Siew Loon Ooi

Bio: Siew Loon Ooi is an academic researcher from Fred Hutchinson Cancer Research Center. The author has contributed to research in topics: Gene & Chromatin. The author has an hindex of 15, co-authored 16 publications receiving 6704 citations. Previous affiliations of Siew Loon Ooi include Johns Hopkins University School of Medicine & Johns Hopkins University.

Functional profiling of the Saccharomyces cerevisiae genome.

Abstract: Determining the effect of gene deletion is a fundamental approach to understanding gene function. Conventional genetic screens exhibit biases, and genes contributing to a phenotype are often missed. We systematically constructed a nearly complete collection of gene-deletion mutants (96% of annotated open reading frames, or ORFs) of the yeast Saccharomyces cerevisiae. DNA sequences dubbed 'molecular bar codes' uniquely identify each strain, enabling their growth to be analysed in parallel and the fitness contribution of each gene to be quantitatively assessed by hybridization to high-density oligonucleotide arrays. We show that previously known and new genes are necessary for optimal growth under six well-studied conditions: high salt, sorbitol, galactose, pH 8, minimal medium and nystatin treatment. Less than 7% of genes that exhibit a significant increase in messenger RNA expression are also required for optimal growth in four of the tested conditions. Our results validate the yeast gene-deletion collection as a valuable resource for functional genomics.

Integrative analysis of the Caenorhabditis elegans genome by the modENCODE project

Abstract: We systematically generated large-scale data sets to improve genome annotation for the nematode Caenorhabditis elegans, a key model organism. These data sets include transcriptome profiling across a developmental time course, genome-wide identification of transcription factor-binding sites, and maps of chromatin organization. From this, we created more complete and accurate gene models, including alternative splice forms and candidate noncoding RNAs. We constructed hierarchical networks of transcription factor-binding and microRNA interactions and discovered chromosomal locations bound by an unusually large number of transcription factors. Different patterns of chromatin composition and histone modification were revealed between chromosome arms and centers, with similarly prominent differences between autosomes and the X chromosome. Integrating data types, we built statistical models relating chromatin, transcription factor binding, and gene expression. Overall, our analyses ascribed putative functions to most of the conserved genome.

Automated cell lineage tracing in Caenorhabditis elegans

Abstract: The invariant cell lineage and cell fate of Caenorhabditis elegans provide a unique opportunity to decode the molecular mechanisms of animal development. To exploit this opportunity, we have developed a system for automated cell lineage tracing during C. elegans embryogenesis, based on 3D, time-lapse imaging and automated image analysis. Using ubiquitously expressed histone–GFP fusion protein to label cells/nuclei and a confocal microscope, the imaging protocol captures embryogenesis at high spatial (31 planes at 1 μm apart) and temporal (every minute) resolution without apparent effects on development. A set of image analysis algorithms then automatically recognizes cells at each time point, tracks cell movements, divisions and deaths over time and assigns cell identities based on the canonical naming scheme. Starting from the four-cell stage (or earlier), our software, named starrynite, can trace the lineage up to the 350-cell stage in 25 min on a desktop computer. The few errors of automated lineaging can then be corrected in a few hours with a graphic interface that allows easy navigation of the images and the reported lineage tree. The system can be used to characterize lineage phenotypes of genes and/or extended to determine gene expression patterns in a living embryo at the single-cell level. We envision that this automation will make it practical to systematically decipher the developmental genes and pathways encoded in the genome of C. elegans.

Disparities in breast cancer characteristics and outcomes by race/ethnicity

Abstract: Disparities in breast cancer stage and mortality by race/ethnicity in the United States are persistent and well known. However, few studies have assessed differences across racial/ethnic subgroups of women broadly defined as Hispanic, Asian, or Pacific Islander, particularly using more recent data. Using data from 17 population-based cancer registries in the Surveillance, Epidemiology, and End Results (SEER) program, we evaluated the relationships between race/ethnicity and breast cancer stage, hormone receptor status, treatment, and mortality. The cohort consisted of 229,594 women 40–79 years of age diagnosed with invasive breast carcinoma between January 2000 and December 2006, including 176,094 non-Hispanic whites, 20,486 Blacks, 15,835 Hispanic whites, 14,951 Asians, 1,224 Pacific Islanders, and 1,004 American Indians/Alaska Natives. With respect to statistically significant findings, American Indian/Alaska Native, Asian Indian/Pakistani, Black, Filipino, Hawaiian, Mexican, Puerto Rican, and Samoan women had 1.3–7.1-fold higher odds of presenting with stage IV breast cancer compared to non-Hispanic white women. Almost all groups were more likely to be diagnosed with estrogen receptor-negative/progesterone receptor-negative (ER−/PR−) disease with Black and Puerto Rican women having the highest odds ratios (2.4 and 1.9-fold increases, respectively) compared to non-Hispanic whites. Lastly, Black, Hawaiian, Puerto Rican, and Samoan patients had 1.5–1.8-fold elevated risks of breast cancer-specific mortality. Breast cancer disparities persist by race/ethnicity, though there is substantial variation within subgroups of women broadly defined as Hispanic or Asian. Targeted, multi-pronged interventions that are culturally appropriate may be important means of reducing the magnitudes of these disparities.

