering the importance and diversity of ULK1 in various biological processes, this review aims to present a comprehensive overview of autophagy and non-autophagy related functions of ULK1 in a variety of human physiological, pathological, and disease processes.     

Amyloidogenic Properties of Peptides Derived from the VHL Tumor Suppressor Protein

The von Hippel-Lindau tumor suppressor protein (pVHL) is involved in maintaining cellular oxygen homeostasis through the regulated degradation of HIF-α. The intrinsically disordered nature of pVHL makes it prone to aggregation that impairs its function, and this is further aggravated in mutant versions of the protein, thus promoting tumor development. By using in silico analysis, we predicted six peptide fragments from pVHL to be amyloidogenic. This was verified for two of the peptides by biophysical approaches, which demonstrated self-assembly and formation of β-sheet-rich aggregates, which, under transmission electron microscopy, atomic force microscopy, and X-ray diffraction, displayed typical fibrillar amyloid characteristics. These motifs may serve as proxies for exploring the nature of pVHL aggregation.     

Are quantum dots ready for in vivo imaging in human subjects?

Nanotechnology has the potential to profoundly transform the nature of cancer diagnosis and cancer patient management in the future. Over the past decade, quantum dots (QDs) have become one of the fastest growing areas of research in nanotechnology. QDs are fluorescent semiconductor nanoparticles suitable for multiplexed in vitro and in vivo imaging. Numerous studies on QDs have resulted in major advancements in QD surface modification, coating, biocompatibility, sensitivity, multiplexing, targeting specificity, as well as important findings regarding toxicity and applicability. For in vitro applications, QDs can be used in place of traditional organic fluorescent dyes in virtually any system, outperforming organic dyes in the majority of cases. In vivo targeted tumor imaging with biocompatible QDs has recently become possible in mouse models. With new advances in QD technology such as bioluminescence resonance energy transfer, synthesis of smaller size non-Cd based QDs, improved surface coating and conjugation, and multifunctional probes for multimodality imaging, it is likely that human applications of QDs will soon be possible in a clinical setting.     

运用实时荧光定量RT-PCR检测人正常组织及肿瘤组织中BRDT表达

目的探讨BRDT基因在正常组织及癌组织中的分布及其作为肿瘤治疗靶分子的可能性。方法运用实时荧光定量PCR方法分析BRDT在人正常组织及癌组织中的表达,使用18S rRNA作为内对照。结果在所检测的正常组织中该蛋白的mRNA只存在于睾丸组织中,而在其它组织不表达;在检测的10例胃腺癌组织标本中,有6例mRNA的微弱表达;在检测的10例食管鳞状细胞癌组织标本中,有3例mRNA的微弱表达;在检测的12例子宫颈鳞状细胞癌组织标本中均未有表达;在检测9例子宫内膜腺癌组织标本中,有2例微弱表达;在检测12例脑癌组织标本中,只有1例微弱表达。结论BRDT不可能作为子宫颈鳞状细胞癌、脑癌、子宫内膜腺癌及食管鳞状细胞癌治疗的潜在分子靶点,是否能够做胃腺癌的靶点还需进一步探讨。     

A multi-objective strategy in genetic algorithms for gene selection of gene expression data

A microarray machine offers the capacity to measure the expression levels of thousands of genes simultaneously. It is used to collect information from tissue and cell samples regarding gene expression differences that could be useful for cancer classification. However, the urgent problems in the use of gene expression data are the availability of a huge number of genes relative to the small number of available samples, and the fact that many of the genes are not relevant to the classification. It has been shown that selecting a small subset of genes can lead to improved accuracy in the classification. Hence, this paper proposes a solution to the problems by using a multiobjective strategy in a genetic algorithm. This approach was tried on two benchmark gene expression data sets. It obtained encouraging results on those data sets as compared with an approach that used a single-objective strategy in a genetic algorithm. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Selecting informative genes from microarray data by using hybrid methods for cancer classification

Gene expression technology, namely microarrays, offers the ability to measure the expression levels of thousands of genes simultaneously in biological organisms. Microarray data are expected to be of significant help in the development of an efficient cancer diagnosis and classification platform. A major problem in these data is that the number of genes greatly exceeds the number of tissue samples. These data also have noisy genes. It has been shown in literature reviews that selecting a small subset of informative genes can lead to improved classification accuracy. Therefore, this paper aims to select a small subset of informative genes that are most relevant for cancer classification. To achieve this aim, an approach using two hybrid methods has been proposed. This approach is assessed and evaluated on two well-known microarray data sets, showing competitive results. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Surface enhanced Raman spectroscopy and its application to molecular and cellular analysis

In this paper, we review the state-of-the-art in surface-enhanced Raman scattering (SERS) based optical detection techniques with an application focus on cancer diagnostics. As we describe herein, SERS has several analytical, biological and engineering advantages over other methods including extremely high sensitivity, inherent molecular specificity of unlabeled targets, and narrow spectral bands. We review advances in both in vitro and in vivo applications of SERS and examine how technical issues with the technology are being addressed. A special technology focus is given to emerging optofluidic devices which aim to merge microfluidic and optical detection technologies into simple packages. We conclude with a brief discussion of some of the emerging challenges in the field and some of the approaches that are likely to enhance their application. Y. S. Huh and A. J. Chung contributed equally.     

Automatic joint classification and segmentation of whole cell 3D images

We present a machine learning tool for automatic texton-based joint classification and segmentation of mitochondria in MNT-1 cells imaged using ion-abrasion scanning electron microscopy (IA-SEM). For diagnosing signatures that may be unique to cellular states such as cancer, automatic tools with minimal user intervention need to be developed for analysis and mining of high-throughput data from these large volume data sets (typically ). Challenges for such a tool in 3D electron microscopy arise due to low contrast and signal-to-noise ratios (SNR) inherent to biological imaging. Our approach is based on block-wise classification of images into a trained list of regions. Given manually labeled images, our goal is to learn models that can localize novel instances of the regions in test datasets. Since datasets obtained using electron microscopes are intrinsically noisy, we improve the SNR of the data for automatic segmentation by implementing a 2D texture-preserving filter on each slice of the 3D dataset. We investigate texton-based region features in this work. Classification is performed by k-nearest neighbor (k-NN) classifier, support vector machines (SVMs), adaptive boosting (AdaBoost) and histogram matching using a NN classifier. In addition, we study the computational complexity vs. segmentation accuracy tradeoff of these classifiers. Segmentation results demonstrate that our approach using minimal training data performs close to semi-automatic methods using the variational level-set method and manual segmentation carried out by an experienced user. Using our method, which we show to have minimal user intervention and high classification accuracy, we investigate quantitative parameters such as volume of the cytoplasm occupied by mitochondria, differences between the surface area of inner and outer membranes and mean mitochondrial width which are quantities potentially relevant to distinguishing cancer cells from normal cells. To test the accuracy of our approach, these quantities are compared against manually computed counterparts. We also demonstrate extension of these methods to segment 3D images obtained using electron tomography.