A degradable polyethylenimine derivative with low toxicity for highly efficient gene delivery

Routine clinical implementation of human gene therapy awaits safe and efficient gene delivery methods. Polymeric vectors hold promise due to the availability of diverse chemistries, potentially providing targeting, low immunogenicity, nontoxicity, and robustness, but lack sufficient gene delivery efficiency. We have synthesized a versatile group of degradable polycations, through addition of 800-Da polyethylenimine (PEI) to small diacrylate cross-linkers. The degradable polymers reported here are similar in structure, size (14-30 kDa), and DNA-binding properties to commercially available 25-kDa PEI, but mediate gene expression two- to 16-fold more efficiently and are essentially nontoxic. These easily synthesized polymers are some of the most efficient polymeric vectors reported to date and provide a versatile platform for investigation of the effects of polymer structure and degradation rate on gene delivery efficiency.     

Computational strategies for analyzing data in gene expression microarray experiments

Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.     

Isolation and characterization of the CYP71D16 trichome-specific promoter from Nicotiana tabacum L

Trichomes are specialized epidermal cells that produce secretions that are thought to provide a first line of defence against pests and pathogens. Many trichome-secreted compounds are used commercially as flavourings, medicines, etc. Described here is the cloning and characterization of the promoter of a tobacco trichome-specific P450 gene, CYP71D16. This promoter is shown to direct the specific expression of the reporter gene, beta-glucuronidase (GUS), in glandular trichomes of Nicotiana tabacum cv. T.I. 1068 at all developmental stages. With the full promoter, GUS activity was predominantly in the gland cell, with less in the stalk cell adjacent to the gland, and in lower stalk cells. GUS staining was also observed in the most distal trichome stalk cells of non-glandular trichomes found on variety T.I. 1112. Promoter deletion analysis revealed that the region from -223 to +111 bp is sufficient to direct trichome-specific expression, but not strong gland expression. Examination of the literature suggests that this is the first characterized trichome-specific-promoter shown to function at all stages of plant development. This promoter may provide efficient bioengineering to enhance pest and pathogen resistance, and for molecular farming based on the trichome gland system.     

Development and characterization of a Chinese hamster ovary cell-specific oligonucleotide microarray

The Chinese hamster ovary (CHO) cell line is one of the most widely used mammalian cell lines for biopharmaceutical production. We have developed and characterized a gene expression microarray (WyeHamster2a) specific for CHO cells that has enabled the study of ~3,500 sequences. Analysis of multiple sets of replicate scans showed that data derived from the WyeHamster2a array is highly reproducible confirming it as a robust tool for profiling. Twelve gene sequences were selected for follow-up RT-qPCR to confirm the accuracy and precision of the microarray results. In all but the most subtle gene expression differences, the microarray proved to be a reliable measure of differential gene expression. Finally, we were able to quantify the difference between using a bona fide CHO-specific microarray for profiling CHO cells versus an alternate, commercially available, rodent microarray such as a mouse or rat-specific format.     

Knowledge-based assessment of gene expression data from chemiluminescence detection.

The first problem in gene expression profiling to be solved is choosing the appropriate gene array, detection procedure, image analysis and data generation depending on the organism of interest, equipment and budget. The next one is how to deduce biologically meaningful data. We assessed gene expression data from chemiluminescent detection and empirically found criteria for the reliable identification of biologically meaningful expression ratios. Current statistical assessments are often applied unreflectedly concerning problems occurring in practice. So interesting results are considered to be irrelevant. This requires a laborious data check. We suggest automation. Our empirically found criteria were transformed into and validated by a knowledge-based system. This system is adaptable to all other methods of expression profiling. We compared the experience-based and new knowledge-based assessment of the expression data from our chemiluminescent and additionally radioactive detection of several experiments with published data to evaluate our entire procedure. With our adaptation of chemiluminescence detection to commercially available Escherichia coli gene arrays we present a useful alternative to common procedures in gene expression monitoring. Moreover, with our consideration of plasmid-harbouring E. coli strains we provide the opportunity to monitor gene expression during processes requiring any plasmids (e.g. recombinant protein expression).     