DNA helicase gene interaction network defined using synthetic lethality analyzed by microarray.

Abstract: We describe a new synthetic lethality analysis by microarray (SLAM) technique that uses approximately 4,600 Saccharomyces cerevisiae haploid deletion mutants with molecular 'bar codes' (TAGs). We used SGS1 and SRS2, two 3'-->5' DNA helicase genes, as 'queries' to identify their redundant and unique biological functions. We introduced these 'query mutations' into a haploid deletion pool by integrative transformation to disrupt the query gene in every cell, generating a double mutant pool. Optimization of integrative transformation efficiency was essential to the success of SLAM. Synthetic interactions defined a DNA helicase genetic network and predicted a role for SRS2 in processing damaged replication forks but, unlike SGS1, not in rDNA replication, DNA topology or lagging strand synthesis. SGS1 and SRS2 have synthetic defects with MRC1 but not RAD9, suggesting that SGS1 and SRS2 function in a parallel pathway with MRC1 to transduce the DNA replication stress signal to the general DNA damage checkpoint pathway. Both helicase genes have rad51-reversible synthetic defects with 5'-->3' DNA helicase RRM3, suggesting that RRM3 helps prevent formation of toxic recombination intermediates. SLAM detects synthetic lethality efficiently and ranks candidate genetic interactions, making it an especially useful method.

Fiji: an open-source platform for biological-image analysis

Abstract: Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.

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。

Network biology: understanding the cell's functional organization

Abstract: A key aim of postgenomic biomedical research is to systematically catalogue all molecules and their interactions within a living cell. There is a clear need to understand how these molecules and the interactions between them determine the function of this enormously complex machinery, both in isolation and when surrounded by other cells. Rapid advances in network biology indicate that cellular networks are governed by universal laws and offer a new conceptual framework that could potentially revolutionize our view of biology and disease pathologies in the twenty-first century.

Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection.

Abstract: We have systematically made a set of precisely defined, single-gene deletions of all nonessential genes in Escherichia coli K-12. Open-reading frame coding regions were replaced with a kanamycin cassette flanked by FLP recognition target sites by using a one-step method for inactivation of chromosomal genes and primers designed to create in-frame deletions upon excision of the resistance cassette. Of 4288 genes targeted, mutants were obtained for 3985. To alleviate problems encountered in high-throughput studies, two independent mutants were saved for every deleted gene. These mutants-the 'Keio collection'-provide a new resource not only for systematic analyses of unknown gene functions and gene regulatory networks but also for genome-wide testing of mutational effects in a common strain background, E. coli K-12 BW25113. We were unable to disrupt 303 genes, including 37 of unknown function, which are candidates for essential genes. Distribution is being handled via GenoBase (http://ecoli.aist-nara.ac.jp/).

Protein Misfolding, Functional Amyloid, and Human Disease

Abstract: Peptides or proteins convert under some conditions from their soluble forms into highly ordered fibrillar aggregates. Such transitions can give rise to pathological conditions ranging from neurodegenerative disorders to systemic amyloidoses. In this review, we identify the diseases known to be associated with formation of fibrillar aggregates and the specific peptides and proteins involved in each case. We describe, in addition, that living organisms can take advantage of the inherent ability of proteins to form such structures to generate novel and diverse biological functions. We review recent advances toward the elucidation of the structures of amyloid fibrils and the mechanisms of their formation at a molecular level. Finally, we discuss the relative importance of the common main-chain and side-chain interactions in determining the propensities of proteins to aggregate and describe some of the evidence that the oligomeric fibril precursors are the primary origins of pathological behavior.