Genomic expression patterns distinguish long-term from short-term glioblastoma survivors: a preliminary feasibility study

We used microarray analysis to investigate associations between genotypic expression profiles and survival phenotypes in patients with primary glioblastoma (GBM). Tumor samples from 7 long-term glioblastoma survivors (>24 months) and 13 short-term survivors (<9 months) were analyzed to detect differential patterns of gene expression between these groups and to identify genotypic subclasses of glioblastomas that correlate with survival phenotypes. Five unsupervised and three supervised clustering algorithms consistently and accurately grouped the tumors into genotypic subgroups corresponding to the two clinical survival phenotypes. Three unique prospective mathematical classification algorithms were subsequently trained to use expression data to stratify unknown glioblastomas between survival groups and performed this task with 100% accuracy in validation studies. A set of 1478 genes with significant differential expression (p<0.01) between long-term and short-term survivors was identified, and additional mathematical filtering was used to isolate a 43-gene "fingerprint" that distinguished survival phenotypes. Differential regulation of a subset of these genes was confirmed using RT-PCR. Gene ontology analysis of the fingerprint demonstrated pathophysiologic functions for the gene products that are consistent with current models of tumor biology, suggesting that differential expression of these genes may contribute etiologically to the observed differences in survival. These results demonstrate that unique expression profiles characterize genotypic subsets of primary GBMs associated with differential survival phenotypes, and these profiles can be used in a prospective fashion to assign unknown tumors to survival groups. Future efforts will focus on building more robust classifiers and identifying additional subclasses of gliomas with phenotypic significance.     

Characterization and phylogenetic analysis of ectoine biosynthesis genes from <Emphasis Type="Italic">Bacillus halodurans</Emphasis>

Ectoine, a cyclic tetrahydropyrimidine (2-methyl-1,4,5,6-tetrahydropyrimidine-4-carboxylic acid), is a natural compound, which serves as a protective substance in many bacterial cells. In this study, the putative ectABC gene cluster from Bacillus halodurans was heterologously expressed in E. coli and the production of ectoine was confirmed by HPLC analysis. The activity of the enzymes coded by the ectA, B and C genes were found to be higher in induced transgenic cells compared to the uninduced cells. Phylogenetic analysis revealed sequence identities ranging from 36–73% for ectA gene, 55–81% for ectB gene and 55–80% for ectC gene indicating that the enzymes are evolutionarily well conserved.     

Climbing the mountain: experimental design for the efficient optimization of stem cell bioprocessing

“To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of.” – R.A. Fisher While this idea is relevant across research scales, its importance becomes critical when dealing with the inherently large, complex and expensive process of preparing material for cell-based therapies (CBTs). Effective and economically viable CBTs will depend on the establishment of optimized protocols for the production of the necessary cell types. Our ability to do this will depend in turn on the capacity to efficiently search through a multi-dimensional problem space of possible protocols in a timely and cost-effective manner. In this review we discuss approaches to, and illustrate examples of the application of statistical design of experiments to stem cell bioprocess optimization.     

Cloning and heterologous expression of ectoine biosynthesis genes from Bacillus halodurans in Escherichia coli

The genes involved in the biosynthetic pathway of ectoine (2-methyl-1,4,5,6-tetrahydropyrimidine-4-carboxylic acid) from Bacillus halodurans were cloned as an operon and expressed in E. coli. Analysis of the deduced ectoine biosynthesis cluster amino acid sequence revealed that the ectoine operon contain 2,389 bp, encoded by three genes; ectA, ectB and ectC that encode proteins of 189, 427 and 129 amino acids with deduced molecular masses of 21,048, 47,120 and 14,797 Da respectively. Extracts of induced cells showed two bands at 41 kDa and 17 kDa, possibly corresponding to the products of the later two genes. However the expression of ectA gene could not be ascertained by SDS-PAGE. The activity of the ectA protein was confirmed by an acylation assay. The transgenic E. coli accumulated upto 4.6 mg ectoine/l culture. This is the first report of an engineered E. coli strain carrying the ectoine genes of the alkaliphilic bacterium, B. halodurans.     

苜蓿叶片水杨酸含量与其蓟马抗性的关系

采用不同抗蓟马特性的苜蓿品系,于不同蓟马虫口密度下测定苜蓿水杨酸含量和虫害程度,以明确苜蓿叶片水杨酸含量与其抗蓟马特性的关系.结果表明,苜蓿叶片游离态、结合态水杨酸含量均与苜蓿抗蓟马特性密切相关;高抗品系(抗蓟马品系)的水杨酸初始含量(0.539 mg·g-1)高于低抗品系(0.403 mg·g-1);与较低抗品系相比,高抗品系受害器官(叶)水杨酸含量随虫口密度和危害点面积的增大而增加相对缓慢,而随危害指数的增大而增加较快(3.84倍).可见,水杨酸主要是间接增强苜蓿对蓟马的抗性;高抗蓟马苜蓿品系能通过快速获得较高水杨酸含量来阻碍被危害伤口的扩大,降低其受危害程度,促使自身产量和品质提高.     

The European Renal Genome Project: An Integrated Approach Towards Understanding the Genetics of Kidney Development and Disease

Rapid progress in genome research creates a wealth of information on the functional annotation of mammalian genome sequences. However, as we accumulate large amounts of scientific information we are facing problems of how to integrate and relate the data produced by various genomic approaches. Here, we propose the novel concept of an organ atlas where diverse data from expression maps to histological findings to mutant phenotypes can be queried, compared and visualized in the context of a three-dimensional reconstruction of the organ. We will seek proof of concept for the organ atlas by elucidating genetic pathways involved in development and pathophysiology of the kidney. Such a kidney atlas may provide a paradigm for a new systems-biology approach in functional genome research aimed at understanding the genetic bases of organ development, physiology and disease.Key Words: EuReGene, kidney, genome, development, pathophysiology, genetics