Robert Shoemaker

Targeted bisulfite sequencing reveals changes in DNA methylation associated with nuclear reprogramming

Current DNA methylation assays are limited in the flexibility and efficiency of characterizing a large number of genomic targets. We report a method to specifically capture an arbitrary subset of genomic targets for single-molecule bisulfite sequencing for digital quantification of DNA methylation at single-nucleotide resolution. A set of ~30,000 padlock probes was designed to assess methylation of ~66,000 CpG sites within 2,020 CpG islands on human chromosome 12, chromosome 20, and 34 selected regions. To investigate epigenetic differences associated with dedifferentiation, we compared methylation in three human fibroblast lines and eight human pluripotent stem cell lines. Chromosome-wide methylation patterns were similar among all lines studied, but cytosine methylation was slightly more prevalent in the pluripotent cells than in the fibroblasts. Induced pluripotent stem (iPS) cells appeared to display more methylation than embryonic stem cells. We found 288 regions methylated differently in fibroblasts and pluripotent cells. This targeted approach should be particularly useful for analyzing DNA methylation in large genomes.

Allele-specific methylation is prevalent and is contributed by CpG-SNPs in the human genome

In diploid mammalian genomes, parental alleles can exhibit different methylation patterns (allele-specific DNA methylation, ASM), which have been documented in a small number of cases except for the imprinted regions and X chromosomes in females. We carried out a chromosome-wide survey of ASM across 16 human pluripotent and adult cell lines using Illumina bisulfite sequencing. We applied the principle of linkage disequilibrium (LD) analysis to characterize the correlation of methylation between adjacent CpG sites on single DNA molecules, and also investigated the correlation between CpG methylation and single nucleotide polymorphisms (SNPs). We observed ASM on 23% approximately 37% heterozygous SNPs in any given cell line. ASM is often cell-type-specific. Furthermore, we found that a significant fraction (38%-88%) of ASM regions is dependent on the presence of heterozygous SNPs in CpG dinucleotides that disrupt their methylation potential. This study identified distinct types of ASM across many cell types and suggests a potential role for CpG-SNP in connecting genetic variation with the epigenome.

Library-free methylation sequencing with bisulfite padlock probes

Targeted quantification of DNA methylation allows for interrogation of the most informative loci across many samples quickly and cost-effectively. Here we report improved bisulfite padlock probes (BSPPs) with a design algorithm to generate efficient padlock probes, a library-free protocol that dramatically reduces sample-preparation cost and time and is compatible with automation, and an efficient bioinformatics pipeline to accurately obtain both methylation levels and genotypes from sequencing of bisulfite-converted DNA.

Systematic search for recipes to generate induced pluripotent stem cells.

Generation of induced pluripotent stem cells (iPSCs) opens a new avenue in regenerative medicine. One of the major hurdles for therapeutic applications is to improve the efficiency of generating iPSCs and also to avoid the tumorigenicity, which requires searching for new reprogramming recipes. We present a systems biology approach to efficiently evaluate a large number of possible recipes and find those that are most effective at generating iPSCs. We not only recovered several experimentally confirmed recipes but we also suggested new ones that may improve reprogramming efficiency and quality. In addition, our approach allows one to estimate the cell-state landscape, monitor the progress of reprogramming, identify important regulatory transition states, and ultimately understand the mechanisms of iPSC generation.

A Novel Knowledge-Driven Systems Biology Approach for Phenotype Prediction upon Genetic Intervention

Deciphering the biological networks underlying complex phenotypic traits, e.g., human disease is undoubtedly crucial to understand the underlying molecular mechanisms and to develop effective therapeutics. Due to the network complexity and the relatively small number of available experiments, data-driven modeling is a great challenge for deducing the functions of genes/proteins in the network and in phenotype formation. We propose a novel knowledge-driven systems biology method that utilizes qualitative knowledge to construct a Dynamic Bayesian network (DBN) to represent the biological network underlying a specific phenotype. Edges in this network depict physical interactions between genes and/or proteins. A qualitative knowledge model first translates typical molecular interactions into constraints when resolving the DBN structure and parameters. Therefore, the uncertainty of the network is restricted to a subset of models which are consistent with the qualitative knowledge. All models satisfying the constraints are considered as candidates for the underlying network. These consistent models are used to perform quantitative inference. By in silico inference, we can predict phenotypic traits upon genetic interventions and perturbing in the network. We applied our method to analyze the puzzling mechanism of breast cancer cell proliferation network and we accurately predicted cancer cell growth rate upon manipulating (anti)cancerous marker genes/proteins.

