Development of Solid-State Nanopore Technology for Life Detection

Biomarkers for life on Earth are an important starting point to guide the search for life elsewhere. However, the search for life beyond Earth should incorporate technologies capable of recognizing an array of potential biomarkers beyond what we see on Earth, in order to minimize the risk of false negatives from life detection missions. With this in mind, charged linear polymers may be a universal signature for life, due to their ability to store information while also inherently reducing the tendency of complex tertiary structure formation that significantly inhibit replication. Thus, these molecules are attractive targets for biosignature detection as potential "self-sustaining chemical signatures." Examples of charged linear polymers, or polyelectrolytes, include deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) as well as synthetic polyelectrolytes that could potentially support life, including threose nucleic acid (TNA) and other xenonucleic acids (XNAs). Nanopore analysis is a novel technology that has been developed for singlemolecule sequencing with exquisite single nucleotide resolution which is also well-suited for analysis of polyelectrolyte molecules. Nanopore analysis has the ability to detect repeating sequences of electrical charges in organic linear polymers, and it is not molecule- specific (i.e. it is not restricted to only DNA or RNA). In this sense, it is a better life detection technique than approaches that are based on specific molecules, such as the polymerase chain reaction (PCR), which requires that the molecule being detected be composed of DNA

Implementing EM and Viterbi algorithms for Hidden Markov Model in linear memory

<p>Abstract</p> <p>Background</p> <p>The Baum-Welch learning procedure for Hidden Markov Models (HMMs) provides a powerful tool for tailoring HMM topologies to data for use in knowledge discovery and clustering. A linear memory procedure recently proposed by <it>Miklós, I. and Meyer, I.M. </it>describes a memory sparse version of the Baum-Welch algorithm with modifications to the original probabilistic table topologies to make memory use independent of sequence length (and linearly dependent on state number). The original description of the technique has some errors that we amend. We then compare the corrected implementation on a variety of data sets with conventional and checkpointing implementations.</p> <p>Results</p> <p>We provide a correct recurrence relation for the emission parameter estimate and extend it to parameter estimates of the Normal distribution. To accelerate estimation of the prior state probabilities, and decrease memory use, we reverse the originally proposed forward sweep. We describe different scaling strategies necessary in all real implementations of the algorithm to prevent underflow. In this paper we also describe our approach to a linear memory implementation of the Viterbi decoding algorithm (with linearity in the sequence length, while memory use is approximately independent of state number). We demonstrate the use of the linear memory implementation on an extended Duration Hidden Markov Model (DHMM) and on an HMM with a spike detection topology. Comparing the various implementations of the Baum-Welch procedure we find that the checkpointing algorithm produces the best overall tradeoff between memory use and speed. In cases where sequence length is very large (for Baum-Welch), or state number is very large (for Viterbi), the linear memory methods outlined may offer some utility.</p> <p>Conclusion</p> <p>Our performance-optimized Java implementations of Baum-Welch algorithm are available at <url>http://logos.cs.uno.edu/~achurban</url>. The described method and implementations will aid sequence alignment, gene structure prediction, HMM profile training, nanopore ionic flow blockades analysis and many other domains that require efficient HMM training with EM.</p

Unzipping Kinetics of Double-Stranded DNA in a Nanopore

We studied the unzipping kinetics of single molecules of double-stranded DNA by pulling one of their two strands through a narrow protein pore. PCR analysis yielded the first direct proof of DNA unzipping in such a system. The time to unzip each molecule was inferred from the ionic current signature of DNA traversal. The distribution of times to unzip under various experimental conditions fit a simple kinetic model. Using this model, we estimated the enthalpy barriers to unzipping and the effective charge of a nucleotide in the pore, which was considerably smaller than previously assumed.Comment: 10 pages, 5 figures, Accepted: Physics Review Letter

Analysis of nanopore detector measurements using Machine-Learning methods, with application to single-molecule kinetic analysis

<p>Abstract</p> <p>Background</p> <p>A nanopore detector has a nanometer-scale trans-membrane channel across which a potential difference is established, resulting in an ionic current through the channel in the pA-nA range. A distinctive channel current blockade signal is created as individually "captured" DNA molecules interact with the channel and modulate the channel's ionic current. The nanopore detector is sensitive enough that nearly identical DNA molecules can be classified with very high accuracy using machine learning techniques such as Hidden Markov Models (HMMs) and Support Vector Machines (SVMs).</p> <p>Results</p> <p>A non-standard implementation of an HMM, emission inversion, is used for improved classification. Additional features are considered for the feature vector employed by the SVM for classification as well: The addition of a single feature representing spike density is shown to notably improve classification results. Another, much larger, feature set expansion was studied (2500 additional features instead of 1), deriving from including all the HMM's transition probabilities. The expanded features can introduce redundant, noisy information (as well as diagnostic information) into the current feature set, and thus degrade classification performance. A hybrid Adaptive Boosting approach was used for feature selection to alleviate this problem.</p> <p>Conclusion</p> <p>The methods shown here, for more informed feature extraction, improve both classification and provide biologists and chemists with tools for obtaining a better understanding of the kinetic properties of molecules of interest.</p

