Biomedical subjects

Gene Cutler

Publications and source records attributed to Gene Cutler.

5 recordsLinked to original sources

PubMed2005

Hepatocyte growth factor is a preferred in vitro substrate for human hepsin, a membrane-anchored serine protease implicated in prostate and ovarian cancers.

Hepsin is a membrane-anchored, trypsin-like serine protease with prominent expression in the human liver and tumours of the prostate and ovaries. To better understand the biological functions of hepsin, we identified macromolecular substrates employing a tetrapeptide PS-SCL (positional scanning-synthetic combinatorial library) screen that rapidly determines the P1-P4 substrate specificity. Hepsin exhibited strong preference at the P1 position for arginine over lysine, and favoured threonine, leucine or asparagine at the P2, glutamine or lysine at the P3, and proline or lysine at the P4 position. The relative activity of hepsin toward individual AMC (7-amino-4-methylcoumarin)-tetrapeptides was generally consistent with the overall peptide profiling results derived from the PC-SCL screen. The most active tetrapeptide substrate Ac (acetyl)-KQLR-AMC matched with the activation cleavage site of the hepatocyte growth factor precursor sc-HGF (single-chain HGF), KQLR downward arrowVVNG (where downward arrow denotes the cleavage site), as identified by a database analysis of trypsin-like precursors. X-ray crystallographic studies with KQLR chloromethylketone showed that the KQLR peptide fits well into the substrate-binding cleft of hepsin. This hepsin-processed HGF induced c-Met receptor tyrosine phosphorylation in SKOV-3 ovarian cancer cells, indicating that the hepsin-cleaved HGF is biologically active. Activation cleavage site mutants of sc-HGF with predicted non-preferred sequences, DPGR downward arrowVVNG or KQLQ downward arrowVVNG, were not processed, illustrating that the P4-P1 residues can be important determinants for substrate specificity. In addition to finding macromolecular hepsin substrates, the extracellular inhibitors of the HGF activator, HAI-1 and HAI-2, were potent inhibitors of hepsin activity (IC50 4+/-0.2 nM and 12+/-0.5 nM respectively). Together, our findings suggest that the HGF precursor is a potential in vivo substrate for hepsin in tumours, where hepsin expression is dysregulated and may influence tumorigenesis through inappropriate activation and/or regulation of HGF receptor (c-Met) functions.

Binding Sites↗

PubMed2005

Multiclass cancer classification and biomarker discovery using GA-based algorithms.

MOTIVATION: The development of microarray-based high-throughput gene profiling has led to the hope that this technology could provide an efficient and accurate means of diagnosing and classifying tumors, as well as predicting prognoses and effective treatments. However, the large amount of data generated by microarrays requires effective reduction of discriminant gene features into reliable sets of tumor biomarkers for such multiclass tumor discrimination. The availability of reliable sets of biomarkers, especially serum biomarkers, should have a major impact on our understanding and treatment of cancer. RESULTS: We have combined genetic algorithm (GA) and all paired (AP) support vector machine (SVM) methods for multiclass cancer categorization. Predictive features can be automatically determined through iterative GA/SVM, leading to very compact sets of non-redundant cancer-relevant genes with the best classification performance reported to date. Interestingly, these different classifier sets harbor only modest overlapping gene features but have similar levels of accuracy in leave-one-out cross-validations (LOOCV). Further characterization of these optimal tumor discriminant features, including the use of nearest shrunken centroids (NSC), analysis of annotations and literature text mining, reveals previously unappreciated tumor subclasses and a series of genes that could be used as cancer biomarkers. With this approach, we believe that microarray-based multiclass molecular analysis can be an effective tool for cancer biomarker discovery and subsequent molecular cancer diagnosis.

Algorithms↗

PubMed2004

Elucidation of signaling properties of vasopressin receptor-related receptor 1 by using the chimeric receptor approach.

The identification of endogenous or surrogate ligands for orphan G protein-coupled receptors (GPCRs) represents one of the most important tasks in GPCR biology and pharmacology. The challenge lies in choosing an appropriate assay in the absence of ways to activate the receptor of interest. We investigated the signaling pathway for an orphan GPCR referred to here as vasopressin receptor-related receptor 1 (VRR1) by generating a chimeric receptor, V1a/VRR1. The engineered construct contained vasopressin V1a receptor with all three intracellular loops and C terminus replaced by those of VRR1. The chimera behaved like a typical GPCR when transiently and stably expressed in mammalian cell lines based on radioligand binding and receptor internalization studies. Upon arginine vasopressin stimulation, this chimeric receptor induced robust calcium mobilization and increase of adenylate cyclase activity. The observed signaling activities are through the activation of the chimera instead of endogenously expressed receptors, as single amino acid changes in the second transmembrane regions of the chimera drastically reduced receptor efficacy and potency. Our results suggest that VRR1 has dual signaling properties in coupling to both G(q) and G(S) pathways. Analysis of native VRR1 receptor signaling pathway by using a recently identified ligand for VRR1 confirmed this conclusion and therefore validated the utility of the chimeric receptor approach for signaling pathway identification.

Amino Acid Sequence↗

PubMed2003

A comparative analysis of HGSC and Celera human genome assemblies and gene sets.

MOTIVATION: Since the simultaneous publication of the human genome assembly by the International Human Genome Sequencing Consortium (HGSC) and Celera Genomics, several comparisons have been made of various aspects of these two assemblies. In this work, we set out to provide a more comprehensive comparative analysis of the two assemblies and their associated gene sets. RESULTS: The local sequence content for both draft genome assemblies has been similar since the early releases, however it took a year for the quality of the Celera assembly to approach that of HGSC, suggesting an advantage of HGSC's hierarchical shotgun (HS) sequencing strategy over Celera's whole genome shotgun (WGS) approach. While similar numbers of ab initio predicted genes can be derived from both assemblies, Celera's Otto approach consistently generated larger, more varied gene sets than the Ensembl gene build system. The presence of a non-overlapping gene set has persisted with successive data releases from both groups. Since most of the unique genes from either genome assembly could be mapped back to the other assembly, we conclude that the gene set discrepancies do not reflect differences in local sequence content but rather in the assemblies and especially the different gene-prediction methodologies.

Databases, Protein↗

PubMed2002

Comparative analysis of human genome assemblies reveals genome-level differences.

Previous comparative analysis has revealed a significant disparity between the predicted gene sets produced by the International Human Genome Sequencing Consortium (HGSC) and Celera Genomics. To determine whether the source of this discrepancy was due to underlying differences in the genomic sequences or different gene prediction methodologies, we analyzed both genome assemblies in parallel. Using the GENSCAN gene prediction algorithm, we generated predicted transcriptomes that could be directly compared. BLAST-based comparisons revealed a 20-30% difference between the transcriptomes. Further differences between the two genomes were revealed with protein domain PFAM analyses. These results suggest that fundamental differences between the two genome assemblies are likely responsible for a significant portion of the discrepancy between the transcript sets predicted by the two groups.

Chromosome Mapping↗