Gene signatures derived from a c-MET-driven liver cancer mouse model predict survival of patients with hepatocellular carcinoma

Biomarkers derived from gene expression profiling data may have a high false-positive rate and must be rigorously validated using independent clinical data sets, which are not always available. Although animal model systems could provide alternative data sets to formulate hypotheses and limit the number of signatures to be tested in clinical samples, the predictive power of such an approach is not yet proven. The present study aims to analyze the molecular signatures of liver cancer in a c-MET-transgenic mouse model and investigate its prognostic relevance to human hepatocellular carcinoma (HCC). Tissue samples were obtained from tumor (TU), adjacent non-tumor (AN) and distant normal (DN) liver in Tet-operator regulated (TRE) human c-MET transgenic mice (n = 21) as well as from a Chinese cohort of 272 HBV- and 9 HCV-associated HCC patients. Whole genome microarray expression profiling was conducted in Affymetrix gene expression chips, and prognostic significances of gene expression signatures were evaluated across the two species. Our data revealed parallels between mouse and human liver tumors, including down-regulation of metabolic pathways and up-regulation of cell cycle processes. The mouse tumors were most similar to a subset of patient samples characterized by activation of the Wnt pathway, but distinctive in the p53 pathway signals. Of potential clinical utility, we identified a set of genes that were down regulated in both mouse tumors and human HCC having significant predictive power on overall and disease-free survival, which were highly enriched for metabolic functions. In conclusions, this study provides evidence that a disease model can serve as a possible platform for generating hypotheses to be tested in human tissues and highlights an efficient method for generating biomarker signatures before extensive clinical trials have been initiated.     

The filamentous fungal gene expression database (FFGED)

Filamentous fungal gene expression assays provide essential information for understanding systemic cellular regulation. To aid research on fungal gene expression, we constructed a novel, comprehensive, free database, the filamentous fungal gene expression database (FFGED), available at http://bioinfo.townsend.yale.edu. FFGED features user-friendly management of gene expression data, which are assorted into experimental metadata, experimental design, raw data, normalized details, and analysis results. Data may be submitted in the process of an experiment, and any user can submit multiple experiments, thus classifying the FFGED as an “active experiment” database. Most importantly, FFGED functions as a collective and collaborative platform, by connecting each experiment with similar related experiments made public by other users, maximizing data sharing among different users, and correlating diverse gene expression levels under multiple experimental designs within different experiments. A clear and efficient web interface is provided with enhancement by AJAX (Asynchronous JavaScript and XML) and through a collection of tools to effectively facilitate data submission, sharing, retrieval and visualization.     

Bayesian modelling of shared gene function

MOTIVATION: Biological assays are often carried out on tissues that contain many cell lineages and active pathways. Microarray data produced using such material therefore reflect superimpositions of biological processes. Analysing such data for shared gene function by means of well-matched assays may help to provide a better focus on specific cell types and processes. The identification of genes that behave similarly in different biological systems also has the potential to reveal new insights into preserved biological mechanisms. RESULTS: In this article, we propose a hierarchical Bayesian model allowing integrated analysis of several microarray data sets for shared gene function. Each gene is associated with an indicator variable that selects whether binary class labels are predicted from expression values or by a classifier which is common to all genes. Each indicator selects the component models for all involved data sets simultaneously. A quantitative measure of shared gene function is obtained by inferring a probability measure over these indicators. Through experiments on synthetic data, we illustrate potential advantages of this Bayesian approach over a standard method. A shared analysis of matched microarray experiments covering (a) a cycle of mouse mammary gland development and (b) the process of in vitro endothelial cell apoptosis is proposed as a biological gold standard. Several useful sanity checks are introduced during data analysis, and we confirm the prior biological belief that shared apoptosis events occur in both systems. We conclude that a Bayesian analysis for shared gene function has the potential to reveal new biological insights, unobtainable by other means. AVAILABILITY: An online supplement and MatLab code are available at http://www.sykacek.net/research.html#mcabf     

