BEST: a novel computational approach for comparing gene expression patterns from early stages of Drosophila melanogaster development

Embryonic gene expression patterns are an indispensable part of modern developmental biology. Currently, investigators must visually inspect numerous images containing embryonic expression patterns to identify spatially similar patterns for inferring potential genetic interactions. The lack of a computational approach to identify pattern similarities is an impediment to advancement in developmental biology research because of the rapidly increasing amount of available embryonic gene expression data. Therefore, we have developed computational approaches to automate the comparison of gene expression patterns contained in images of early stage Drosophila melanogaster embryos (prior to the beginning of germ-band elongation); similarities and differences in gene expression patterns in these early stages have extensive developmental effects. Here we describe a basic expression search tool (BEST) to retrieve best matching expression patterns for a given query expression pattern and a computational device for gene interaction inference using gene expression pattern images and information on the associated genotypes and probes. Analysis of a prototype collection of Drosophila gene expression pattern images is presented to demonstrate the utility of these methods in identifying biologically meaningful matches and inferring gene interactions by direct image content analysis. In particular, the use of BEST searches for gene expression patterns is akin to that of BLAST searches for finding similar sequences. These computational developmental biology methodologies are likely to make the great wealth of embryonic gene expression pattern data easily accessible and to accelerate the discovery of developmental networks.     

Gene expression patterns in human liver cancers

Hepatocellular carcinoma (HCC) is a leading cause of death worldwide. Using cDNA microarrays to characterize patterns of gene expression in HCC, we found consistent differences between the expression patterns in HCC compared with those seen in nontumor liver tissues. The expression patterns in HCC were also readily distinguished from those associated with tumors metastatic to liver. The global gene expression patterns intrinsic to each tumor were sufficiently distinctive that multiple tumor nodules from the same patient could usually be recognized and distinguished from all the others in the large sample set on the basis of their gene expression patterns alone. The distinctive gene expression patterns are characteristic of the tumors and not the patient; the expression programs seen in clonally independent tumor nodules in the same patient were no more similar than those in tumors from different patients. Moreover, clonally related tumor masses that showed distinct expression profiles were also distinguished by genotypic differences. Some features of the gene expression patterns were associated with specific phenotypic and genotypic characteristics of the tumors, including growth rate, vascular invasion, and p53 overexpression.     

Cis-transcriptional variation in maize inbred lines B73 and Mo17 leads to additive expression patterns in the F1 hybrid

Microarray analysis of gene expression patterns in immature ear, seedling, and embryo tissues from the maize inbred lines B73 and Mo17 identified numerous genes with variable expression. Some genes had detectable expression in only one of the two inbreds; most of these genes were detected in the genomic DNA of both inbreds, indicating that the expression differences are likely caused by differential regulation rather than by differences in gene content. Gene expression was also monitored in the reciprocal F1 hybrids B73xMo17 and Mo17xB73. The reciprocal F1 hybrid lines did not display parental effects on gene expression levels. Approximately 80% of the differentially expressed genes displayed additive expression patterns in the hybrids relative to the inbred parents. The approximately 20% of genes that display nonadditive expression patterns tend to be expressed at levels within the parental range, with minimal evidence for novel expression levels greater than the high parent or less than the low parent. Analysis of allele-specific expression patterns in the hybrid suggested that intraspecific variation in gene expression levels is largely attributable to cis-regulatory variation in maize. Collectively, our data suggest that allelic cis-regulatory variation between B73 and Mo17 dictates maintenance of inbred allelic expression levels in the F1 hybrid, resulting in additive expression patterns.     

Evolution of yellow gene regulation and pigmentation in Drosophila

BACKGROUND: Changes in developmental gene expression are central to phenotypic evolution, but the genetic mechanisms underlying these changes are not well understood. Interspecific differences in gene expression can arise from evolutionary changes in cis-regulatory DNA and/or in the expression of trans-acting regulatory proteins, but few case studies have distinguished between these mechanisms. Here, we compare the regulation of the yellow gene, which is required for melanization, among distantly related Drosophila species with different pigment patterns and determine the phenotypic effects of divergent Yellow expression. RESULTS: Yellow expression has diverged among D. melanogaster, D. subobscura, and D. virilis and, in all cases, correlates with the distribution of black melanin. Species-specific Yellow expression patterns were retained in D. melanogaster transformants carrying the D. subobscura and D. virilis yellow genes, indicating that sequence evolution within the yellow gene underlies the divergence of Yellow expression. Evolutionary changes in the activity of orthologous cis-regulatory elements are responsible for differences in abdominal Yellow expression; however, cis-regulatory element evolution is not the sole cause of divergent Yellow expression patterns. Transformation of the D. melanogaster yellow gene into D. virilis altered its expression pattern, indicating that trans-acting factors that regulate the D. melanogaster yellow gene have also diverged between these two species. Finally, we found that the phenotypic effects of evolutionary changes in Yellow expression depend on epistatic interactions with other genes. CONCLUSIONS: Evolutionary changes in Yellow expression correlate with divergent melanin patterns and are a result of evolution in both cis- and trans-regulation. These changes were likely necessary for the divergence of pigmentation, but evolutionary changes in other genes were also required.     

