S14 protein in breast cancer cells: direct evidence of regulation by SREBP-1c, superinduction with progestin, and effects on cell growth

Most breast cancers exhibit brisk lipogenesis, and require it for growth. S14 is a lipogenesis-related nuclear protein that is overexpressed in most breast cancers. Sterol response element-binding protein-1c (SREBP-1c) is required for induction of lipogenesis-related genes, including S14 and fatty acid synthase (FAS), in hepatocytes, and correlation of SREBP-1c and FAS expression suggested that SREBP-1c drives lipogenesis in tumors as well. We directly tested the hypothesis that SREBP-1c drives S14 expression and mediates lipogenic effects of progestin in T47D breast cancer cells. Dominant-negative SREBP-1c inhibited induction of S14 and FAS mRNAs by progestin, while active SREBP-1c induced without hormone and superinduced in its presence. Changes in S14 mRNA were reflected in protein levels. A lag time and lack of progestin response elements indicated that S14 and FAS gene activation by progestin is indirect. Knockdown of S14 reduced, whereas overexpression stimulated, T47D cell growth, while nonlipogenic MCF10a mammary epithelial cells were not growth-inhibited. These data directly demonstrate that SREBP-1c drives S14 gene expression in breast cancer cells, and progestin magnifies that effect via an indirect mechanism. This supports the prediction, based on S14 gene amplification and overexpression in breast tumors, that S14 augments breast cancer cell growth and survival.     

Genetic variations of the NR3C1 gene in children with sporadic nephrotic syndrome

Previous studies have demonstrated that the genetic variations of glucocorticoid receptor gene (NR3C1) are associated with both familial steroid resistance and acquired steroid resistance in some diseases, such as Cushing's disease, leukemia, lupus nephritis, and female pseudohermaphroditism. In this study, we examined the genetic variations of NR3C1 in 35 children with sporadic steroid-resistant nephrotic syndrome (SRNS), and in 83 cases with sporadic steroid-sensitive NS (SSNS) using polymerase chain reaction, denaturing high-performance liquid chromatography and DNA sequencing, and analyzed possible associations between NR3C1 variants and steroid resistance in sporadic NS. No causative mutations were found; however, six previously identified and six novel polymorphisms, 1206C > T, 1374A > G, 2382C > T, 2193T > G, IVS7-68_-63delAAAAAA, and IVS8-9C > G, were detected. Two novel haplotypes, [1374A > G; IVS7-68_-63delAAAAAA; IVS8-9C > G; 2382C > T] and [1896C > T; 2166C > T; 2430T > C], of NR3C1 were also identified in sporadic NS and controls. The odds ratios (95% Confidence Interval) for the two novel NR3C1 haplotypes in the sporadic nephrotic children at risk of steroid resistance were 4.970 (0.889-27.788) and 2.194 (0.764-6.306), respectively, but the association between NR3C1 haplotypes and steroid resistance was not significant. Further studies on the possible association between the two novel NR3C1 haplotypes and steroid resistance in sporadic NS in larger cohorts are required.     

Attributable risk function in the proportional hazards model for censored time-to-event

Time-to-event endpoints are often used in clinical and epidemiological studies to evaluate disease association with hazardous exposures. In the statistical literature of time-to-event analysis, such association is usually measured by the hazard ratio in the proportional hazards model. In public health, it is also of important interest to assess the excess risk attributable to an exposure in a given population. In this article, we extend the notion of 'population attributable fraction' for the binary outcomes to the attributable risk function for the event times in prospective studies. A simple estimator of the time-varying attributable risk function is proposed under the proportional hazards model. Its inference procedures are established. Monte-Carlo simulation studies are conducted to evaluate its validity and performance. The proposed methodology is motivated and demonstrated by the data collected in a multicenter acquired immunodeficiency syndrome (AIDS) cohort study to estimate the attributable risk of human immunodeficiency virus type 1 (HIV-1) infections due to several potential risk factors.     

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background  

The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities.     

Tools for integrated sequence-structure analysis with UCSF Chimera

Background  

Comparing related structures and viewing the structures in the context of sequence alignments are important tasks in protein structure-function research. While many programs exist for individual aspects of such work, there is a need for interactive visualization tools that: (a) provide a deep integration of sequence and structure, far beyond mapping where a sequence region falls in the structure and vice versa; (b) facilitate changing data of one type based on the other (for example, using only sequence-conserved residues to match structures, or adjusting a sequence alignment based on spatial fit); (c) can be used with a researcher's own data, including arbitrary sequence alignments and annotations, closely or distantly related sets of proteins, etc.; and (d) interoperate with each other and with a full complement of molecular graphics features. We describe enhancements to UCSF Chimera to achieve these goals.     

Molecular Cloning and Characterization of Two 9-Lipoxygenase Genes from Taxus chinensis

Plant lipoxygenases (LOXs) are functionally diverse class of dioxygenases involved in multiple physiological processes such as plant growth, biotic and abiotic stress responses, and secondary metabolite accumulation. In this paper, two LOX genes, TcLOX1 and TcLOX2, were cloned from Taxus chinensis cells. Multiple alignment of the deduced amino acid sequences with those of other plants demonstrated the putative LH2/PLAT domain, lipoxygenase iron-binding catalytic domain, lipoxygenase_1 and lipoxygenase_2 signature sequences. Phylogenetic analysis suggested that TcLOX1 and TcLOX2 putative proteins are most probably 9-LOXs, and shared the highest identity with the tea plant CsLOX1 and Picea sitchensis LOX genes, respectively. Semiquantitative RT-PCR analysis showed that TcLOX1 was preferentially expressed in stem and root, while TcLOX2 was preferentially expressed in root. Real-time quantitative PCR analysis showed that a strong upregulation of TcLOX1 was observed in response to methyl jasmonate and abscisic acid (ABA), while TcLOX2 was strongly upregulated by ABA. However, TcLOX1 and TcLOX2 were nearly not responding to salicylic acid. These data suggest both TcLOX1 and TcLOX2 play an important role in T. chinensis, and they are required in different physiological processes involved in different plant signals in vivo.