A Mixture model with random-effects components for clustering correlated gene-expression profiles

Motivation: The clustering of gene profiles across some experimental conditions of interest contributes significantly to the elucidation of unknown gene function, the validation of gene discoveries and the interpretation of biological processes. However, this clustering problem is not straightforward as the profiles of the genes are not all independently distributed and the expression levels may have been obtained from an experimental design involving replicated arrays. Ignoring the dependence between the gene profiles and the structure of the replicated data can result in important sources of variability in the experiments being overlooked in the analysis, with the consequent possibility of misleading inferences being made. We propose a random-effects model that provides a unified approach to the clustering of genes with correlated expression levels measured in a wide variety of experimental situations. Our model is an extension of the normal mixture model to account for the correlations between the gene profiles and to enable covariate information to be incorporated into the clustering process. Hence the model is applicable to longitudinal studies with or without replication, for example, time-course experiments by using time as a covariate, and to cross-sectional experiments by using categorical covariates to represent the different experimental classes. Results: We show that our random-effects model can be fitted by maximum likelihood via the EM algorithm for which the E(expectation) and M(maximization) steps can be implemented in closed form. Hence our model can be fitted deterministically without the need for time-consuming Monte Carlo approximations. The effectiveness of our model-based procedure for the clustering of correlated gene profiles is demonstrated on three real datasets, representing typical microarray experimental designs, covering time-course, repeated-measurement and cross-sectional data. In these examples, relevant clusters of the genes are obtained, which are supported by existing gene-function annotation. A synthetic dataset is considered too.

Phosphoregulators: Protein kinases and protein phosphatases of mouse

With the completion of the human and mouse genome sequences, the task now turns to identifying their encoded transcripts and assigning gene function. In this study, we have undertaken a computational approach to identify and classify all of the protein kinases and phosphatases present in the mouse gene complement. A nonredundant set of these sequences was produced by mining Ensembl gene predictions and publicly available cDNA sequences with a panel of InterPro domains. This approach identified 561 candidate protein kinases and 162 candidate protein phosphatases. This cohort was then analyzed using TribeMCL protein sequence similarity clustering followed by CLUSTALV alignment and hierarchical tree generation. This approach allowed us to (1) distinguish between true members of the protein kinase and phosphatase families and enzymes of related biochemistry, (2) determine the structure of the families, and (3) suggest functions for previously uncharacterized members. The classifications obtained by this approach were in good agreement with previous schemes and allowed us to demonstrate domain associations with a number of clusters. Finally, we comment on the complementary nature of cDNA and genome-based gene detection and the impact of the FANTOM2 transcriptome project.

Design of artificial myocardial microtissues

Cultivation technologies promoting organization of mammalian cells in three dimensions are essential for gene-function analyses as well as drug testing and represent the first step toward the design of tissue replacements and bioartificial organs. Embedded in a three-dimensional environment, cells are expected to develop tissue-like higher order intercellular structures (cell-cell contacts, extracellular matrix) that orchestrate cellular functions including proliferation, differentiation, apoptosis, and angiogenesis with unmatched quality. We have refined the hanging drop cultivation technology to pioneer beating heart microtissues derived from pure primary rat and mouse cardiomyocyte cultures as well as mixed populations reflecting the cell type composition of rodent hearts. Phenotypic characterization combined with detailed analysis of muscle-specific cell traits, extracellular matrix components, as well as endogenous vascular endothelial growth factor (VEGF) expression profiles of heart microtissues revealed (1) a linear cell number-microtissue size correlation, (2) intermicrotissue superstructures, (3) retention of key cardiomyocyte-specific cell qualities, (4) a sophisticated extracellular matrix, and (5) a high degree of self-organization exemplified by the tendency of muscle structures to assemble at the periphery of these myocardial spheroids. Furthermore (6), myocardial spheroids support endogenous VEGF expression in a size-dependent manner that will likely promote vascularization of heart microtissues produced from defined cell mixtures as well as support connection to the host vascular system after implantation. As cardiomyocytes are known to be refractory to current transfection technologies we have designed lentivirus-based transduction strategies to lead the way for genetic engineering of myocardial microtissues in a clinical setting.