Predicting and analyzing early wake-up associated gene expressions by integrating GWAS and eQTL studies

Circadian rhythms are endogenous 24-hour rhythmic oscillations affecting human behaviors, such as sleep, blood pressure and other biological processes, the disturbance of which lead to circadian rhythm sleep disorders (CRSDs). In this study, based on the data from genome-wide association studies (GWASs) and expression quantitative trait loci (eQTLs), we tried to identify novel gene expression patterns in brain tissues that were associated with early wake-up. First, the maximum-relevance-minimum-redundancy (mRMR) method was adopted to analyze the involved gene expression patterns, yielding a feature list. Second, the incremental feature selection (IFS) method and the Dagging algorithm were applied to extract important gene expression patterns, which yield the best performance for Dagging. As a result, 4374 gene expression patterns were obtained, and they were further used to build an optimal classifier with a good performance of a Matthews's correlation coefficient of 0.933. Furthermore, the most important 49 gene expression patterns were extensively analyzed. Four genes were found to be related to circadian rhythm, as reported in previous studies. As a first attempt in identifying the target genes whose expression levels are associated with sleep-wake rhythms through integrating GWAS and eQTL results, this study can motivate more investigations in this regard.This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.     

Knots in the family tree: evolutionary relationships and functions of knox homeobox genes

Knotted-like homeobox (knox) genes constitute a gene family in plants. Class I knox genes are expressed in shoot apical meristems, and (with notable exceptions) not in lateral organ primordia. Class II genes have more diverse expression patterns. Loss and gain of function mutations indicate that knox genes are important regulators of meristem function. Gene duplication has contributed to the evolution of families of homeodomain proteins in metazoans. We believe that similar mechanisms have contributed to the diversity of knox gene function in plants. Knox genes may have contributed to the evolution of compound leaves in tomato and could be involved in the evolution of morphological traits in other species. Alterations in cis-regulatory regions in some knox genes correlate with novel patterns of gene expression and distinctive morphologies. Preliminary data from the analysis of class I knox gene expression illustrates the evolution of complex patterns of knox expression is likely to have occurred through loss and gain of domains of gene expression.     

Hox genes, homology and axis formation—The application of morphological concepts to evolutionary developmental biology

This article focuses on the interphyletic comparison of gene expression patterns. By means of the hypothesis of the inversion of the dorsoventral axis during the evolution of the Bilateria, it is demonstrated, that evolutionary developmental biologists use similarities in spatial and temporal gene expression patterns as evidence for the formulation of hypotheses of homology concerning either developing structures or body regions. The molecular genetic and morphogenetic evidence used is discussed within the framework of a cladistic-phylogenetic analysis based on the phylogenetic tree of the Bilateria. I argue that similarity of spatial and temporal gene expression patterns is not a sufficient criterion for homology inference. Therefore, gene expression patterns should be coded as characters. Their homology should be tested in concert with other characters.

Furthermore, it is demonstrated, that spatial and temporal similar gene expression patterns, indicating similar molecular genetic mechanisms, were interpreted as an analytical criterion of homology, offering the possibility to identify similar structures. In contrast to this, the evolutionary developmental biolgists have not developed a causal-analytically extended concept of shape, from which a causal-analytical concept of homology could be deduced. Instead, the homology concept from evolutionary morphology is used.     

GEPIS--quantitative gene expression profiling in normal and cancer tissues

MOTIVATION: Expression profiling in diverse tissues is fundamental to understanding gene function as well as therapeutic target identification. The vast collection of expressed sequence tags (ESTs) and the associated tissue source information provides an attractive opportunity for studying gene expression. RESULTS: To facilitate EST-based expression analysis, we developed GEPIS (gene expression profiling in silico), a tool that integrates EST and tissue source information to compute gene expression patterns in a large panel of normal and tumor samples. We found EST-based expression patterns to be consistent with published papers as well as our own experimental results. We also built a GEPIS Regional Atlas that depicts expression characteristics of all genes in a selected genomic region. This program can be adapted for large-scale screening for genes with desirable expression patterns, as illustrated by our large-scale mining for tissue- and tumor-specific genes. AVAILABILITY: The email server version of the GEPIS application is freely available at http://share.gene.com/share/gepis. An interactive version of GEPIS will soon be freely available at http://www.cgl.ucsf.edu/Research/genentech/gepis/. The source code, modules, data and gene lists can be downloaded at http://share.gene.com/share/gepis.     

A genome-wide study of DNA methylation patterns and gene expression levels in multiple human and chimpanzee tissues

The modification of DNA by methylation is an important epigenetic mechanism that affects the spatial and temporal regulation of gene expression. Methylation patterns have been described in many contexts within and across a range of species. However, the extent to which changes in methylation might underlie inter-species differences in gene regulation, in particular between humans and other primates, has not yet been studied. To this end, we studied DNA methylation patterns in livers, hearts, and kidneys from multiple humans and chimpanzees, using tissue samples for which genome-wide gene expression data were also available. Using the multi-species gene expression and methylation data for 7,723 genes, we were able to study the role of promoter DNA methylation in the evolution of gene regulation across tissues and species. We found that inter-tissue methylation patterns are often conserved between humans and chimpanzees. However, we also found a large number of gene expression differences between species that might be explained, at least in part, by corresponding differences in methylation levels. In particular, we estimate that, in the tissues we studied, inter-species differences in promoter methylation might underlie as much as 12%-18% of differences in gene expression levels between humans and chimpanzees.     

Variation in gene expression patterns in human gastric cancers

Gastric cancer is the world's second most common cause of cancer death. We analyzed gene expression patterns in 90 primary gastric cancers, 14 metastatic gastric cancers, and 22 nonneoplastic gastric tissues, using cDNA microarrays representing approximately 30,300 genes. Gastric cancers were distinguished from nonneoplastic gastric tissues by characteristic differences in their gene expression patterns. We found a diversity of gene expression patterns in gastric cancer, reflecting variation in intrinsic properties of tumor and normal cells and variation in the cellular composition of these complex tissues. We identified several genes whose expression levels were significantly correlated with patient survival. The variations in gene expression patterns among cancers in different patients suggest differences in pathogenetic pathways and potential therapeutic strategies.