Stage-dependent gene expression profiles during natural killer cell development

Natural killer (NK) cells develop from hematopoietic stem cells (HSCs) in the bone marrow. To understand the molecular regulation of NK cell development, serial analysis of gene expression (SAGE) was applied to HSCs, NK precursor (pNK) cells, and mature NK cells (mNK) cultured without or with OP9 stromal cells. From 170,464 total individual tags from four SAGE libraries, 35,385 unique genes were identified. A set of genes was expressed in a stage-specific manner: 15 genes in HSCs, 30 genes in pNK cells, and 27 genes in mNK cells. Among them, lipoprotein lipase induced NK cell maturation and cytotoxic activity. Identification of genome-wide profiles of gene expression in different stages of NK cell development affords us a fundamental basis for defining the molecular network during NK cell development.     

Remodelling of the angular collagen fiber distribution in cardiovascular tissues

Understanding collagen fiber remodelling is desired to optimize the mechanical conditioning protocols in tissue-engineering of load-bearing cardiovascular structures. Mathematical models offer strong possibilities to gain insight into the mechanisms and mechanical stimuli involved in these remodelling processes. In this study, a framework is proposed to investigate remodelling of angular collagen fiber distribution in cardiovascular tissues. A structurally based model for collagenous cardiovascular tissues is extended with remodelling laws for the collagen architecture, and the model is subsequently applied to the arterial wall and aortic valve. For the arterial wall, the model predicts the presence of two helically arranged families of collagen fibers. A branching, diverging hammock-type fiber architecture is predicted for the aortic valve. It is expected that the proposed model may be of great potential for the design of improved tissue engineering protocols and may give further insight into the pathophysiology of cardiovascular diseases.     

Microarray-based gene expression profiles in multiple tissues of the domesticated silkworm, Bombyx mori

We designed and constructed a genome-wide microarray with 22,987 70-mer oligonucleotides covering the presently known and predicted genes in the silkworm genome, and surveyed the gene expression in multiple silkworm tissues on day 3 of the fifth instar. Clusters of tissue-prevalent and tissue-specific genes and genes that are differentially expressed in different tissues were identified, and they reflect well major tissue-specific functions on the molecular level. The data presented in this study provide a new resource for annotating the silkworm genome.     

A computational model to describe the collagen orientation in statically cultured engineered tissues

Collagen provides cardiovascular tissues with the ability to withstand haemodynamic loads. A similar network is essential to obtain in tissue-engineered (TE) samples of the same nature. Yet, the mechanism of collagen orientation is not fully understood. Typically collagen remodelling is linked to mechanical loading. However, TE constructs also show an oriented collagen network when developed under static culture. Experiments under these conditions also indicate that the tissue gradually compacts due to contractile stresses developed in the α-actin fibres of the cells. Therefore, it is hypothesised that cellular contractile stresses are responsible for collagen orientation. A model describing the cellular α-actin turnover and the stresses developed by them is integrated in a structural constitutive model describing the mechanical behaviour of collagen fibres. Results show that the model can successfully capture the sample compaction, tissue stress generation and its heterogeneous collagen arrangement.     

Dynamics of extracellular matrix production and turnover in tissue engineered cardiovascular structures

Appropriate matrix formation, turnover and remodeling in tissue-engineered small diameter vascular conduits are crucial requirements for their long-term patency and function. This complex process requires the deposition and accumulation of extracellular matrix molecules as well as the remodeling of this extracellular matrix (ECM) by matrix metalloproteinases (MMPs) and their endogenous inhibitors (TIMPs). In this study, we have investigated the dynamics of ECM production and the activity of MMPs and TIMPs in long-term tissue-engineered vascular conduits using quantitative ECM analysis, substrate gel electrophoresis, radiometric enzyme assays and Western blot analyses. Over a time period of 169 days in vivo, levels of elastin and proteoglycans/glycosaminoglycans in tissue-engineered constructs came to approximate those of their native tissue counter parts. The kinetics of collagen deposition and remodeling, however, apparently require a much longer time period. Through the use of substrate gel electrophoresis, proteolytic bands whose molecular weight was consistent with their identification as the active form of MMP-2 (approximately 64--66 kDa) were detected in all native and tissue-engineered samples. Additional proteolytic bands migrating at approximately 72 kDa representing the latent form of MMP-2 were detected in tissue-engineered samples at time points from 5 throughout 55 days. Radiometric assays of MMP-1 activity demonstrated no significant differences between the native and tissue-engineered samples. This study determines the dynamics of ECM production and turnover in a long-term tissue-engineered vascular tissue and highlights the importance of ECM remodeling in the development of successful tissue-engineered vascular structures.     

