Analysis of a common inheritable idiotype in IgA-deficient sera using monoclonal antibodies

In these studies we describe the production of three mAb raised to an idiotype on an IgG anticasein antibody isolated from the serum of one IgA-deficient blood donor. These are IgM kappa and block the binding of casein Ag to anticasein antibody. Sera of unrelated IgA-deficient donors were tested for the presence of the idiotype; 15 of 56 IgA-deficient sera (25%) contain the anticasein idiotype, whereas 1 of 45 normal sera was positive. Anticasein antibodies as a whole were predominantly of the IgG1 and IgG3 subclass; idiotype-positive anticaseins are predominantly of the IgG1 subclass. For IgA-deficient donors, the relative amount of idiotype-positive anticasein antibody was correlated with the level of anticasein present in the serum. Studies were done to investigate the potential inheritance of the idiotype in families; in three of four families the idiotype was inherited in an apparent autosomal dominant pattern. Our data show that a common cross-reactive idiotype can be detected in the sera of IgA-deficient individuals and their family members. This suggests that V region markers may be conserved in this humoral immunodeficiency disease.     

Predicting Metabolic Pathways of Small Molecules and Enzymes Based on Interaction Information of Chemicals and Proteins

Metabolic pathway analysis, one of the most important fields in biochemistry, is pivotal to understanding the maintenance and modulation of the functions of an organism. Good comprehension of metabolic pathways is critical to understanding the mechanisms of some fundamental biological processes. Given a small molecule or an enzyme, how may one identify the metabolic pathways in which it may participate? Answering such a question is a first important step in understanding a metabolic pathway system. By utilizing the information provided by chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions, a novel method was proposed by which to allocate small molecules and enzymes to 11 major classes of metabolic pathways. A benchmark dataset consisting of 3,348 small molecules and 654 enzymes of yeast was constructed to test the method. It was observed that the first order prediction accuracy evaluated by the jackknife test was 79.56% in identifying the small molecules and enzymes in a benchmark dataset. Our method may become a useful vehicle in predicting the metabolic pathways of small molecules and enzymes, providing a basis for some further analysis of the pathway systems.     

Expression profiling of <Emphasis Type="Italic">Crambe abyssinica</Emphasis>under arsenate stress identifies genes and gene networks involved in arsenic metabolism and detoxification

Background  

Arsenic contamination is widespread throughout the world and this toxic metalloid is known to cause cancers of organs such as liver, kidney, skin, and lung in human. In spite of a recent surge in arsenic related studies, we are still far from a comprehensive understanding of arsenic uptake, detoxification, and sequestration in plants. Crambe abyssinica, commonly known as 'abyssinian mustard', is a non-food, high biomass oil seed crop that is naturally tolerant to heavy metals. Moreover, it accumulates significantly higher levels of arsenic as compared to other species of the Brassicaceae family. Thus, C. abyssinica has great potential to be utilized as an ideal inedible crop for phytoremediation of heavy metals and metalloids. However, the mechanism of arsenic metabolism in higher plants, including C. abyssinica, remains elusive.     

A yeast's eye view of mammalian reproduction: cross-species gene co-expression in meiotic prophase

Background  

Meiotic prophase is a critical stage in sexual reproduction. Aberrant chromosome recombination during this stage is a leading cause of human miscarriages and birth defects. However, due to the experimental intractability of mammalian gonads, only a very limited number of meiotic genes have been characterized. Here we aim to identify novel meiotic genes important in human reproduction through computational mining of cross-species and cross-sex time-series expression data from budding yeast, mouse postnatal testis, mouse embryonic ovary, and human fetal ovary.     

Calculation of accurate small angle X-ray scattering curves from coarse-grained protein models

Background  

Genome sequencing projects have expanded the gap between the amount of known protein sequences and structures. The limitations of current high resolution structure determination methods make it unlikely that this gap will disappear in the near future. Small angle X-ray scattering (SAXS) is an established low resolution method for routinely determining the structure of proteins in solution. The purpose of this study is to develop a method for the efficient calculation of accurate SAXS curves from coarse-grained protein models. Such a method can for example be used to construct a likelihood function, which is paramount for structure determination based on statistical inference.