Versatile peptide dendrimers for nucleic acid delivery

Dendrimers are nonviral vectors that have attracted interest on account of a number of features. They are structurally versatile because their size, shape, and surface charge can be selectively altered. Here we examine the functions of a new family of composite dendrimers that were synthesized with lipidic amino acid cores. These dendrimers are bifunctional because they are characterized by positively charged (lysine) modules for interaction with nucleic acids and neutral lipidic moieties for membrane lipid-bilayer transit. We assessed their structure-function correlations by a combination of molecular and biophysical techniques. Our assessment revealed an unexpected pleitropy of functions subserved by these vectors that included plasmid and oligonucleotide delivery. We also generated a firefly luciferase cell line in which we could modulate luciferase activity by RNA interference. We found that these vectors could also mediate RNA suppression of luciferase expression by delivering double-stranded luciferase transcripts generated in vitro. The structural uniqueness of these lipidic peptide dendrimers coupled with their ease and specificity of assembly and the versatility in their choice of cargo, puts them in a new category of macromolecule carriers. These vectors, therefore, have potential applications as epigenetic modifiers of gene function. (C) 2004 Wiley-Liss, Inc. and the American Pharmacists Association.

Genome reduction in Leptospira borgpetersenii reflects limited transmission potential

Leptospirosis is one of the most common zoonotic diseases in the world, resulting in high morbidity and mortality in humans and affecting global livestock production. Most infections are caused by either Leptospira borgpetersenii or Leptospira interrogans, bacteria that vary in their distribution in nature and rely on different modes of transmission. We report the complete genomic sequences of two strains of L. borgpetersenii serovar Hardjo that have distinct phenotypes and virulence. These two strains have nearly identical genetic content, with subtle frameshift and point mutations being a common form of genetic variation. Starkly limited regions of synteny are shared between the large chromosomes of L. borgpetersenii and L. interrogans, probably the result of frequent recombination events between insertion sequences. The L. borgpetersenii genome is ≈700 kb smaller and has a lower coding density than L. interrogans, indicating it is decaying through a process of insertion sequence-mediated genome reduction. Loss of gene function is not random but is centered on impairment of environmental sensing and metabolite transport and utilization. These features distinguish L. borgpetersenii from L. interrogans, a species with minimal genetic decay and that survives extended passage in aquatic environments encountering a mammalian host. We conclude that L. borgpetersenii is evolving toward dependence on a strict host-to-host transmission cycle.

Multilevel modelling for inference of genetic regulatory networks

Time-course experiments with microarrays are often used to study dynamic biological systems and genetic regulatory networks (GRNs) that model how genes influence each other in cell-level development of organisms. The inference for GRNs provides important insights into the fundamental biological processes such as growth and is useful in disease diagnosis and genomic drug design. Due to the experimental design, multilevel data hierarchies are often present in time-course gene expression data. Most existing methods, however, ignore the dependency of the expression measurements over time and the correlation among gene expression profiles. Such independence assumptions violate regulatory interactions and can result in overlooking certain important subject effects and lead to spurious inference for regulatory networks or mechanisms. In this paper, a multilevel mixed-effects model is adopted to incorporate data hierarchies in the analysis of time-course data, where temporal and subject effects are both assumed to be random. The method starts with the clustering of genes by fitting the mixture model within the multilevel random-effects model framework using the expectation-maximization (EM) algorithm. The network of regulatory interactions is then determined by searching for regulatory control elements (activators and inhibitors) shared by the clusters of co-expressed genes, based on a time-lagged correlation coefficients measurement. The method is applied to two real time-course datasets from the budding yeast (Saccharomyces cerevisiae) genome. It is shown that the proposed method provides clusters of cell-cycle regulated genes that are supported by existing gene function annotations, and hence enables inference on regulatory interactions for the genetic network.