Biomedical subjects
Michael Hallett
Publications and source records attributed to Michael Hallett.
Quantitative proteomics analysis of the secretory pathway.
We report more than 1400 proteins of the secretory-pathway proteome and provide spatial information on the relative presence of each protein in the rough and smooth ER Golgi cisternae and Golgi-derived COPI vesicles. The data support a role for COPI vesicles in recycling and cisternal maturation, showing that Golgi-resident proteins are present at a higher concentration than secretory cargo. Of the 1400 proteins, 345 were identified as previously uncharacterized. Of these, 230 had their subcellular location deduced by proteomics. This study provides a comprehensive catalog of the ER and Golgi proteomes with insight into their identity and function.
Mapping cis-acting regulatory variation in recombinant congenic strains.
We present an integrated approach for the enriched detection of genes subject to cis-acting variation in the mouse genome. Gene expression profiling was performed with lung tissue from a panel of recombinant congenic strains (RCS) derived from A/J and C57BL/6J inbred mouse strains. A multiple-regression model measuring the association between gene expression level, donor strain of origin (DSO), and predominant strain background identified over 1,500 genes (P < 0.05) whose expression profiles differed according to the DSO. This model also identified over 1,200 genes whose expression showed dependence on background (P < 0.05), indicating the influence of background genetic context on transcription levels. Sequences obtained from 1-kb segments of 3'-untranslated regions identified single nucleotide polymorphisms in 64% of genes whose expression levels correlated with DSO status, compared with 29% of genes that displayed no association (P < 0.01, Fisher exact test). Allelic imbalance was identified in 50% of genes positive for expression-DSO association, compared with 22% of negative genes (P < 0.05, Fisher exact test). Together, these results demonstrate the utility of RCS mice for identifying the roles of proximal genetic determinants and background genetic context in determining gene expression levels. We propose the use of this integrated experimental approach in multiple tissues from this and other RCS panels as a means for genome-wide cataloging of genetic regulatory mechanisms in laboratory strains of mice.
Gene expression signatures of morphologically normal breast tissue identify basal-like tumors.
INTRODUCTION: The role of the cellular microenvironment in breast tumorigenesis has become an important research area. However, little is known about gene expression in histologically normal tissue adjacent to breast tumor, if this is influenced by the tumor, and how this compares with non-tumor-bearing breast tissue. METHODS: To address this, we have generated gene expression profiles of morphologically normal epithelial and stromal tissue, isolated using laser capture microdissection, from patients with breast cancer or undergoing breast reduction mammoplasty (n = 44). RESULTS: Based on this data, we determined that morphologically normal epithelium and stroma exhibited distinct expression profiles, but molecular signatures that distinguished breast reduction tissue from tumor-adjacent normal tissue were absent. Stroma isolated from morphologically normal ducts adjacent to tumor tissue contained two distinct expression profiles that correlated with stromal cellularity, and shared similarities with soft tissue tumors with favorable outcome. Adjacent normal epithelium and stroma from breast cancer patients showed no significant association between expression profiles and standard clinical characteristics, but did cluster ER/PR/HER2-negative breast cancers with basal-like subtype expression profiles with poor prognosis. CONCLUSION: Our data reveal that morphologically normal tissue adjacent to breast carcinomas has not undergone significant gene expression changes when compared to breast reduction tissue, and provide an important gene expression dataset for comparative studies of tumor expression profiles.
Identifying regulatory subnetworks for a set of genes.
High throughput genomic/proteomic strategies, such as microarray studies, drug screens, and genetic screens, often produce a list of genes that are believed to be important for one or more reasons. Unfortunately it is often difficult to discern meaningful biological relationships from such lists. This study presents a new bioinformatic approach that can be used to identify regulatory subnetworks for lists of significant genes or proteins. We demonstrate the utility of this approach using an interaction network for yeast constructed from BIND, TRANSFAC, SCPD, and chromatin immunoprecipitation (ChIP)-Chip data bases and lists of genes from well known metabolic pathways or differential expression experiments. The approach accurately rediscovers known regulatory elements of the heat shock response as well as the gluconeogenesis, galactose, glycolysis, and glucose fermentation pathways in yeast. We also find evidence supporting a previous conjecture that approximately half of the enzymes in a metabolic pathway are transcriptionally co-regulated. Finally we demonstrate a previously unknown connection between GAL80 and the diauxic shift in yeast.
DePIE: Designing Primers for Protein Interaction Experiments.
Several primer prediction and analysis programs have been developed for diverse applications. However, none of these existing programs can be directly used for the design of primers in protein interaction experiments, since proteins may have transmembrane domains (TMDs) and/or a signal peptide that must be excluded from experiments. Furthermore, it is frequently the case that a short restriction sequences must be added to each primer in order to clone PCR products into a given destination vectors for expression. DePIE, a web-based primer design tool, was developed to address these deficiencies. The program takes as input NCBI protein accession numbers and returns primer information including nucleotide sequences, thermodynamic melting temperature of the nucleotide sequences and the target positions. DePIE is implemented in JAVA, PERL and PHP and has proven to be very efficient in designing primers for our interaction experiments. DePIE services can be accessed at the web site: http://biocore.unl.edu/primer/primerPI.html.