Design principles for riboswitch function

Scientific and technological advances that enable the tuning of integrated regulatory components to match network and system requirements are critical to reliably control the function of biological systems. RNA provides a promising building block for the construction of tunable regulatory components based on its rich regulatory capacity and our current understanding of the sequence–function relationship. One prominent example of RNA-based regulatory components is riboswitches, genetic elements that mediate ligand control of gene expression through diverse regulatory mechanisms. While characterization of natural and synthetic riboswitches has revealed that riboswitch function can be modulated through sequence alteration, no quantitative frameworks exist to investigate or guide riboswitch tuning. Here, we combined mathematical modeling and experimental approaches to investigate the relationship between riboswitch function and performance. Model results demonstrated that the competition between reversible and irreversible rate constants dictates performance for different regulatory mechanisms. We also found that practical system restrictions, such as an upper limit on ligand concentration, can significantly alter the requirements for riboswitch performance, necessitating alternative tuning strategies. Previous experimental data for natural and synthetic riboswitches as well as experiments conducted in this work support model predictions. From our results, we developed a set of general design principles for synthetic riboswitches. Our results also provide a foundation from which to investigate how natural riboswitches are tuned to meet systems-level regulatory demands

Aptamer-based multiplexed proteomic technology for biomarker discovery

Interrogation of the human proteome in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology. We present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 [mu]L of serum or plasma). Our current assay allows us to measure ~800 proteins with very low limits of detection (1 pM average), 7 logs of overall dynamic range, and 5% average coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding DNA aptamer concentration signature, which is then quantified with a DNA microarray. In essence, our assay takes advantage of the dual nature of aptamers as both folded binding entities with defined shapes and unique sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to discover unique protein signatures characteristic of various disease states. More generally, we describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine

Retuning of Inferior Colliculus Neurons Following Spiral Ganglion Lesions: A Single-Neuron Model of Converging Inputs

Lesions of spiral ganglion cells, representing a restricted sector of the auditory nerve array, produce immediate changes in the frequency tuning of inferior colliculus (IC) neurons. There is a loss of excitation at the lesion frequencies, yet responses to adjacent frequencies remain intact and new regions of activity appear. This leads to immediate changes in tuning and in tonotopic progression. Similar effects are seen after different methods of peripheral damage and in auditory neurons in other nuclei. The mechanisms that underlie these postlesion changes are unknown, but the acute effects seen in IC strongly suggest the “unmasking” of latent inputs by the removal of inhibition. In this study, we explore computational models of single neurons with a convergence of excitatory and inhibitory inputs from a range of characteristic frequencies (CFs), which can simulate the narrow prelesion tuning of IC neurons, and account for the changes in CF tuning after a lesion. The models can reproduce the data if inputs are aligned relative to one another in a precise order along the dendrites of model IC neurons. Frequency tuning in these neurons approximates that seen physiologically. Removal of inputs representing a narrow range of frequencies leads to unmasking of previously subthreshold excitatory inputs, which causes changes in CF. Conversely, if all of the inputs converge at the same point on the cell body, receptive fields are broad and unmasking rarely results in CF changes. However, if the inhibition is tonic with no stimulus-driven component, then unmasking can still produce changes in CF

Role of HPV Genotype, Multiple Infections, and Viral Load on the Risk of High-Grade Cervical Neoplasia

