Prediction of potent shRNAs with a sequential classification algorithm.

We present SplashRNA, a sequential classifier to predict potent microRNA-based short hairpin RNAs (shRNAs). Trained on published and novel data sets, SplashRNA outperforms previous algorithms and reliably predicts the most efficient shRNAs for a given gene. Combined with an optimized miR-E backbone, >90% of high-scoring SplashRNA predictions trigger >85% protein knockdown when expressed from a single genomic integration. SplashRNA can significantly improve the accuracy of loss-of-function genetics studies and facilitates the generation of compact shRNA libraries.

An optimized microRNA backbone for effective single-copy RNAi.

Short hairpin RNA (shRNA) technology enables stable and regulated gene repression. For establishing experimentally versatile RNAi tools and minimizing toxicities, synthetic shRNAs can be embedded into endogenous microRNA contexts. However, due to our incomplete understanding of microRNA biogenesis, such "shRNAmirs" often fail to trigger potent knockdown, especially when expressed from a single genomic copy. Following recent advances in design of synthetic shRNAmir stems, here we take a systematic approach to optimize the experimental miR-30 backbone. Among several favorable features, we identify a conserved element 3' of the basal stem as critically required for optimal shRNAmir processing and implement it in an optimized backbone termed "miR-E", which strongly increases mature shRNA levels and knockdown efficacy. Existing miR-30 reagents can be easily converted to miR-E, and its combination with up-to-date design rules establishes a validated and accessible platform for generating effective single-copy shRNA libraries that will facilitate the functional annotation of the genome.

Simultaneous detection of multiple mRNA markers CK19, CEA, c-Met, Her2/neu and hMAM with membrane array, an innovative technique with a great potential for breast cancer diagnosis.

The objective of this study was mainly to develop and evaluate a membrane array-based method simultaneously detecting the expression levels of a multiple mRNA marker panel in the peripheral blood for used in complementary breast cancer diagnosis. The mRNA markers employed included cytokeratin 19 (CK-19), carcinoembryonic antigen (CEA), c-Met, Her2/neu, and mammaglobin (hMAM). The specimens of peripheral blood were collected from 80 healthy women and 102 female patients with breast cancer. The expression levels of molecular markers were evaluated by real-time Q-PCR and membrane array. Data obtained from real-time Q-PCR and membrane array were subjected to linear regression analysis, revealing that there was a high degree of correlation between the results of these two methods (r=0.979, P<0.0001). The result of membrane array assay with a combined panel of five mRNA markers was demonstrated to achieve sensitivity of 80.6%, and specificity of 83.8% for breast cancer detection, much higher than those of analysis of single marker. In addition, we demonstrated that the membrane array method could detect circulating cancer cells at a density as low as five cancer cells per 1 ml of blood. The analysis of correlation between the outcome of membrane array and clinicopathological characteristics indicated that overexpression of the multiple marker panel was significantly correlated with tumor size (P=0.030) and TNM stage (0.009). In conclusion, the detection of circulating cancer cells by means of membrane array simultaneously monitoring five mRNA markers could significantly enhance the sensitivity and specificity for cancer cell detection.