Biomedical subjects

Yueyi Liu

Publications and source records attributed to Yueyi Liu.

5 recordsLinked to original sources

PubMed2005

Chromosomal clustering and GATA transcriptional regulation of intestine-expressed genes in C. elegans.

We used mRNA tagging to identify genes expressed in the intestine of C. elegans. Animals expressing an epitope-tagged protein that binds the poly-A tail of mRNAs (FLAG::PAB-1) from an intestine-specific promoter (ges-1) were used to immunoprecipitate FLAG::PAB-1/mRNA complexes from the intestine. A total of 1938 intestine-expressed genes (P<0.001) were identified using DNA microarrays. First, we compared the intestine-expressed genes with those expressed in the muscle and germline, and identified 510 genes enriched in all three tissues and 624 intestine-, 230 muscle- and 1135 germ line-enriched genes. Second, we showed that the 1938 intestine-expressed genes were physically clustered on the chromosomes, suggesting that the order of genes in the genome is influenced by the effect of chromatin domains on gene expression. Furthermore, the commonly expressed genes showed more chromosomal clustering than the tissue-enriched genes, suggesting that chromatin domains may influence housekeeping genes more than tissue-specific genes. Third, in order to gain further insight into the regulation of intestinal gene expression, we searched for regulatory motifs. This analysis found that the promoters of the intestine genes were enriched for the GATA transcription factor consensus binding sequence. We experimentally verified these results by showing that the GATA motif is required in cis and that GATA transcription factors are required in trans for expression of these intestinal genes.

Animals↗

PubMed2004

A suite of web-based programs to search for transcriptional regulatory motifs.

The identification of regulatory motifs is important for the study of gene expression. Here we present a suite of programs that we have developed to search for regulatory sequence motifs: (i) BioProspector, a Gibbs-sampling-based program for predicting regulatory motifs from co-regulated genes in prokaryotes or lower eukaryotes; (ii) CompareProspector, an extension to BioProspector which incorporates comparative genomics features to be used for higher eukaryotes; (iii) MDscan, a program for finding protein-DNA interaction sites from ChIP-on-chip targets. All three programs examine a group of sequences that may share common regulatory motifs and output a list of putative motifs as position-specific probability matrices, the individual sites used to construct the motifs and the location of each site on the input sequences. The web servers and executables can be accessed at http://seqmotifs.stanford.edu.

Algorithms↗

PubMed2004

Eukaryotic regulatory element conservation analysis and identification using comparative genomics.

Comparative genomics is a promising approach to the challenging problem of eukaryotic regulatory element identification, because functional noncoding sequences may be conserved across species from evolutionary constraints. We systematically analyzed known human and Saccharomyces cerevisiae regulatory elements and discovered that human regulatory elements are more conserved between human and mouse than are background sequences. Although S. cerevisiae regulatory elements do not appear to be more conserved by comparison of S. cerevisiae to Schizosaccharomyces pombe, they are more conserved when compared with multiple other yeast genomes (Saccharomyces paradoxus, Saccharomyces mikatae, and Saccharomyces bayanus). Based on these analyses, we developed a sequence-motif-finding algorithm called CompareProspector, which extends Gibbs sampling by biasing the search in regions conserved across species. Using human-mouse comparison, CompareProspector identified known motifs for transcription factors Mef2, Myf, Srf, and Sp1 from a set of human-muscle-specific genes. It also discovered the NFAT motif from genes up-regulated by CD28 stimulation in T-cells, which implies the direct involvement of NFAT in mediating the CD28 stimulatory signal. Using Caenorhabditis elegans-Caenorhabditis briggsae comparison, CompareProspector found the PHA-4 motif and the UNC-86 motif. CompareProspector outperformed many other computational motif-finding programs, demonstrating the power of comparative genomics-based biased sampling in eukaryotic regulatory element identification.

Algorithms↗

PubMed2003

Identification of promoter regions in the human genome by using a retroviral plasmid library-based functional reporter gene assay.

Attempts to identify regulatory sequences in the human genome have involved experimental and computational methods such as cross-species sequence comparisons and the detection of transcription factor binding-site motifs in coexpressed genes. Although these strategies provide information on which genomic regions are likely to be involved in gene regulation, they do not give information on their functions. We have developed a functional selection for promoter regions in the human genome that uses a retroviral plasmid library-based system. This approach enriches for and detects promoter function of isolated DNA fragments in an in vitro cell culture assay. By using this method, we have discovered likely promoters of known and predicted genes, as well as many other putative promoter regions based on the presence of features such as CpG islands. Comparison of sequences of 858 plasmid clones selected by this assay with the human genome draft sequence indicates that a significantly higher percentage of sequences align to the 500-bp segment upstream of the transcription start sites of known genes than would be expected from random genomic sequences. We also observed enrichment for putative promoter regions of genes predicted in at least two annotation databases and for clones overlapping with CpG islands. Functional validation of randomly selected clones enriched by this method showed that a large fraction of these putative promoters can drive the expression of a reporter gene in transient transfection experiments. This method promises to be a useful genome-wide function-based approach that can complement existing methods to look for promoters.

3T3 Cells↗

PubMed2002

Comparative genomics approaches to study organism similarities and differences.

Comparative genomics is a large-scale, holistic approach that compares two or more genomes to discover the similarities and differences between the genomes and to study the biology of the individual genomes. Comparative studies can be performed at different levels of the genomes to obtain multiple perspectives about the organisms. We discuss in detail the type of analyses that offer significant biological insights in the comparisons of (1) genome structure including overall genome statistics, repeats, genome rearrangement at both DNA and gene level, synteny, and breakpoints; (2) coding regions including gene content, protein content, orthologs, and paralogs; and (3) noncoding regions including the prediction of regulatory elements. We also briefly review the currently available computational tools in comparative genomics such as algorithms for genome-scale sequence alignment, gene identification, and nonhomology-based function prediction.

Animals↗