A large-scale effort, termed the Secreted Protein Discovery Initiative (SPDI), was undertaken to identify novel secreted and transmembrane proteins. In the first of several approaches, a biological signal sequence trap in yeast cells was utilized to identify cDNA clones encoding putative secreted proteins. A second strategy utilized various algorithms that recognize features such as the hydrophobic properties of signal sequences to identify putative proteins encoded by expressed sequence tags (ESTs) from human cDNA libraries. A third approach surveyed ESTs for protein sequence similarity to a set of known receptors and their ligands with the BLAST algorithm. Finally, both signal-sequence prediction algorithms and BLAST were used to identify single exons of potential genes from within human genomic sequence. The isolation of full-length cDNA clones for each of these candidate genes resulted in the identification of >1000 novel proteins. A total of 256 of these cDNAs are still novel, including variants and novel genes, per the most recent GenBank release version. The success of this large-scale effort was assessed by a bioinformatics analysis of the proteins through predictions of protein domains, subcellular localizations, and possible functional roles. The SPDI collection should facilitate efforts to better understand intercellular communication, may lead to new understandings of human diseases, and provides potential opportunities for the development of therapeutics.

In the present study we have used a novel, comprehensive mRNA profiling technique (GeneCalling) for determining differential gene expression profiles of human endothelial cells undergoing differentiation into tubelike structures. One hundred fifteen cDNA fragments were identified and shown to represent 90 distinct genes. Although some of the genes identified have previously been implicated in angiogenesis, potential roles for many new genes, including OX-40, white protein homolog, KIAA0188, a homolog of angiopoietin-2, ADAMTS-4 (aggrecanase-1), and stanniocalcin were revealed. Support for the biological significance was confirmed by the abrogation of the changes in the expression of angiogenesis inhibitors and in situ hybridization studies. This study has significantly extends the molecular fingerprint of the changes in gene expression that occur during endothelial differentiation and provides new insights into the potential role of a number of new molecules in angiogenesis. (Am J Pathol 2000,

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Beneficial cardiac effects of growth hormone (GH) have been shown in heart failure in several settings, but studies are lacking on this and other forms of treatment in the cardiomyopathic (CM) mouse heart. In mice with dilated cardiomyopathy due to disruption of the muscle LIM protein (MLP) gene [MLP null mice (MLP-/-)], natural history was first assessed by an initial echocardiogram at 8 weeks and a later follow-up study (n = 31). In most mice, left ventricular (LV) dilation increased and/or function decreased by 5 months, and 3 of 12 mice followed for 9 months died. At the end of follow-up, 22 MLP-/- mice (average age 10.2 months) had both LV dilation and reduced LV function and were selected for studies of GH effects on cardiac function and gene expression; mice were randomized to vehicle (controls) or recombinant human (rh) GH and restudied after 2 weeks. In the GH-treated group compared to the control group, LV % fractional shortening and LV wall thickness (echocardiography) were increased, the LV dP/dtmax (catheter-tip micromanometry) was enhanced, and LV relaxation (tau) improved; however, the LV weight was not significantly increased. The LV expression of many genes was altered in MLP-/- mice, and several were influenced by GH. Thus, short-term RhGH treatment improved LV function in a setting of chronic cardiac deterioration and significantly reduced elevated LV mRNA expression of some (ANP, BNP) but not other members of the embryonic gene program. The MLP null cardiomyopathic mouse can be useful for exploring altered signaling and therapeutic interventions in heart failure.

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Libraries of monovalent display-phage expressing mutant human B-type natriuretic peptide (hBNP) were used to identify variants that preferentially bind natriuretic peptide receptor-A (NPR-A) compared to receptor-C (NPR-C). Position 19 was a significant determinant of receptor specificity for hBNP display phage. The synthetic hBNP variant S19R had a 265-fold improved NPR-A binding over NPR-C, analogous to the atrial natriuretic peptide (ANP) specificity mutation G16R. Mutation of the last three residues of the hBNP disulfide ring, G23F/ L24W/G25R, resulted in about 9-fold improved selectivity. The analogous mutations in ANP decreased NPR-A binding, suggesting divergence in the mechanism of NPR-A recognition.

Traditional methods for flow cytometry (FCM) data processing have relied on manual gating of cell events to define cell populations for statistical analysis. However, this approach has become increasingly problematic with the advances in instrumentation and reagents that allow for evaluation of larger numbers of cell properties. Recently several groups have developed computational methods for automatically identifying cell populations in multidimensional FCM data obviating the need for manual gating. In order to compare the performance of these methods, the Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) competition was established to make available a common set of FCM data together with manual gating results for comparative analysis. The first FlowCAP competition included 5 different data sets with data from 12-30 samples containing 5000-100,000 cell events stained with 3-10 fluorochrome markers. We received 36 analysis result submissions from 14 research groups. Both model fitting and density-based clustering methods were found to perform well in comparison with manual gating by domain experts as the gold standard, using statistical tests to measure and rank algorithm performance. In addition, combining results using a computational “ensemble” method was found to outperform all individual methods. These results suggest that, in the near future, automated computational methods may become an integral part of routine FCM data analysis.