Furin Is the Major Proprotein Convertase Required for KISS1-to-Kisspeptin Processing

KISS1 is a broadly functional secreted proprotein that is then processed into small peptides, termed kisspeptins (KP). Since sequence analysis showed cleavage at KR or RR dibasic sites of the nascent protein, it was hypothesized that enzyme(s) belonging to the proprotein convertase family of proteases process KISS1 to generate KP. To this end, cell lines over-expressing KISS1 were treated with the proprotein convertase inhibitors, Dec-RVKR-CMK and α1-PDX, and KISS1 processing was completely inhibited. To identify the specific enzyme(s) responsible for KISS1 processing, mRNA expression was systematically analyzed for six proprotein convertases found in secretory pathways. Consistent expression of the three proteases – furin, PCSK5 and PCSK7 – were potentially implicated in KISS1 processing. However, shRNA-mediated knockdown of furin – but not PCSK5 or PCSK7 – blocked KISS1 processing. Thus, furin appears to be the essential enzyme for the generation of kisspeptins.     

Data merging for integrated microarray and proteomic analysis.

The functioning of even a simple biological system is much more complicated than the sum of its genes, proteins and metabolites. A premise of systems biology is that molecular profiling will facilitate the discovery and characterization of important disease pathways. However, as multiple levels of effector pathway regulation appear to be the norm rather than the exception, a significant challenge presented by high-throughput genomics and proteomics technologies is the extraction of the biological implications of complex data. Thus, integration of heterogeneous types of data generated from diverse global technology platforms represents the first challenge in developing the necessary foundational databases needed for predictive modelling of cell and tissue responses. Given the apparent difficulty in defining the correspondence between gene expression and protein abundance measured in several systems to date, how do we make sense of these data and design the next experiment? In this review, we highlight current approaches and challenges associated with integration and analysis of heterogeneous data sets, focusing on global analysis obtained from high-throughput technologies.     

Sample size determination for the false discovery rate

MOTIVATION: There is not a widely applicable method to determine the sample size for experiments basing statistical significance on the false discovery rate (FDR). RESULTS: We propose and develop the anticipated FDR (aFDR) as a conceptual tool for determining sample size. We derive mathematical expressions for the aFDR and anticipated average statistical power. These expressions are used to develop a general algorithm to determine sample size. We provide specific details on how to implement the algorithm for a k-group (k > or = 2) comparisons. The algorithm performs well for k-group comparisons in a series of traditional simulations and in a real-data simulation conducted by resampling from a large, publicly available dataset. AVAILABILITY: Documented S-plus and R code libraries are freely available from www.stjuderesearch.org/depts/biostats.     

Statistical development and evaluation of microarray gene expression data filters.

Filtering is a common practice used to simplify the analysis of microarray data by removing from subsequent consideration probe sets believed to be unexpressed. The m/n filter, which is widely used in the analysis of Affymetrix data, removes all probe sets having fewer than m present calls among a set of n chips. The m/n filter has been widely used without considering its statistical properties. The level and power of the m/n filter are derived. Two alternative filters, the pooled p-value filter and the error-minimizing pooled p-value filter are proposed. The pooled p-value filter combines information from the present-absent p-values into a single summary p-value which is subsequently compared to a selected significance threshold. We show that pooled p-value filter is the uniformly most powerful statistical test under a reasonable beta model and that it exhibits greater power than the m/n filter in all scenarios considered in a simulation study. The error-minimizing pooled p-value filter compares the summary p-value with a threshold determined to minimize a total-error criterion based on a partition of the distribution of all probes' summary p-values. The pooled p-value and error-minimizing pooled p-value filters clearly perform better than the m/n filter in a case-study analysis. The case-study analysis also demonstrates a proposed method for estimating the number of differentially expressed probe sets excluded by filtering and subsequent impact on the final analysis. The filter impact analysis shows that the use of even the best filter may hinder, rather than enhance, the ability to discover interesting probe sets or genes. S-plus and R routines to implement the pooled p-value and error-minimizing pooled p-value filters have been developed and are available from www.stjuderesearch.org/depts/biostats/index.html.     

Effects of husbandry parameters on the life-history traits of the apple snail, Marisa cornuarietis: effects of temperature, photoperiod, and population density

Abstract. These experiments are part of a larger study designed to investigate the influence of husbandry parameters on the life history of the apple snail, Marisa cornuarietis . The overall objective of the program is to identify suitable husbandry conditions for maintaining multi-generation populations of this species in the laboratory for use in ecotoxicological testing. In this article, we focus on the effects of photoperiod, temperature, and population density on adult fecundity and juvenile growth. Increasing photoperiod from 12 to 16 h of light per day had no effect on adult fecundity or egg hatching and relatively minor effects on juvenile growth and development. Rearing snails at temperatures between 22°C and 28°C did not influence the rates of egg production or egg clutch size. However, the rates of growth and development (of eggs and juveniles) increased with increasing temperature in this range, and when temperatures were reduced to 22°C egg-hatching success was impaired. Juvenile growth and development were more sensitive to rearing density than adult fecundity traits. On the basis of the present results, we conclude that rearing individuals of M. cornuarietis at a temperature of 25°C, a photoperiod of 12L:12D, and a density of <0.8 snails L⁻¹ (with lower densities for juvenile snails) should provide favorable husbandry conditions for maintaining multi-generation populations of this species.     

The human plasma proteome: a nonredundant list developed by combination of four separate sources

We have merged four different views of the human plasma proteome, based on different methodologies, into a single nonredundant list of 1175 distinct gene products. The methodologies used were 1) literature search for proteins reported to occur in plasma or serum; 2) multidimensional chromatography of proteins followed by two-dimensional electrophoresis and mass spectroscopy (MS) identification of resolved proteins; 3) tryptic digestion and multidimensional chromatography of peptides followed by MS identification; and 4) tryptic digestion and multidimensional chromatography of peptides from low-molecular-mass plasma components followed by MS identification. Of 1,175 nonredundant gene products, 195 were included in more than one of the four input datasets. Only 46 appeared in all four. Predictions of signal sequence and transmembrane domain occurrence, as well as Genome Ontology annotation assignments, allowed characterization of the nonredundant list and comparison of the data sources. The "nonproteomic" literature (468 input proteins) is strongly biased toward signal sequence-containing extracellular proteins, while the three proteomics methods showed a much higher representation of cellular proteins, including nuclear, cytoplasmic, and kinesin complex proteins. Cytokines and protein hormones were almost completely absent from the proteomics data (presumably due to low abundance), while categories like DNA-binding proteins were almost entirely absent from the literature data (perhaps unexpected and therefore not sought). Most major categories of proteins in the human proteome are represented in plasma, with the distribution at successively deeper layers shifting from mostly extracellular to a distribution more like the whole (primarily cellular) proteome. The resulting nonredundant list confirms the presence of a number of interesting candidate marker proteins in plasma and serum.