Biomedical subjects

Clement Chung

Publications and source records attributed to Clement Chung.

3 recordsLinked to original sources

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

Integrating global proteomic and genomic expression profiles generated from islet alpha cells: opportunities and challenges to deriving reliable biological inferences.

Systematic profiling of expressed gene products represents a promising research strategy for elucidating the molecular phenotypes of islet cells. To this end, we have combined complementary genomic and proteomic methods to better assess the molecular composition of murine pancreatic islet glucagon-producing alphaTC-1 cells as a model system, with the expectation of bypassing limitations inherent to either technology alone. Gene expression was measured with an Affymetrix MG_U74Av2 oligonucleotide array, while protein expression was examined by performing high-resolution gel-free shotgun MS/MS on a nuclear-enriched cell extract. Both analyses were carried out in triplicate to control for experimental variability. Using a stringent detection p value cutoff of 0.04, 48% of all potential mRNA transcripts were predicted to be expressed (probes classified as present in at least two of three replicates), while 1,651 proteins were identified with high-confidence using rigorous database searching. Although 762 of 888 cross-referenced cognate mRNA-protein pairs were jointly detected by both platforms, a sizeable number (126) of gene products was detected exclusively by MS alone. Conversely, marginal protein identifications often had convincing microarray support. Based on these findings, we present an operational framework for both interpreting and integrating dual genomic and proteomic datasets so as to obtain a more reliable perspective into islet alpha cell function.

Animals↗

PubMedDate not supplied

Human tissue profiling with multidimensional protein identification technology.

Profiling of tissues and cell types through systematic characterization of expressed genes or proteins shows promise as a basic research tool, and has potential applications in disease diagnosis and classification. We used multidimensional protein identification protein identification technology (MudPIT) to analyze proteomes for enriched nuclear extracts of eight human tissues: brain, heart, liver, lung, muscle, pancreas, spleen, and testis. We show that the method is approximately 80% reproducible. We address issues of relative abundance, tissue-specificity, and selectivity, and the significance of proteins whose expression does not correlate with that of the corresponding mRNA. Surprisingly, most proteins are detected in a single tissue. These proteins tend to fulfill specialist (and potentially tissue-specific) functions compared to proteins expressed in two or more tissues.

Algorithms↗