Eric H. Davidson

Bio: Eric H. Davidson is an academic researcher from California Institute of Technology. The author has contributed to research in topics: Strongylocentrotus purpuratus & Gene regulatory network. The author has an hindex of 106, co-authored 454 publications receiving 47058 citations. Previous affiliations of Eric H. Davidson include Carnegie Institution for Science & Institute for Systems Biology.

Conservation of the sequence and temporal expression of let-7 heterochronic regulatory RNA

Abstract: Two small RNAs regulate the timing of Caenorhabditis elegans development. Transition from the first to the second larval stage fates requires the 22-nucleotide lin-4 RNA and transition from late larval to adult cell fates requires the 21-nucleotide let-7 RNA. The lin-4 and let-7 RNA genes are not homologous to each other, but are each complementary to sequences in the 3' untranslated regions of a set of protein-coding target genes that are normally negatively regulated by the RNAs. Here we have detected let-7 RNAs of ~21 nucleotides in samples from a wide range of animal species, including vertebrate, ascidian, hemichordate, mollusc, annelid and arthropod, but not in RNAs from several cnidarian and poriferan species, Saccharomyces cerevisiae, Escherichia coli or Arabidopsis. We did not detect lin-4 RNA in these species. We found that let-7 temporal regulation is also conserved: let-7 RNA expression is first detected at late larval stages in C. elegans and Drosophila , at 48 hours after fertilization in zebrafish, and in adult stages of annelids and molluscs. The let-7 regulatory RNA may control late temporal transitions during development across animal phylogeny.

Direct multiplexed measurement of gene expression with color-coded probe pairs.

Abstract: We describe a technology, the NanoString nCounter gene expression system, which captures and counts individual mRNA transcripts. Advantages over existing platforms include direct measurement of mRNA expression levels without enzymatic reactions or bias, sensitivity coupled with high multiplex capability, and digital readout. Experiments performed on 509 human genes yielded a replicate correlation coefficient of 0.999, a detection limit between 0.1 fM and 0.5 fM, and a linear dynamic range of over 500-fold. Comparison of the NanoString nCounter gene expression system with microarrays and TaqMan PCR demonstrated that the nCounter system is more sensitive than microarrays and similar in sensitivity to real-time PCR. Finally, a comparison of transcript levels for 21 genes across seven samples measured by the nCounter system and SYBR Green real-time PCR demonstrated similar patterns of gene expression at all transcript levels.

Gene Regulation for Higher Cells: A Theory

Abstract: Cell differentiation is based almost certainly on the regulation of gene activity, so that for each state of differentiation a certain set of genes is active in transcription and other genes are inactive. The establishment of this concept (1) has depended on evidence indicating that the cells of an organism generally contain identical genomes (2). Direct support for the idea that regulation of gene activity underlies cell differentiation comes from evidence that much of the genome in higher cell types is inactive (3) and that different ribonucleic acids (RNA) are synthesized in different cell types (4).

A Genomic Regulatory Network for Development

Abstract: Development of the body plan is controlled by large networks of regulatory genes. A gene regulatory network that controls the specification of endoderm and mesoderm in the sea urchin embryo is summarized here. The network was derived from large-scale perturbation analyses, in combination with computational methodologies, genomic data, cis-regulatory analysis, and molecular embryology. The network contains over 40 genes at present, and each node can be directly verified at the DNA sequence level by cis-regulatory analysis. Its architecture reveals specific and general aspects of development, such as how given cells generate their ordained fates in the embryo and why the process moves inexorably forward in developmental time.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Mapping and quantifying mammalian transcriptomes by RNA-Seq.

Abstract: We have mapped and quantified mouse transcriptomes by deeply sequencing them and recording how frequently each gene is represented in the sequence sample (RNA-Seq). This provides a digital measure of the presence and prevalence of transcripts from known and previously unknown genes. We report reference measurements composed of 41–52 million mapped 25-base-pair reads for poly(A)-selected RNA from adult mouse brain, liver and skeletal muscle tissues. We used RNA standards to quantify transcript prevalence and to test the linear range of transcript detection, which spanned five orders of magnitude. Although >90% of uniquely mapped reads fell within known exons, the remaining data suggest new and revised gene models, including changed or additional promoters, exons and 3′ untranscribed regions, as well as new candidate microRNA precursors. RNA splice events, which are not readily measured by standard gene expression microarray or serial analysis of gene expression methods, were detected directly by mapping splice-crossing sequence reads. We observed 1.45 × 10 5 distinct splices, and alternative splices were prominent, with 3,500 different genes expressing one or more alternate internal splices. The mRNA population specifies a cell’s identity and helps to govern its present and future activities. This has made transcriptome analysis a general phenotyping method, with expression microarrays of many kinds in routine use. Here we explore the possibility that transcriptome analysis, transcript discovery and transcript refinement can be done effectively in large and complex mammalian genomes by ultra-high-throughput sequencing. Expression microarrays are currently the most widely used methodology for transcriptome analysis, although some limitations persist. These include hybridization and cross-hybridization artifacts 1–3 , dye-based detection issues and design constraints that preclude or seriously limit the detection of RNA splice patterns and previously unmapped genes. These issues have made it difficult for standard array designs to provide full sequence comprehensiveness (coverage of all possible genes, including unknown ones, in large genomes) or transcriptome comprehensiveness (reliable detection of all RNAs of all prevalence classes, including the least abundant ones that are physiologically relevant). Other