Background:HPV testing provides a much more sensitive method of detection for high-grade lesions than cytology, but specificity is low. Here we explore the extent to which full HPV genotyping, viral load and multiplicity of types can be used to improve specificity. Methods:A population-based sample of 47,120 women undergoing cervical screening were tested for 13 high-risk HPV genotypes. Positive predictive values (PPV) for CIN grade 2 or worse (CIN2+; N=3449) and CIN3 or worse (CIN3+; N=1475) over three years of follow-up were estimated for HPV genotype and viral load. Weighted multivariate logistic regression models were used to estimate the odds of CIN2+ or CIN3+ according to genotype, multiplicity of types and viral load. Results:High-risk HPV was detected in 15.4% of women. A hierarchy of HPV genotypes based on sequentially maximizing PPVs for CIN3+ found HPV16>33>31 to be the most predictive, followed sequentially by HPV18>35>58>45>52>59>51>39>56>68. After adjusting for higher ranked genotypes, multiple HPV infections added little to risk prediction. High viral loads for HPV18, 35, 52 and 58 carried more risk than low viral loads for HPV16, 31 and 33. High viral load for HPV16 was significantly more associated with CIN3+ than low viral load. Conclusions:HPV genotype and viral load, but not multiplicity of HPV infections, are important predictors of CIN2+ and CIN3+. Impact:The ability to identify women at higher risk of CIN2+ and CIN3+ based on both HPV genotype and viral load could be important for individualising triage plans, particularly as HPV becomes the primary screening test

Ligand-induced sequestering of branchpoint sequence allows conditional control of splicing

<p>Abstract</p> <p>Background</p> <p>Despite tremendous progress in understanding the mechanisms of constitutive and alternative splicing, an important and widespread step along the gene expression pathway, our ability to deliberately regulate gene expression at this step remains rudimentary. The present study was performed to investigate whether a theophylline-dependent "splice switch" that sequesters the branchpoint sequence (BPS) within RNA-theophylline complex can regulate alternative splicing.</p> <p>Results</p> <p>We constructed a series of pre-mRNAs in which the BPS was inserted within theophylline aptamer. We show that theophylline-induced sequestering of BPS inhibits pre-mRNA splicing both in vitro and in vivo in a dose-dependent manner. Several lines of evidence suggest that theophylline-dependent inhibition of splicing is highly specific, and thermodynamic stability of RNA-theophylline complex as well as the location of BPS within this complex affects the efficiency of splicing inhibition. Finally, we have constructed an alternative splicing model pre-mRNA substrate in which theophylline caused exon skipping both in vitro and in vivo, suggesting that a small molecule-RNA interaction can modulate alternative splicing.</p> <p>Conclusion</p> <p>These findings provide the ability to control splicing pattern at will and should have important implications for basic, biotechnological, and biomedical research.</p

Identification of Novel Therapeutic Targets in Microdissected Clear Cell Ovarian Cancers

Clear cell ovarian cancer is an epithelial ovarian cancer histotype that is less responsive to chemotherapy and carries poorer prognosis than serous and endometrioid histotypes. Despite this, patients with these tumors are treated in a similar fashion as all other ovarian cancers. Previous genomic analysis has suggested that clear cell cancers represent a unique tumor subtype. Here we generated the first whole genomic expression profiling using epithelial component of clear cell ovarian cancers and normal ovarian surface specimens isolated by laser capture microdissection. All the arrays were analyzed using BRB ArrayTools and PathwayStudio software to identify the signaling pathways. Identified pathways validated using serous, clear cell cancer cell lines and RNAi technology. In vivo validations carried out using an orthotopic mouse model and liposomal encapsulated siRNA. Patient-derived clear cell and serous ovarian tumors were grafted under the renal capsule of NOD-SCID mice to evaluate the therapeutic potential of the identified pathway. We identified major activated pathways in clear cells involving in hypoxic cell growth, angiogenesis, and glucose metabolism not seen in other histotypes. Knockdown of key genes in these pathways sensitized clear cell ovarian cancer cell lines to hypoxia/glucose deprivation. In vivo experiments using patient derived tumors demonstrate that clear cell tumors are exquisitely sensitive to antiangiogenesis therapy (i.e. sunitinib) compared with serous tumors. We generated a histotype specific, gene signature associated with clear cell ovarian cancer which identifies important activated pathways critical for their clinicopathologic characteristics. These results provide a rational basis for a radically different treatment for ovarian clear cell patients

State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data

Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods to neural spike data simultaneously recorded from the motor cortex of an awake monkey and demonstrate that the higher-order spike correlation organizes dynamically in relation to a behavioral demand