Annotating genes of known and unknown function by large-scale coexpression analysis

About 40% of the proteins encoded in eukaryotic genomes are proteins of unknown function (PUFs). Their functional characterization remains one of the main challenges in modern biology. In this study we identified the PUF encoding genes from Arabidopsis (Arabidopsis thaliana) using a combination of sequence similarity, domain-based, and empirical approaches. Large-scale gene expression analyses of 1,310 publicly available Affymetrix chips were performed to associate the identified PUF genes with regulatory networks and biological processes of known function. To generate quality results, the study was restricted to expression sets with replicated samples. First, genome-wide clustering and gene function enrichment analysis of clusters allowed us to associate 1,541 PUF genes with tightly coexpressed genes for proteins of known function (PKFs). Over 70% of them could be assigned to more specific biological process annotations than the ones available in the current Gene Ontology release. The most highly overrepresented functional categories in the obtained clusters were ribosome assembly, photosynthesis, and cell wall pathways. Interestingly, the majority of the PUF genes appeared to be controlled by the same regulatory networks as most PKF genes, because clusters enriched in PUF genes were extremely rare. Second, large-scale analysis of differentially expressed genes was applied to identify a comprehensive set of abiotic stress-response genes. This analysis resulted in the identification of 269 PKF and 104 PUF genes that responded to a wide variety of abiotic stresses, whereas 608 PKF and 206 PUF genes responded predominantly to specific stress treatments. The provided coexpression and differentially expressed gene data represent an important resource for guiding future functional characterization experiments of PUF and PKF genes. Finally, the public Plant Gene Expression Database (http://bioweb.ucr.edu/PED) was developed as part of this project to provide efficient access and mining tools for the vast gene expression data of this study.     

Integration of clinical and gene expression data has a synergetic effect on predicting breast cancer outcome

Breast cancer outcome can be predicted using models derived from gene expression data or clinical data. Only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. We rigorously compare three different integration strategies (early, intermediate, and late integration) as well as classifiers employing no integration (only one data type) using five classifiers of varying complexity. We perform our analysis on a set of 295 breast cancer samples, for which gene expression data and an extensive set of clinical parameters are available as well as four breast cancer datasets containing 521 samples that we used as independent validation.mOn the 295 samples, a nearest mean classifier employing a logical OR operation (late integration) on clinical and expression classifiers significantly outperforms all other classifiers. Moreover, regardless of the integration strategy, the nearest mean classifier achieves the best performance. All five classifiers achieve their best performance when integrating clinical and expression data. Repeating the experiments using the 521 samples from the four independent validation datasets also indicated a significant performance improvement when integrating clinical and gene expression data. Whether integration also improves performances on other datasets (e.g. other tumor types) has not been investigated, but seems worthwhile pursuing. Our work suggests that future models for predicting breast cancer outcome should exploit both data types by employing a late OR or intermediate integration strategy based on nearest mean classifiers.     

Application of Bayesian decomposition for analysing microarray data

MOTIVATION: Microarray and gene chip technology provide high throughput tools for measuring gene expression levels in a variety of circumstances, including cellular response to drug treatment, cellular growth and development, tumorigenesis, among many other processes. In order to interpret the large data sets generated in experiments, data analysis techniques that consider biological knowledge during analysis will be extremely useful. We present here results showing the application of such a tool to expression data from yeast cell cycle experiments. RESULTS: Originally developed for spectroscopic analysis, Bayesian Decomposition (BD) includes two features which make it useful for microarray data analysis: the ability to assign genes to multiple coexpression groups and the ability to encode biological knowledge into the system. Here we demonstrate the ability of the algorithm to provide insight into the yeast cell cycle, including identification of five temporal patterns tied to cell cycle phases as well as the identification of a pattern tied to an approximately 40 min cell cycle oscillator. The genes are simultaneously assigned to the patterns, including partial assignment to multiple patterns when this is required to explain the expression profile. AVAILABILITY: The application is available free to academic users under a material transfer agreement. Go to http://bioinformatics.fccc.edu/ for more details.     

