Biomedical subjects

Peter S Linsley

Publications and source records attributed to Peter S Linsley.

5 recordsLinked to original sources

PubMed2003

Expression profiling reveals off-target gene regulation by RNAi.

RNA interference is thought to require near-identity between the small interfering RNA (siRNA) and its cognate mRNA. Here, we used gene expression profiling to characterize the specificity of gene silencing by siRNAs in cultured human cells. Transcript profiles revealed siRNA-specific rather than target-specific signatures, including direct silencing of nontargeted genes containing as few as eleven contiguous nucleotides of identity to the siRNA. These results demonstrate that siRNAs may cross-react with targets of limited sequence similarity.

Base Sequence↗

PubMed2003

Genetics of gene expression surveyed in maize, mouse and man.

Treating messenger RNA transcript abundances as quantitative traits and mapping gene expression quantitative trait loci for these traits has been pursued in gene-specific ways. Transcript abundances often serve as a surrogate for classical quantitative traits in that the levels of expression are significantly correlated with the classical traits across members of a segregating population. The correlation structure between transcript abundances and classical traits has been used to identify susceptibility loci for complex diseases such as diabetes and allergic asthma. One study recently completed the first comprehensive dissection of transcriptional regulation in budding yeast, giving a detailed glimpse of a genome-wide survey of the genetics of gene expression. Unlike classical quantitative traits, which often represent gross clinical measurements that may be far removed from the biological processes giving rise to them, the genetic linkages associated with transcript abundance affords a closer look at cellular biochemical processes. Here we describe comprehensive genetic screens of mouse, plant and human transcriptomes by considering gene expression values as quantitative traits. We identify a gene expression pattern strongly associated with obesity in a murine cross, and observe two distinct obesity subtypes. Furthermore, we find that these obesity subtypes are under the control of different loci.

Animals↗

PubMed2002

Modulation of TCR-induced transcriptional profiles by ligation of CD28, ICOS, and CTLA-4 receptors.

Signals generated by T cell receptor (TCR) and CD28 engagement are required for optimal T cell activation, but how these signals integrate within the cell is still largely unknown. We have used near genome-scale expression profiling to monitor T cell signal transduction pathways triggered via TCR and/or costimulatory receptors. Ligation of CD28 alone induced a set of short-lived early response transcripts in both Jurkat T cells and primary CD4 T cells, thus providing evidence that CD28 engagement can affect gene regulation independently of TCR engagement. Simultaneous signaling through both the TCR and CD28 resulted in altered expression of several thousand genes following several distinct temporal patterns. Most of these gene regulations were induced by TCR signaling alone and were augmented to varying degrees by CD28 costimulation. CD28 and ICOS costimulation had nearly identical effects on gene regulation, but a few transcripts (e.g., IL2, IL9) were significantly more affected by CD28. Therefore, the distinctive functional outcomes of costimulation via CD28 and ICOS are accompanied by relatively few distinct differences in gene expression. Cytotoxic T lymphocyte antigen 4 (CTLA-4) engagement selectively blocked augmentation of gene regulations by CD28-mediated costimulation, but did not ablate gene regulation induced by TCR triggering alone.

Abatacept↗

PubMed2002

Gene expression profiling predicts clinical outcome of breast cancer.

Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumours according to their clinical behaviour. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70-80% of patients receiving this treatment would have survived without it. None of the signatures of breast cancer gene expression reported to date allow for patient-tailored therapy strategies. Here we used DNA microarray analysis on primary breast tumours of 117 young patients, and applied supervised classification to identify a gene expression signature strongly predictive of a short interval to distant metastases ('poor prognosis' signature) in patients without tumour cells in local lymph nodes at diagnosis (lymph node negative). In addition, we established a signature that identifies tumours of BRCA1 carriers. The poor prognosis signature consists of genes regulating cell cycle, invasion, metastasis and angiogenesis. This gene expression profile will outperform all currently used clinical parameters in predicting disease outcome. Our findings provide a strategy to select patients who would benefit from adjuvant therapy.

Adult↗

PubMed2002

Recent developments in DNA microarrays.

DNA microarrays are used to quantify tens of thousands of DNA or RNA sequences in a single assay. Upon their introduction approximately six years ago, DNA microarrays were viewed as a disruptive technology that would fundamentally alter the scientific landscape. Supporting this view, the number of applications of DNA microarray technology has since expanded exponentially. Here, we review recent advances in microarray technology and selected new applications of the technology.

Animals↗