Analysis of gene expression profiles of gastric normal and cancer tissues by SAGE

In an attempt to understand the molecular bases of gastric cancer progression, we have analyzed the differentially expressed genes in gastric cancer by SAGE. Four SAGE cDNA tag libraries were constructed from two sets of gastric cancer and normal tissues and 241,127 tags were obtained. By comparing the tags from cancer and normal tissues, 414 differentially expressed tags, representing 383 genes, were identified in cancer tissues (p 相似文献   

Tumor-specific gene expression patterns with gene expression profiles

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, “RFE_Relief algorithm” was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.     

Tumor-specific gene expression patterns with gene expression profiles

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, "RFE_Relief algorithm" was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.     

Predicting node positivity in gastric cancer from gene expression profiles

Lymphovascular invasion (LVI) in gastric cancer is readily demonstrated pre-operatively by mucosal biopsy during endoscopy, which can also provide samples for gene expression profiling. We have examined gene expression associated with lymphovascular invasion in a cohort of gastric cancer patients and have developed a 28-gene predictor of tumor aggressiveness for forecasting risk of node positivity (N+), which could potentially be useful pre-operatively. The resulting model’s ranking of increasing tumor aggressiveness correlated positively with N+ status, reaching statistical significance, and was three times the correlation of LVI with N+ status.     

Pattern of lipid profiles in cardiovascular tissues of hysterectomized albino rats

Heart, dorsal aorta and plasma significantly accumulated the lipid components such as triacylglycerol, cholesterol and glycerol in two months period of hysterectomy which may lead to the development of cardiovascular disorders. Administration of PGF2 alpha decreased the lipid fractions of cardiovascular tissues probably due to luteolysis and ovarian activation.     

Recursive regularization for inferring gene networks from time-course gene expression profiles

Background  

Inferring gene networks from time-course microarray experiments with vector autoregressive (VAR) model is the process of identifying functional associations between genes through multivariate time series. This problem can be cast as a variable selection problem in Statistics. One of the promising methods for variable selection is the elastic net proposed by Zou and Hastie (2005). However, VAR modeling with the elastic net succeeds in increasing the number of true positives while it also results in increasing the number of false positives.     

Growth and development of collagen fibrils in immature tissues from rat and sheep

The collagen fibril diameter distribution of four immature tissues from both rat and sheep have been determined from the transverse sections observed in the transmission electron microscope. In many instances before birth, the form of the distribution for the tissues is both unimodal and sharp and the mean diameters of the distributions lie close to a multiple of 80 A. For some tissues, the collagen fibril diameter distributions may be resolved into a number of components, each of which represents a population of fibrils with a diameter close to a multiple of 80 A (8 nm). These data confirm and extend previous observations by the authors that small collagen fibrils growth occurs by the accretion of 80 A units. The form of the collagen fibrils diameter distribution at birth is broad for the sheep tissues but narrow for the rat tissues, thus confirming that the range of fibril diameters at this stage of life reflects the differing degree of development of precocious and altricious animals.     

Robust prediction of gene regulation in colorectal cancer tissues from DNA methylation profiles

DNA methylation is recognized as one of several epigenetic regulators of gene expression and as potential driver of carcinogenesis through gene-silencing of tumor suppressors and activation of oncogenes. However, abnormal methylation, even of promoter regions, does not necessarily alter gene expression levels, especially if the gene is already silenced, leaving the exact mechanisms of methylation unanswered. Using a large cohort of matching DNA methylation and gene expression samples of colorectal cancer (CRC; n = 77) and normal adjacent mucosa tissues (n = 108), we investigated the regulatory role of methylation on gene expression. We show that on a subset of genes enriched in common cancer pathways, methylation is significantly associated with gene regulation through gene-specific mechanisms. We built two classification models to infer gene regulation in CRC from methylation differences of tumor and normal tissues, taking into account both gene-silencing and gene-activation effects through hyper- and hypo-methylation of CpGs. The classification models result in high prediction performances in both training and independent CRC testing cohorts (0.92<AUC<0.97) as well as in individual patient data (average AUC = 0.82), suggesting a robust interplay between methylation and gene regulation. Validation analysis in other cancerous tissues resulted in lower prediction performances (0.69<AUC<0.90); however, it identified genes that share robust dependencies across cancerous tissues. In conclusion, we present a robust classification approach that predicts the gene-specific regulation through DNA methylation in CRC tissues with possible transition to different cancer entities. Furthermore, we present HMGA1 as consistently associated with methylation across cancers, suggesting a potential candidate for DNA methylation targeting cancer therapy.