Elimination of Ligation Dependent Artifacts in T4 RNA Ligase to Achieve High Efficiency and Low Bias MicroRNA Capture

Adapter ligation is a critical first step in many microRNA analysis methods including microarray, qPCR, and sequencing. Previous studies have shown that ligation bias can have dramatic effects on both the fidelity of expression profiles and reproducibility across samples. We have developed a method for high efficiency and low bias microRNA capture by 3′ adapter ligation using T4 RNA ligase that does not require pooled adapters. Using a panel of 20 microRNA, we investigated the effects of ligase type, PEG concentration, ligase amount, adapter concentration, incubation time, incubation temperature, and adapter design on capture efficiency and bias. Of these factors, high PEG% was found to be critical in suppressing ligation bias. We obtained high average capture efficiency and low CV across the 20 microRNA panel, both in idealized buffer conditions (86%±10%) and total RNA spiking conditions (64%±17%). We demonstrate that this method is reliable across microRNA species that previous studies have had difficulty capturing and that our adapter design performs significantly better than the common adapter designs. Further, we demonstrate that the optimization methodology must be specifically designed for minimizing bias in order to obtain the ideal reaction parameters.     

Time-of-flight Secondary Ion Mass Spectrometry of Branched RNA Fragrants: Messenger RNA Splicing Intermedistes

Abstract

The negative ion mass spectra generated by a reflecting time-of-flight mass spectrometer are reported for a series of protected oligonucleotides. Quasimolecular and sequence ions have been detected, and the location and nature of protecting groups have been confirmed.     

Acyclic Nucleosides Containing Two Heterocyclic Bases

Abstract

A new series of acyclic nucleoside derivatives is described which contain two purine or pyrimidine rings (or a combination) attached to glycerol. The analogues described have been tested for activity against herpesviruses and were found to be inactive.     

Modeling the Effect of Amino Acids and Copper on Monoclonal Antibody Productivity and Glycosylation: A Modular Approach

In manufacturing monoclonal antibodies (mAbs), it is crucial to be able to predict how process conditions and supplements affect productivity and quality attributes, especially glycosylation. Supplemental inputs, such as amino acids and trace metals in the media, are reported to affect cell metabolism and glycosylation; quantifying their effects is essential for effective process development. We aim to present and validate, through a commercially relevant cell culture process, a technique for modeling such effects efficiently. While existing models can predict mAb production or glycosylation dynamics under specific process configurations, adapting them to new processes remains challenging, because it involves modifying the model structure and often requires some mechanistic understanding. Here, a modular modeling technique for adapting an existing model for a fed-batch Chinese hamster ovary (CHO) cell culture process without structural modifications or mechanistic insight is presented. Instead, data is used, obtained from designed experimental perturbations in media supplementation, to train and validate a supplemental input effect model, which is used to “patch” the existing model. The combined model can be used for model-based process development to improve productivity and to meet product quality targets more efficiently. The methodology and analysis are generally applicable to other CHO cell lines and cell types.     

cIAPs block Ripoptosome formation, a RIP1/caspase-8 containing intracellular cell death complex differentially regulated by cFLIP isoforms

The intracellular regulation of cell death pathways by cIAPs has been enigmatic. Here we show that loss of cIAPs promotes the spontaneous formation of an intracellular platform that activates either apoptosis or necroptosis. This 2 MDa intracellular complex that we designate "Ripoptosome" is necessary but not sufficient for cell death. It contains RIP1, FADD, caspase-8, caspase-10, and caspase inhibitor cFLIP isoforms. cFLIP(L) prevents Ripoptosome formation, whereas, intriguingly, cFLIP(S) promotes Ripoptosome assembly. When cIAPs are absent, caspase activity is the "rheostat" that is controlled by cFLIP isoforms in the Ripoptosome and decides if cell death occurs by RIP3-dependent necroptosis or caspase-dependent apoptosis. RIP1 is the core component of the complex. As exemplified by our studies for TLR3 activation, our data argue that the?Ripoptosome critically influences the outcome of membrane-bound receptor triggering. The differential quality of cell death mediated by the Ripoptosome may cause important pathophysiological consequences during inflammatory responses.     

The Ripoptosome, a signaling platform that assembles in response to genotoxic stress and loss of IAPs

A better understanding of the mechanisms through which anticancer drugs exert their effects is essential to improve combination therapies. While studying how genotoxic stress kills cancer cells, we discovered a large ～2MDa cell death-inducing platform, referred to as "Ripoptosome." It contains the core components RIP1, FADD, and caspase-8, and assembles in response to genotoxic stress-induced depletion of XIAP, cIAP1 and cIAP2. Importantly, it forms independently of TNF, CD95L/FASL, TRAIL, death-receptors, and mitochondrial pathways. It also forms upon Smac-mimetic (SM) treatment without involvement of autocrine TNF. Ripoptosome assembly requires RIP1's kinase activity and can stimulate caspase-8-mediated apoptosis as well as caspase-independent necrosis. It is negatively regulated by FLIP, cIAP1, cIAP2, and XIAP. Mechanistically, IAPs target components of this complex for ubiquitylation and inactivation. Moreover, we find that etoposide-stimulated Ripoptosome formation converts proinflammatory cytokines into prodeath signals. Together, our observations shed new light on fundamental mechanisms by which chemotherapeutics may kill cancer cells.     