An Integrated Approach to Identifying Cis-Regulatory Modules in the Human Genome

In eukaryotic genomes, it is challenging to accurately determine target sites of transcription factors (TFs) by only using sequence information. Previous efforts were made to tackle this task by considering the fact that TF binding sites tend to be more conserved than other functional sites and the binding sites of several TFs are often clustered. Recently, ChIP-chip and ChIP-sequencing experiments have been accumulated to identify TF binding sites as well as survey the chromatin modification patterns at the regulatory elements such as promoters and enhancers. We propose here a hidden Markov model (HMM) to incorporate sequence motif information, TF-DNA interaction data and chromatin modification patterns to precisely identify cis-regulatory modules (CRMs). We conducted ChIP-chip experiments on four TFs, CREB, E2F1, MAX, and YY1 in 1% of the human genome. We then trained a hidden Markov model (HMM) to identify the labels of the CRMs by incorporating the sequence motifs recognized by these TFs and the ChIP-chip ratio. Chromatin modification data was used to predict the functional sites and to further remove false positives. Cross-validation showed that our integrated HMM had a performance superior to other existing methods on predicting CRMs. Incorporating histone signature information successfully penalized false prediction and improved the whole performance. The dataset we used and the software are available at http://nash.ucsd.edu/CIS/.

Mediators and dynamics of DNA methylation

As an inherited epigenetic marker occurring mainly on cytosines at CpG dinucleotides, DNA methylation occurs across many higher eukaryotic organisms. Looking at methylation patterns genome‐wide classifies cell types uniquely and in several cases discriminates between healthy and cancerous cell types. DNA methylation can occur allele‐specifically, which allows the cellular regulatory machinery to recognize each allele separately. Although only a small number of allele specifically methylated (ASM) regions are known, genome‐wide experiments show that ASM is prevalent throughout the human genome. These DNA methylation patterns can be modified via DNA demethylation, which is important for induced pluripotent stem reprogramming and primordial germ cells. Recent evidence shows that the protein activation‐induced cytidine deaminase plays a critical role in these demethylation events. Many transcription factors mediate DNA methylation patterns. Some transcription factors bind specifically to methylated or unmethylated sequences and other transcription factors protect genomic regions (e.g., promoter regions) from nearby DNA methylation encroachment. Possibly acting as another epigenetic regulatory layer, methylated cytosines are also converted to 5‐hydroxyethylcyotines, which is a new modification type whose biological significance has yet been defined. WIREs Syst Biol Med 2011 3 281–298 DOI: 10.1002/wsbm.124

Global identification of transcriptional regulators of pluripotency and differentiation in embryonic stem cells

Human embryonic stem cells (hESCs) hold great promise for regenerative medicine because they can undergo unlimited self-renewal and retain the capability to differentiate into all cell types in the body. Although numerous genes/proteins such as Oct4 and Gata6 have been identified to play critical regulatory roles in self-renewal and differentiation of hESC, the majority of the regulators in these cellular processes and more importantly how these regulators co-operate with each other and/or with epigenetic modifications are still largely unknown. We propose here a systematic approach to integrate genomic and epigenomic data for identification of direct regulatory interactions. This approach allows reconstruction of cell-type-specific transcription networks in embryonic stem cells (ESCs) and fibroblasts at an unprecedented scale. Many links in the reconstructed networks coincide with known regulatory interactions or literature evidence. Systems-level analyses of these networks not only uncover novel regulators for pluripotency and differentiation, but also reveal extensive interplays between transcription factor binding and epigenetic modifications. Especially, we observed poised enhancers characterized by both active (H3K4me1) and repressive (H3K27me3) histone marks that contain enriched Oct4- and Suz12-binding sites. The success of such a systems biology approach is further supported by experimental validation of the predicted interactions.

Vitamin A in Pregnancy

Abstract The identity, chemistry, physiology, and pathology of vitamin A have been well studied and numerous reports are found in the literature, but only in recent years (1934) have blood assay and photometric methods of determining vitamin A capacity been generally available. Since that time, the vitamin A levels of numerous groups of patients have been studied, although mostly under variable methods and conditions, not including pregnancy. It has been our purpose in this study to attempt to establish via photometric tests the average vitamin A capacity through pregnancy, and the frequency, degree and significance of deviations from this “normal.”