Do-it-yourself: construction of a custom cDNA macroarray platform with high sensitivity and linear range

Background: Research involving gene expression profiling and clinical applications, such as diagnostics and prognostics, often require a DNA array platform that is flexibly customisable and cost-effective, but at the same time is highly sensitive and capable of accurately and reproducibly quantifying the transcriptional expression of a vast number of genes over the whole transcriptome dynamic range using low amounts of RNA sample. Hereto, a set of easy-to-implement practical optimisations to the design of cDNA-based nylon macroarrays as well as sample (33)P-labeling, hybridisation protocols and phosphor screen image processing were analysed for macroarray performance. Results: The here proposed custom macroarray platform had an absolute sensitivity as low as 50,000 transcripts and a linear range of over 5 log-orders. Its quality of identifying differentially expressed genes was at least comparable to commercially available microchips. Interestingly, the quantitative accuracy was found to correlate significantly with corresponding reversed transcriptase - quantitative PCR values, the gold standard gene expression measure (Pearson's correlation test p < 0.0001). Furthermore, the assay has low cost and input RNA requirements (0.5 mu g and less) and has a sound reproducibility. Conclusions: Results presented here, demonstrate for the first time that self-made cDNA-based nylon macroarrays can produce highly reliable gene expression data with high sensitivity and covering the entire mammalian dynamic range of mRNA abundances. Starting off from minimal amounts of unamplified total RNA per sample, a reasonable amount of samples can be assayed simultaneously for the quantitative expression of hundreds of genes in an easily customisable and cost-effective manner

A cyclic peptide inhibitor of HIF-1 heterodimerization that inhibits hypoxia signaling in cancer cells

Hypoxia inducible factor-1 (HIF-1) is a heterodimeric transcription factor that acts as the master regulator of cellular response to reduced oxygen levels, thus playing a key role in the adaptation, survival and progression of tumors. Here we report cyclo-CLLFVY, identified from a library of 3.2 million cyclic hexapeptides using a genetically encoded high-throughput screening platform, as an inhibitor of the HIF-1α/HIF-1β protein-protein interaction in vitro and in cells. The identified compound inhibits HIF-1 dimerization and transcription activity by binding to the PAS-B domain of HIF-1α, reducing HIF-1-mediated hypoxia response signaling in a variety of cell lines, without affecting the function of the closely related HIF-2 isoform. The reported cyclic peptide demonstrates the utility of our high-throughput screening platform for the identification of protein-protein interaction inhibitors, and forms the starting point for the development of HIF-1 targeted cancer therapeutics

HiTRACE: High-throughput robust analysis for capillary electrophoresis

Motivation: Capillary electrophoresis (CE) of nucleic acids is a workhorse technology underlying high-throughput genome analysis and large-scale chemical mapping for nucleic acid structural inference. Despite the wide availability of CE-based instruments, there remain challenges in leveraging their full power for quantitative analysis of RNA and DNA structure, thermodynamics, and kinetics. In particular, the slow rate and poor automation of available analysis tools have bottlenecked a new generation of studies involving hundreds of CE profiles per experiment. Results: We propose a computational method called high-throughput robust analysis for capillary electrophoresis (HiTRACE) to automate the key tasks in large-scale nucleic acid CE analysis, including the profile alignment that has heretofore been a rate-limiting step in the highest throughput experiments. We illustrate the application of HiTRACE on thirteen data sets representing 4 different RNAs, three chemical modification strategies, and up to 480 single mutant variants; the largest data sets each include 87,360 bands. By applying a series of robust dynamic programming algorithms, HiTRACE outperforms prior tools in terms of alignment and fitting quality, as assessed by measures including the correlation between quantified band intensities between replicate data sets. Furthermore, while the smallest of these data sets required 7 to 10 hours of manual intervention using prior approaches, HiTRACE quantitation of even the largest data sets herein was achieved in 3 to 12 minutes. The HiTRACE method therefore resolves a critical barrier to the efficient and accurate analysis of nucleic acid structure in experiments involving tens of thousands of electrophoretic bands.Comment: Revised to include Supplement. Availability: HiTRACE is freely available for download at http://hitrace.stanford.ed

Advances in vision-based lane detection: algorithms, integration, assessment, and perspectives on ACP-based parallel vision

Lane detection is a fundamental aspect of most current advanced driver assistance systems (ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous vision-based lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system, and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed

Anti-prion drug mPPIg5 inhibits PrP(C) conversion to PrP(Sc).

Prion diseases, also known as transmissible spongiform encephalopathies, are a group of fatal neurodegenerative diseases that include scrapie in sheep, bovine spongiform encephalopathy (BSE) in cattle and Creutzfeldt-Jakob disease (CJD) in humans. The 'protein only hypothesis' advocates that PrP(Sc), an abnormal isoform of the cellular protein PrP(C), is the main and possibly sole component of prion infectious agents. Currently, no effective therapy exists for these diseases at the symptomatic phase for either humans or animals, though a number of compounds have demonstrated the ability to eliminate PrPSc in cell culture models. Of particular interest are synthetic polymers known as dendrimers which possess the unique ability to eliminate PrP(Sc) in both an intracellular and in vitro setting. The efficacy and mode of action of the novel anti-prion dendrimer mPPIg5 was investigated through the creation of a number of innovative bio-assays based upon the scrapie cell assay. These assays were used to demonstrate that mPPIg5 is a highly effective anti-prion drug which acts, at least in part, through the inhibition of PrP(C) to PrP(Sc) conversion. Understanding how a drug works is a vital component in maximising its performance. By establishing the efficacy and method of action of mPPIg5, this study will help determine which drugs are most likely to enhance this effect and also aid the design of dendrimers with anti-prion capabilities for the future

Receptors for Insulin-Like Growth Factor-2 and Androgens as Therapeutic Targets in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) occurs in 10-15% of all breast cancer patients, yet it accounts for about half of all breast cancer deaths. There is an urgent need to identify new antitumor targets to provide additional treatment options for patients afflicted with this aggressive disease. Preclinical evidence suggests a critical role for insulin-like growth factor-2 (IGF2) and androgen receptor (AR) in regulating TNBC progression. To advance this work, a panel of TNBC cell lines was investigated with all cell lines showing significant expression of IGF2. Treatment with IGF2 stimulated cell proliferation in vitro (p &lt; 0.05). Importantly, combination treatments with IGF1R inhibitors BMS-754807 and NVP-AEW541 elicited significant inhibition of TNBC cell proliferation (p &lt; 0.001). Based on Annexin-V binding assays, BMS-754807, NVP-AEW541 and enzalutamide induced TNBC cell death (p &lt; 0.005). Additionally, combination of enzalutamide with BMS-754807 or NVP-AEW541 exerted significant reductions in TNBC proliferation even in cells with low AR expression (p &lt; 0.001). Notably, NVP-AEW541 and BMS-754807 reduced AR levels in BT549 TNBC cells. These results provide evidence that IGF2 promotes TNBC cell viability and proliferation, while inhibition of IGF1R/IR and AR pathways contribute to blockade of TNBC proliferation and promotion of apoptosis in vitro