Recent transcriptome studies have revealed that a large number of transcripts in mammals and other organisms do not encode proteins but function as noncoding RNAs (ncRNAs) instead. As millions of transcripts are generated by large-scale cDNA and EST sequencing projects every year, there is a need for automatic methods to distinguish protein-coding RNAs from noncoding RNAs accurately and quickly. We developed a support vector machine-based classifier, named Coding Potential Calculator (CPC), to assess the protein-coding potential of a transcript based on six biologically meaningful sequence features. Tenfold cross-validation on the training dataset and further testing on several large datasets showed that CPC can discriminate coding from noncoding transcripts with high accuracy. Furthermore, CPC also runs an order-of-magnitude faster than a previous state-of-the-art tool and has higher accuracy. We developed a user-friendly web-based interface of CPC at http://cpc.cbi.pku.edu.cn. In addition to predicting the coding potential of the input transcripts, the CPC web server also graphically displays detailed sequence features and additional annotations of the transcript that may facilitate users’ further investigation.

Interleukin 17 (IL-17)-producing T helper cells (T(H)-17 cells) are increasingly recognized as key participants in various autoimmune diseases, including multiple sclerosis. Although sets of transcription factors and cytokines are known to regulate T(H)-17 differentiation, the role of noncoding RNA is poorly understood. Here we identify a T(H)-17 cell-associated microRNA, miR-326, whose expression was highly correlated with disease severity in patients with multiple sclerosis and mice with experimental autoimmune encephalomyelitis (EAE). In vivo silencing of miR-326 resulted in fewer T(H)-17 cells and mild EAE, and its overexpression led to more T(H)-17 cells and severe EAE. We also found that miR-326 promoted T(H)-17 differentiation by targeting Ets-1, a negative regulator of T(H)-17 differentiation. Our data show a critical role for microRNA in T(H)-17 differentiation and the pathogenesis of multiple sclerosis.

Novel silica-coated terbium(III) chelate fluorescent nanoparticles have been prepared and characterized as a new type of fluorescence probe for highly sensitive time-resolved fluorescence bioassay. The preparation was carried out in a water-in-oil microemulsion containing a strongly fluorescent Tb(3+) chelate, N,N,N(1),N(1)-[2,6-bis(3'-aminomethyl-1'-pyrazolyl)-phenylpyridine]tetrakis(acetate)-Tb(3+), Triton X-100, hexanol, and cyclohexane by controlling hydrolysis of tetraethyl orthosilicate. The nanoparticles are spherical and uniform in size, 42 +/- 3 nm in diameter, strongly fluorescent, and highly photostable and have enough of a long fluorescence lifetime (1.52 ms) for time-resolved fluorescence measurement. A stable and nontoxic method was developed for the surface modification and protein immobilization of the nanoparticles. As a model of application, the nanoparticle-labeled streptavidin was prepared and used in a sandwich-type time-resolved fluoroimmunoassay of human prostate-specific antigen (PSA) by using a 96-well microtiter plate as the solid-phase carrier. The method gives a detection limit of 7.0 pg/mL for the PSA assay.