Author
Bing Lim
Other affiliations: Merck & Co., Agency for Science, Technology and Research, Massachusetts Institute of Technology ...read more
Bio: Bing Lim is an academic researcher from Genome Institute of Singapore. The author has contributed to research in topics: Stem cell & Embryonic stem cell. The author has an hindex of 70, co-authored 157 publications receiving 23132 citations. Previous affiliations of Bing Lim include Merck & Co. & Agency for Science, Technology and Research.
Papers published on a yearly basis
Papers
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TL;DR: By integrating RNA interference–mediated depletion of Oct4 and Nanog with microarray expression profiling, it is demonstrated that these factors can activate or suppress transcription, and it is shown that common core downstream targets are important to keep ES cells from differentiating.
Abstract: Oct4 and Nanog are transcription factors required to maintain the pluripotency and self-renewal of embryonic stem (ES) cells. Using the chromatin immunoprecipitation paired-end ditags method, we mapped the binding sites of these factors in the mouse ES cell genome. We identified 1,083 and 3,006 high-confidence binding sites for Oct4 and Nanog, respectively. Comparative location analyses indicated that Oct4 and Nanog overlap substantially in their targets, and they are bound to genes in different configurations. Using de novo motif discovery algorithms, we defined the cis-acting elements mediating their respective binding to genomic sites. By integrating RNA interference-mediated depletion of Oct4 and Nanog with microarray expression profiling, we demonstrated that these factors can activate or suppress transcription. We further showed that common core downstream targets are important to keep ES cells from differentiating. The emerging picture is one in which Oct4 and Nanog control a cascade of pathways that are intricately connected to govern pluripotency, self-renewal, genome surveillance and cell fate determination.
2,489 citations
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TL;DR: Rna22 as discussed by the authors identifies microRNA binding sites and their corresponding heteroduplexes, and then identifies the targeting microRNAs by finding putative microRN binding sites in the sequence of interest.
1,888 citations
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TL;DR: The findings demonstrate the abundance of CDS-located miRNA targets, some of which can be species-specific, and support an augmented model whereby animal miRNAs exercise their control on mRNAs through targets that can reside beyond the 3′ untranslated region.
Abstract: MicroRNAs (miRNAs) are short RNAs that direct messenger RNA degradation or disrupt mRNA translation in a sequence-dependent manner. For more than a decade, attempts to study the interaction of miRNAs with their targets were confined to the 3' untranslated regions of mRNAs, fuelling an underlying assumption that these regions are the principal recipients of miRNA activity. Here we focus on the mouse Nanog, Oct4 (also known as Pou5f1) and Sox2 genes and demonstrate the existence of many naturally occurring miRNA targets in their amino acid coding sequence (CDS). Some of the mouse targets analysed do not contain the miRNA seed, whereas others span exon-exon junctions or are not conserved in the human and rhesus genomes. miR-134, miR-296 and miR-470, upregulated on retinoic-acid-induced differentiation of mouse embryonic stem cells, target the CDS of each transcription factor in various combinations, leading to transcriptional and morphological changes characteristic of differentiating mouse embryonic stem cells, and resulting in a new phenotype. Silent mutations at the predicted targets abolish miRNA activity, prevent the downregulation of the corresponding genes and delay the induced phenotype. Our findings demonstrate the abundance of CDS-located miRNA targets, some of which can be species-specific, and support an augmented model whereby animal miRNAs exercise their control on mRNAs through targets that can reside beyond the 3' untranslated region.
1,329 citations
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TL;DR: A robust approach is described that couples chromatin immunoprecipitation (ChIP) with the paired-end ditag (PET) sequencing strategy for unbiased and precise global localization of transcription-factor binding sites (TFBS).
1,180 citations
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TL;DR: A positive and potentially self-reinforcing regulatory loop that maintains Pou5f1 and Sox2 expression via the Oct4/Sox2 complex in pluripotent cells is uncovered.
Abstract: Embryonic stem cells (ESCs) are pluripotent cells that can either self-renew or differentiate into many cell types. Oct4 and Sox2 are transcription factors essential to the pluripotent and self-renewing phenotypes of ESCs. Both factors are upstream in the hierarchy of the transcription regulatory network and are partners in regulating several ESC-specific genes. In ESCs, Sox2 is transcriptionally regulated by an enhancer containing a composite sox-oct element that Oct4 and Sox2 bind in a combinatorial interaction. It has previously been shown that Pou5f1, the Oct4 gene, contains a distal enhancer imparting specific expression in both ESCs and preimplantation embryos. Here, we identify a composite sox-oct element within this enhancer and show that it is involved in Pou5f1 transcriptional activity in ESCs. In vitro experiments with ESC nuclear extracts demonstrate that Oct4 and Sox2 interact specifically with this regulatory element. More importantly, by chromatin immunoprecipitation assay, we establish that both Oct4 and Sox2 bind directly to the composite sox-oct elements in both Pou5f1 and Sox2 in living mouse and human ESCs. Specific knockdown of either Oct4 or Sox2 by RNA interference leads to the reduction of both genes' enhancer activities and endogenous expression levels in addition to ESC differentiation. Our data uncover a positive and potentially self-reinforcing regulatory loop that maintains Pou5f1 and Sox2 expression via the Oct4/Sox2 complex in pluripotent cells.
710 citations
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TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
34,830 citations
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TL;DR: Induction of pluripotent stem cells from mouse embryonic or adult fibroblasts by introducing four factors, Oct3/4, Sox2, c-Myc, and Klf4, under ES cell culture conditions is demonstrated and iPS cells, designated iPS, exhibit the morphology and growth properties of ES cells and express ES cell marker genes.
23,959 citations
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TL;DR: It is demonstrated that iPS cells can be generated from adult human fibroblasts with the same four factors: Oct3/4, Sox2, Klf4, and c-Myc.
18,175 citations
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TL;DR: The current understanding of miRNA target recognition in animals is outlined and the widespread impact of miRNAs on both the expression and evolution of protein-coding genes is discussed.
18,036 citations
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TL;DR: This work presents Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer, and uses a dynamic Poisson distribution to effectively capture local biases in the genome, allowing for more robust predictions.
Abstract: We present Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer. MACS empirically models the shift size of ChIP-Seq tags, and uses it to improve the spatial resolution of predicted binding sites. MACS also uses a dynamic Poisson distribution to effectively capture local biases in the genome, allowing for more robust predictions. MACS compares favorably to existing ChIP-Seq peak-finding algorithms, and is freely available.
13,008 citations