Showing papers by "Cheng Li published in 2008"

Highly parallel identification of essential genes in cancer cells

Abstract: More complete knowledge of the molecular mechanisms underlying cancer will improve prevention, diagnosis and treatment. Efforts such as The Cancer Genome Atlas are systematically characterizing the structural basis of cancer, by identifying the genomic mutations associated with each cancer type. A powerful complementary approach is to systematically characterize the functional basis of cancer, by identifying the genes essential for growth and related phenotypes in different cancer cells. Such information would be particularly valuable for identifying potential drug targets. Here, we report the development of an efficient, robust approach to perform genome-scale pooled shRNA screens for both positive and negative selection and its application to systematically identify cell essential genes in 12 cancer cell lines. By integrating these functional data with comprehensive genetic analyses of primary human tumors, we identified known and putative oncogenes such as EGFR, KRAS, MYC, BCR-ABL, MYB, CRKL, and CDK4 that are essential for cancer cell proliferation and also altered in human cancers. We further used this approach to identify genes involved in the response of cancer cells to tumoricidal agents and found 4 genes required for the response of CML cells to imatinib treatment: PTPN1, NF1, SMARCB1, and SMARCE1, and 5 regulators of the response to FAS activation, FAS, FADD, CASP8, ARID1A and CBX1. Broad application of this highly parallel genetic screening strategy will not only facilitate the rapid identification of genes that drive the malignant state and its response to therapeutics but will also enable the discovery of genes that participate in any biological process.

Evolution of the Aging Brain Transcriptome and Synaptic Regulation

Abstract: Alzheimer's disease and other neurodegenerative disorders of aging are characterized by clinical and pathological features that are relatively specific to humans. To obtain greater insight into how brain aging has evolved, we compared age-related gene expression changes in the cortex of humans, rhesus macaques, and mice on a genome-wide scale. A small subset of gene expression changes are conserved in all three species, including robust age-dependent upregulation of the neuroprotective gene apolipoprotein D (APOD) and downregulation of the synaptic cAMP signaling gene calcium/calmodulin-dependent protein kinase IV (CAMK4). However, analysis of gene ontology and cell type localization shows that humans and rhesus macaques have diverged from mice due to a dramatic increase in age-dependent repression of neuronal genes. Many of these age-regulated neuronal genes are associated with synaptic function. Notably, genes associated with GABA-ergic inhibitory function are robustly age-downregulated in humans but not in mice at the level of both mRNA and protein. Gene downregulation was not associated with overall neuronal or synaptic loss. Thus, repression of neuronal gene expression is a prominent and recently evolved feature of brain aging in humans and rhesus macaques that may alter neural networks and contribute to age-related cognitive changes.

Automating dChip: toward reproducible sharing of microarray data analysis

Abstract: Background: During the past decade, many software packages have been developed for analysis and visualization of various types of microarrays. We have developed and maintained the widely used dChip as a microarray analysis software package accessible to both biologist and data analysts. However, challenges arise when dChip users want to analyze large number of arrays automatically and share data analysis procedures and parameters. Improvement is also needed when the dChip user support team tries to identify the causes of reported analysis errors or bugs from users. Results: We report here implementation and application of the dChip automation module. Through this module, dChip automation files can be created to include menu steps, parameters, and data viewpoints to run automatically. A data-packaging function allows convenient transfer from one user to another of the dChip software, microarray data, and analysis procedures, so that the second user can reproduce the entire analysis session of the first user. An analysis report file can also be generated during an automated run, including analysis logs, user comments, and viewpoint screenshots. Conclusion: The dChip automation module is a step toward reproducible research, and it can prompt a more convenient and reproducible mechanism for sharing microarray software, data, and analysis procedures and results. Automation data packages can also be used as publication supplements. Similar automation mechanisms could be valuable to the research community if implemented in other genomics and bioinformatics software packages.

Major copy proportion analysis of tumor samples using SNP arrays

Abstract: Single nucleotide polymorphisms (SNPs) are the most common genetic variations in the human genome and are useful as genomic markers. Oligonucleotide SNP microarrays have been developed for high-throughput genotyping of up to 900,000 human SNPs and have been used widely in linkage and cancer genomics studies. We have previously used Hidden Markov Models (HMM) to analyze SNP array data for inferring copy numbers and loss-of-heterozygosity (LOH) from paired normal and tumor samples and unpaired tumor samples. We proposed and implemented major copy proportion (MCP) analysis of oligonucleotide SNP array data. A HMM was constructed to infer unobserved MCP states from observed allele-specific signals through emission and transition distributions. We used 10 K, 100 K and 250 K SNP array datasets to compare MCP analysis with LOH and copy number analysis, and showed that MCP performs better than LOH analysis for allelic-imbalanced chromosome regions and normal contaminated samples. The major and minor copy alleles can also be inferred from allelic-imbalanced regions by MCP analysis. MCP extends tumor LOH analysis to allelic imbalance analysis and supplies complementary information to total copy numbers. MCP analysis of mixing normal and tumor samples suggests the utility of MCP analysis of normal-contaminated tumor samples. The described analysis and visualization methods are readily available in the user-friendly dChip software.

Use of high-density SNP-array analysis to identify novel chromosomal abnormalities that predict survival in multiple myeloma

Abstract: 8522 Background: Despite major improvements in the treatment of myeloma over the last decade, disease course is variable due to genetic heterogeneity. Even though cytogenetics is a difficult art in...