Multispectral fingerprinting for improved in vivo cell dynamics analysis

Background  

Tracing cell dynamics in the embryo becomes tremendously difficult when cell trajectories cross in space and time and tissue density obscure individual cell borders. Here, we used the chick neural crest (NC) as a model to test multicolor cell labeling and multispectral confocal imaging strategies to overcome these roadblocks.     

A class I chitinase from soybean seed coat.

Protein extracts from soybean (Glycine max [L.] Merr) seed hulls were fractionated by isoelectric focusing and SDS-PAGE analysis and components identified by peptide microsequencing. An abundant 32 kDa protein possessed an N-terminal cysteine-rich hevein domain present in class I chitinases and in other chitin-binding proteins. The protein could be purified from seed coats by single step binding to a chitin bead matrix and displayed chitinase activity by an electrophoretic zymogram assay. The corresponding cDNA and genomic clones for the chitinase protein were isolated and characterized, and the expression pattern determined by RNA blot analysis. The deduced peptide sequence of 320 amino acids included an N-terminal signal peptide and conserved chitin-binding and catalytic domains interspaced by a proline hinge. An 11.3 kb EcoRI genomic fragment bearing the 2.4 kb chitinase gene was fully sequenced. The gene contained two introns and was flanked by A+T-rich tracts. Analysis by DNA blot hybridization showed that this is a single or low copy gene in the soybean genome. The chitinase is expressed late in seed development, with particularly high expression in the seed coat. Expression was also evident in the late stages of development of the pod, root, leaf, and embryo, and in tissues responding to pathogen infection. This study further illustrates the differences in protein composition of the various seed tissues and demonstrates that defence-related proteins are prevalent in the seed coat.     

A novel mode of chromosomal evolution peculiar to filamentous Ascomycete fungi

Background  

Gene loss, inversions, translocations, and other chromosomal rearrangements vary among species, resulting in different rates of structural genome evolution. Major chromosomal rearrangements are rare in most eukaryotes, giving large regions with the same genes in the same order and orientation across species. These regions of macrosynteny have been very useful for locating homologous genes in different species and to guide the assembly of genome sequences. Previous analyses in the fungi have indicated that macrosynteny is rare; instead, comparisons across species show no synteny or only microsyntenic regions encompassing usually five or fewer genes. To test the hypothesis that chromosomal evolution is different in the fungi compared to other eukaryotes, synteny was compared between species of the major fungal taxa.     

Immunoglobulin G galactosylation and sialylation are associated with pregnancy-induced improvement of rheumatoid arthritis and the postpartum flare: results from a large prospective cohort study

Introduction  

Improvement of rheumatoid arthritis (RA) during pregnancy has been causatively associated with increased galactosylation of immunoglobulin G (IgG) N-glycans. Since previous studies were small, did not include the postpartum flare and did not study sialylation, these issues were addressed in the present study.     

Using the realized relationship matrix to disentangle confounding factors for the estimation of genetic variance components of complex traits

Background

In the analysis of complex traits, genetic effects can be confounded with non-genetic effects, especially when using full-sib families. Dominance and epistatic effects are typically confounded with additive genetic and non-genetic effects. This confounding may cause the estimated genetic variance components to be inaccurate and biased.

Methods

In this study, we constructed genetic covariance structures from whole-genome marker data, and thus used realized relationship matrices to estimate variance components in a heterogenous population of ~ 2200 mice for which four complex traits were investigated. These mice were genotyped for more than 10,000 single nucleotide polymorphisms (SNP) and the variances due to family, cage and genetic effects were estimated by models based on pedigree information only, aggregate SNP information, and model selection for specific SNP effects.

Results and conclusions

We show that the use of genome-wide SNP information can disentangle confounding factors to estimate genetic variances by separating genetic and non-genetic effects. The estimated variance components using realized relationship were more accurate and less biased, compared to those based on pedigree information only. Models that allow the selection of individual SNP in addition to fitting a relationship matrix are more efficient for traits with a significant dominance variance.