Functional genomics and cell wall biosynthesis in loblolly pine

Loblolly pine (Pinus taeda L.) is the most widely planted tree species in the USA and an important tree in commercial forestry world-wide. The large genome size and long generation time of this species present obstacles to both breeding and molecular genetic analysis. Gene discovery by partial DNA sequence determination of cDNA clones is an effective means of building a knowledge base for molecular investigations of mechanisms governing aspects of pine growth and development, including the commercially relevant properties of secondary cell walls in wood. Microarray experiments utilizing pine cDNA clones can be used to gain additional information about the potential roles of expressed genes in wood formation. Different methods have been used to analyze data from first-generation pine microarrays, with differing degrees of success. Disparities in predictions of differential gene expression between cDNA sequencing experiments and microarray experiments arise from differences in the nature of the respective analyses, but both approaches provide lists of candidate genes which should be further investigated for potential roles in cell wall formation in differentiating pine secondary xylem. Some of these genes seem to be specific to pine, while others also occur in model plants such as Arabidopsis, where they could be more efficiently investigated.     

Sequence-matched probes produce increased cross-platform consistency and more reproducible biological results in microarray-based gene expression measurements

Cancer derived microarray data sets are routinely produced by various platforms that are either commercially available or manufactured by academic groups. The fundamental difference in their probe selection strategies holds the promise that identical observations produced by more than one platform prove to be more robust when validated by biology. However, cross-platform comparison requires matching corresponding probe sets. We are introducing here sequence-based matching of probes instead of gene identifier-based matching. We analyzed breast cancer cell line derived RNA aliquots using Agilent cDNA and Affymetrix oligonucleotide microarray platforms to assess the advantage of this method. We show, that at different levels of the analysis, including gene expression ratios and difference calls, cross-platform consistency is significantly improved by sequence- based matching. We also present evidence that sequence-based probe matching produces more consistent results when comparing similar biological data sets obtained by different microarray platforms. This strategy allowed a more efficient transfer of classification of breast cancer samples between data sets produced by cDNA microarray and Affymetrix gene-chip platforms.     

Improving missing value estimation in microarray data with gene ontology

MOTIVATION: Gene expression microarray experiments produce datasets with frequent missing expression values. Accurate estimation of missing values is an important prerequisite for efficient data analysis as many statistical and machine learning techniques either require a complete dataset or their results are significantly dependent on the quality of such estimates. A limitation of the existing estimation methods for microarray data is that they use no external information but the estimation is based solely on the expression data. We hypothesized that utilizing a priori information on functional similarities available from public databases facilitates the missing value estimation. RESULTS: We investigated whether semantic similarity originating from gene ontology (GO) annotations could improve the selection of relevant genes for missing value estimation. The relative contribution of each information source was automatically estimated from the data using an adaptive weight selection procedure. Our experimental results in yeast cDNA microarray datasets indicated that by considering GO information in the k-nearest neighbor algorithm we can enhance its performance considerably, especially when the number of experimental conditions is small and the percentage of missing values is high. The increase of performance was less evident with a more sophisticated estimation method. We conclude that even a small proportion of annotated genes can provide improvements in data quality significant for the eventual interpretation of the microarray experiments. AVAILABILITY: Java and Matlab codes are available on request from the authors. SUPPLEMENTARY MATERIAL: Available online at http://users.utu.fi/jotatu/GOImpute.html.