Efficient and fine mapping of RMES1 conferring resistance to sorghum aphid Melanaphis sacchari

Melanaphis sacchari causes serious damage to sorghum (Sorghum bicolor (L.) Moench) growth, development and productivity in many countries. A dominant gene (RMES1) conferring resistance to M. sacchari has been found in the grain sorghum variety Henong 16 (HN16), but fine mapping of the RMES1 locus remains to be reported. In this study, genetic populations segregating for RMES1 were prepared with HN16 and BTx623 as parental lines. The latter had been used for sorghum genome sequencing but was found to be susceptible to M. sacchari in this work. A total of 11 molecular markers were mapped to the short arm of chromosome 6 harboring RMES1. The closest markers flanking the RMES1 locus were Sb6m2650 and Sb6rj2776, which delimited a chromosomal region of about 126 kb containing five predicted genes. The utility of the newly identified DNA markers for tagging RMES1 in molecular breeding of M. sacchari resistance, and further efforts in cloning RMES1, are discussed.     

Treatment with vector-expressed small hairpin RNAs against Ki67 RNA-induced cell growth inhibition and apoptosis in human renal carcinoma cells

Short hairpin RNAs (shRNAs) transcribed by.RNA polymerase Ⅲ promoters can triggersequence-selective gene silencing in mammalian cells.By virtue of their excellent function in knocking downexpression of cancer-associated genes,shRNAs could be used as new therapeutic agents for cancer.Asoverexpression of Ki67 in renal cancer has been correlated to a more aggressive tumor phenotype,inhibitionof Ki67 protein expression by means of shRNAs seems to be a promising approach for the therapy of renalcancer.In this study,we constructed an expression plasmid encoding shRNAs against the Ki67 gene,namedpSilencerKi67,and transfected it into human renal carcinoma cells.The pSilencerKi67 was shown to signifi-cantly knock down the expression of the Ki67 gene in human renal carcinoma cells,resulting in inhibitingproliferation and inducing apoptotic cell death that can be maintained for at least 6d.These findings offer thepromise of using vector-based shRNAs against Ki67 in renal cancer gene therapy.     

Recent progresses in the application of machine learning approach for predicting protein functional class independent of sequence similarity

Protein sequence contains clues to its function. Functional prediction from sequence presents a challenge particularly for proteins that have low or no sequence similarity to proteins of known function. Recently, machine learning methods have been explored for predicting functional class of proteins from sequence-derived properties independent of sequence similarity, which showed promising potential for low- and non-homologous proteins. These methods can thus be explored as potential tools to complement alignment- and clustering-based methods for predicting protein function. This article reviews the strategies, current progresses, and underlying difficulties in using machine learning methods for predicting the functional class of proteins. The relevant software and web-servers are described. The reported prediction performances in the application of these methods are also presented, which need to be interpreted with caution as they are dependent on such factors as datasets used and choice of parameters.     

Hofmeister effects in protein unfolding kinetics: estimation of changes in surface area upon formation of the transition state

We studied the effect of three electrolytes (LiCl, Na(2)SO(4), GuHCl) on the unfolding reaction of chymopapain, a two-domain protein belonging in the papain family of cysteine proteinases. Due to methodological reasons, these studies were carried out at pH 1.5 where the protein unfolds following biphasic kinetics. We have observed the presence of two different effects of electrolyte concentration on the unfolding reactions. At low ionic strength, the ionic atmosphere brought about an increase in reaction rates, regardless of the type of ions being present; this effect is attributed to a general "electrostatic screening" of charge-charge interactions in the macromolecule. At high ionic strength, each electrolyte exerted a distinctively different effect: both rate constants were largely increased by GuHCl (a well-known protein denaturant), but only slightly by LiCl; in contrast, Na(2)SO(4) (a good precipitant) decreased the value of both unfolding rates. These ion-specific (Hofmeister) effects were further used to estimate changes in accessible surface area (DeltaASA) upon formation of the transition states (TS) for unfolding. Results obtained with LiCl and Na(2)SO(4), which we analyzed by means of a parameterization derived from published solubility data of amino acid derivatives, are consistent with DeltaASA increments (for each phase) of about 8.0% of the total theoretical DeltaASA for complete unfolding of the chymopapain molecule. Results in the presence of GuHCl, which were analyzed by using a previous parameterization of protein unfolding data, gave larger DeltaASAs of activation, equivalent to 13 and 16% of the total unfolding DeltaASA.     

Biodesulfurization of dibenzothiophene by growing cells of Gordonia sp. in batch cultures

A new isolate, identified as Gordonia sp. ZD-7 by 16S rDNA sequence analysis, grew in n-hexadecane containing dibenzothiophene (DBT) which was degraded from 2.8 mM to 0.2 mM within 48 h. Biodesulfurization could be repeatedly performed for more than 190 h, with average desulfurization rates of 5 mmol DBT kg cells (dry wt)⁻¹ h⁻¹.     

Systematic characterization of nuclear proteome during apoptosis: a quantitative proteomic study by differential extraction and stable isotope labeling

Identification and characterization of the nuclear proteome is important for detailed understanding of multiple signaling events in eukaryotic cells. Toward this goal, we extensively characterized the nuclear proteome of human T leukemia cells by sequential extraction of nuclear proteins with different physicochemical properties using three buffer conditions. This large scale proteomic study also tested the feasibility and technical challenges associated with stable isotope labeling by amino acids in cell culture (SILAC) to uncover quantitative changes during apoptosis. Analyzing proteins from three nuclear fractions extracted from naive and apoptotic cells generated 780,530 MS/MS spectra that were used for database searching using the SEQUEST algorithm. This analysis resulted in the identification and quantification of 1,174 putative nuclear proteins. A number of known nuclear proteins involved in apoptosis as well as novel proteins not known to be part of the nuclear apoptotic machinery were identified and quantified. Consistent with SILAC-based quantifications, immunofluorescence staining of nucleus, mitochondria, and some associated proteins from both organelles revealed a dynamic recruitment of mitochondria into nuclear invaginations during apoptosis.