Comparative analyses of pandemic H1N1 and seasonal H1N1, H3N2, and influenza B infections depict distinct clinical pictures in ferrets

Influenza A and B infections are a worldwide health concern to both humans and animals. High genetic evolution rates of the influenza virus allow the constant emergence of new strains and cause illness variation. Since human influenza infections are often complicated by secondary factors such as age and underlying medical conditions, strain or subtype specific clinical features are difficult to assess. Here we infected ferrets with 13 currently circulating influenza strains (including strains of pandemic 2009 H1N1 [H1N1pdm] and seasonal A/H1N1, A/H3N2, and B viruses). The clinical parameters were measured daily for 14 days in stable environmental conditions to compare clinical characteristics. We found that H1N1pdm strains had a more severe physiological impact than all season strains where pandemic A/California/07/2009 was the most clinically pathogenic pandemic strain. The most serious illness among seasonal A/H1N1 and A/H3N2 groups was caused by A/Solomon Islands/03/2006 and A/Perth/16/2009, respectively. Among the 13 studied strains, B/Hubei-Wujiagang/158/2009 presented the mildest clinical symptoms. We have also discovered that disease severity (by clinical illness and histopathology) correlated with influenza specific antibody response but not viral replication in the upper respiratory tract. H1N1pdm induced the highest and most rapid antibody response followed by seasonal A/H3N2, seasonal A/H1N1 and seasonal influenza B (with B/Hubei-Wujiagang/158/2009 inducing the weakest response). Our study is the first to compare the clinical features of multiple circulating influenza strains in ferrets. These findings will help to characterize the clinical pictures of specific influenza strains as well as give insights into the development and administration of appropriate influenza therapeutics.     

Automatic target recognition based on cross-plot

Automatic target recognition that relies on rapid feature extraction of real-time target from photo-realistic imaging will enable efficient identification of target patterns. To achieve this objective, Cross-plots of binary patterns are explored as potential signatures for the observed target by high-speed capture of the crucial spatial features using minimal computational resources. Target recognition was implemented based on the proposed pattern recognition concept and tested rigorously for its precision and recall performance. We conclude that Cross-plotting is able to produce a digital fingerprint of a target that correlates efficiently and effectively to signatures of patterns having its identity in a target repository.     

Complete microscale profiling of tumor microangiogenesis: A microradiological methodology reveals fundamental aspects of tumor angiogenesis and yields an array of quantitative parameters for its characterization

Complete profiling would substantially facilitate the fundamental understanding of tumor angiogenesis and of possible anti-angiogenesis cancer treatments. We developed an integrated synchrotron-based methodology with excellent performances: detection of very small vessels by high spatial resolution (~ 1 μm) and nanoparticle contrast enhancement, in vivo dynamics investigations with high temporal resolution (~ 1 ms), and three-dimensional quantitative morphology parametrization by computer tracing. The smallest (3–10 μm) microvessels were found to constitute > 80% of the tumor vasculature and exhibit many structural anomalies. Practical applications are presented, including vessel microanalysis in xenografted tumors, monitoring the effects of anti-angiogenetic agents and in vivo detection of tumor vascular rheological properties.     

Surface Roughness Detection of Arteries via Texture Analysis of Ultrasound Images for Early Diagnosis of Atherosclerosis

There is a strong research interest in identifying the surface roughness of the carotid arterial inner wall via texture analysis for early diagnosis of atherosclerosis. The purpose of this study is to assess the efficacy of texture analysis methods for identifying arterial roughness in the early stage of atherosclerosis. Ultrasound images of common carotid arteries of 15 normal mice fed a normal diet and 28 apoE^−/− mice fed a high-fat diet were recorded by a high-frequency ultrasound system (Vevo 2100, frequency: 40 MHz). Six different texture feature sets were extracted based on the following methods: first-order statistics, fractal dimension texture analysis, spatial gray level dependence matrix, gray level difference statistics, the neighborhood gray tone difference matrix, and the statistical feature matrix. Statistical analysis indicates that 11 of 19 texture features can be used to distinguish between normal and abnormal groups (p<0.05). When the 11 optimal features were used as inputs to a support vector machine classifier, we achieved over 89% accuracy, 87% sensitivity and 93% specificity. The accuracy, sensitivity and specificity for the k-nearest neighbor classifier were 73%, 75% and 70%, respectively. The results show that it is feasible to identify arterial surface roughness based on texture features extracted from ultrasound images of the carotid arterial wall. This method is shown to be useful for early detection and diagnosis of atherosclerosis.