Biomedical subjects

Michael T Hallett

Publications and source records attributed to Michael T Hallett.

4 recordsLinked to original sources

PubMed2006

Strain-dependent pulmonary gene expression profiles of a cystic fibrosis mouse model.

Cystic fibrosis (CF) lung disease severity is influenced by unknown genetic factors apart from the disease causative gene, cystic fibrosis transmembrane conductance regulator (CFTR). Previous studies have shown the C57BL/6J congenic Cftr(-/-) (B6 CF) mouse to develop a fibrotic lung disease compared with both CF mice of the BALB/c background and wild-type animals. In this report, gene expression profiling with microarrays was used to identify genes differentially expressed in the lungs of B6 and BALB CF mice compared with non-CF littermates. Seven hundred two genes or expressed sequence tags (ESTs) were identified to be differentially expressed between the B6 CF and non-CF control lungs (P < 0.05), and, by Gene Ontology classification, the B6 CF response included the cell proliferation categories of DNA metabolism and mitosis. In the response of BALB mice to nonfunctional Cftr, 943 genes/ESTs were differentially expressed compared with controls. The biological processes of apoptosis and T and B cell proliferation were prominent in the gene list of the BALB CF strain. In support of this strain difference, increased T lymphocyte infiltration was evident in the lungs of BALB CF mice, through immunohistochemical staining, compared with the lungs from both B6 CF and non-CF control mice. Four hundred forty-four genes/ESTs were differentially expressed between B6 CF and BALB CF mice (P < 0.05, fold > 2), including 56 that map to previously identified linkage intervals. These results suggest that the variable severity of CF lung disease in this mouse model is controlled by multiple genetic factors, including those of an immune response.

Animals↗

PubMed2006

Global survey of organ and organelle protein expression in mouse: combined proteomic and transcriptomic profiling.

Organs and organelles represent core biological systems in mammals, but the diversity in protein composition remains unclear. Here, we combine subcellular fractionation with exhaustive tandem mass spectrometry-based shotgun sequencing to examine the protein content of four major organellar compartments (cytosol, membranes [microsomes], mitochondria, and nuclei) in six organs (brain, heart, kidney, liver, lung, and placenta) of the laboratory mouse, Mus musculus. Using rigorous statistical filtering and machine-learning methods, the subcellular localization of 3274 of the 4768 proteins identified was determined with high confidence, including 1503 previously uncharacterized factors, while tissue selectivity was evaluated by comparison to previously reported mRNA expression patterns. This molecular compendium, fully accessible via a searchable web-browser interface, serves as a reliable reference of the expressed tissue and organelle proteomes of a leading model mammal.

Animals↗

PubMed2005

Refining protein subcellular localization.

The study of protein subcellular localization is important to elucidate protein function. Even in well-studied organisms such as yeast, experimental methods have not been able to provide a full coverage of localization. The development of bioinformatic predictors of localization can bridge this gap. We have created a Bayesian network predictor called PSLT2 that considers diverse protein characteristics, including the combinatorial presence of InterPro motifs and protein interaction data. We compared the localization predictions of PSLT2 to high-throughput experimental localization datasets. Disagreements between these methods generally involve proteins that transit through or reside in the secretory pathway. We used our multi-compartmental predictions to refine the localization annotations of yeast proteins primarily by distinguishing between soluble lumenal proteins and soluble proteins peripherally associated with organelles. To our knowledge, this is the first tool to provide this functionality. We used these sub-compartmental predictions to characterize cellular processes on an organellar scale. The integration of diverse protein characteristics and protein interaction data in an appropriate setting can lead to high-quality detailed localization annotations for whole proteomes. This type of resource is instrumental in developing models of whole organelles that provide insight into the extent of interaction and communication between organelles and help define organellar functionality.

Amino Acid Motifs↗

PubMed2004

Predicting subcellular localization via protein motif co-occurrence.

The prediction of subcellular localization of proteins from their primary sequence is a challenging problem in bioinformatics. We have created a Bayesian network localization predictor called PSLT that is based on the combinatorial presence of InterPro motifs and specific membrane domains in human proteins. This probabilistic framework generates a likelihood of localization to all organelles and allows to predict multicompartmental proteins. When used to predict on nine compartments, PSLT achieves an accuracy of 78% as estimated by using a 10-fold cross-validation test and a coverage of 74%. When used to predict the localization of proteins from other closely related species, it achieves a prediction accuracy and a coverage >80%. We compared the localization predictions of PSLT to those determined through GFP-tagging and microscopy for a group of human proteins. We found two general classes of proteins that are mislocalized by the GFP-tagging strategy but are correctly localized by PSLT. This suggests that PSLT can be used in combination with experimental approaches for localization to identify proteins for which additional experimental validation is required. We used our predictor to annotate all 9793 human proteins from SWISS-PROT release 41.25, 16% of which are predicted by PSLT to be present in more than one compartment.

Humans↗