Duration learning for analysis of nanopore ionic current blockades

<p>Abstract</p> <p>Background</p> <p>Ionic current blockade signal processing, for use in nanopore detection, offers a promising new way to analyze single molecule properties, with potential implications for DNA sequencing. The alpha-Hemolysin transmembrane channel interacts with a translocating molecule in a nontrivial way, frequently evidenced by a complex ionic flow blockade pattern. Typically, recorded current blockade signals have several levels of blockade, with various durations, all obeying a fixed statistical profile for a given molecule. Hidden Markov Model (HMM) based duration learning experiments on artificial two-level Gaussian blockade signals helped us to identify proper modeling framework. We then apply our framework to the real multi-level DNA hairpin blockade signal.</p> <p>Results</p> <p>The identified upper level blockade state is observed with durations that are geometrically distributed (consistent with an a physical decay process for remaining in any given state). We show that mixture of convolution chains of geometrically distributed states is better for presenting multimodal long-tailed duration phenomena. Based on learned HMM profiles we are able to classify 9 base-pair DNA hairpins with accuracy up to 99.5% on signals from same-day experiments.</p> <p>Conclusion</p> <p>We have demonstrated several implementations for <it>de novo </it>estimation of duration distribution probability density function with HMM framework and applied our model topology to the real data. The proposed design could be handy in molecular analysis based on nanopore current blockade signal.</p

Nanopore-based kinetics analysis of individual antibody-channel and antibody-antigen interactions

<p>Abstract</p> <p>Background</p> <p>The UNO/RIC Nanopore Detector provides a new way to study the binding and conformational changes of individual antibodies. Many critical questions regarding antibody function are still unresolved, questions that can be approached in a new way with the nanopore detector.</p> <p>Results</p> <p>We present evidence that different forms of channel blockade can be associated with the same antibody, we associate these different blockades with different orientations of "capture" of an antibody in the detector's nanometer-scale channel. We directly detect the presence of antibodies via reductions in channel current. Changes to blockade patterns upon addition of antigen suggest indirect detection of antibody/antigen binding. Similarly, DNA-hairpin anchored antibodies have been studied, where the DNA linkage is to the carboxy-terminus at the base of the antibody's Fc region, with significantly fewer types of (lengthy) capture blockades than was observed for free (un-bound) IgG antibody. The introduction of chaotropic agents and its effects on protein-protein interactions have also been observed.</p> <p>Conclusion</p> <p>Nanopore-based approaches may eventually provide a direct analysis of the complex conformational "negotiations" that occur upon binding between proteins.</p

A novel, fast, HMM-with-Duration implementation – for application with a new, pattern recognition informed, nanopore detector

<p>Abstract</p> <p>Background</p> <p>Hidden Markov Models (HMMs) provide an excellent means for structure identification and feature extraction on stochastic sequential data. An HMM-with-Duration (HMMwD) is an HMM that can also exactly model the hidden-label length (recurrence) distributions – while the regular HMM will impose a best-fit geometric distribution in its modeling/representation.</p> <p>Results</p> <p>A Novel, Fast, HMM-with-Duration (HMMwD) Implementation is presented, and experimental results are shown that demonstrate its performance on two-state synthetic data designed to model Nanopore Detector Data. The HMMwD experimental results are compared to (i) the ideal model and to (ii) the conventional HMM. Its accuracy is clearly an improvement over the standard HMM, and matches that of the ideal solution in many cases where the standard HMM does not. Computationally, the new HMMwD has all the speed advantages of the conventional (simpler) HMM implementation. In preliminary work shown here, HMM feature extraction is then used to establish the first pattern recognition-informed (PRI) sampling control of a Nanopore Detector Device (on a "live" data-stream).</p> <p>Conclusion</p> <p>The improved accuracy of the new HMMwD implementation, at the same order of computational cost as the standard HMM, is an important augmentation for applications in gene structure identification and channel current analysis, especially PRI sampling control, for example, where speed is essential. The PRI experiment was designed to inherit the high accuracy of the well characterized and distinctive blockades of the DNA hairpin molecules used as controls (or blockade "test-probes"). For this test set, the accuracy inherited is 99.9%.</p

Management of adult-onset Still's disease:evidence- and consensus-based recommendations by experts

Objectives: Adult-onset Still's disease (AOSD) is a rare condition characterized by fevers, rash, and arthralgia/arthritis; most doctors treating AOSD in the Netherlands treat &lt;5 patients per year. Currently, there is no internationally accepted treatment guideline for AOSD. The objectives of this study were to conduct a Delphi panel aimed at reaching consensus about diagnostic and treatment strategies for patients with AOSD and to use the outcomes as a basis for a treatment algorithm. Methods: The Delphi panel brought together 18 AOSD experts: rheumatologists, internists and paediatricians. The Delphi process consisted of three rounds. In the first two rounds, online lists of questions and statements were completed. In the third round, final statements were discussed during a virtual meeting and a final vote took place. Consensus threshold was set at 80%. Two targeted literature searches were performed identifying the level of evidence of the consensus-based statements. Results: Consensus was reached on 29 statements, including statements related to diagnosis and diagnostic tests, definition of response and remission, the therapy, the use of methotrexate and tapering of treatment. The panel consented on reduction of the use of glucocorticoids to avoid side effects, and preferred the use of biologics over conventional treatment. The role of IL-1 and IL-6 blocking agents was considered important in the treatment of AOSD. Conclusion: In this Delphi panel, a high level of consensus was achieved on recommendations for diagnosis and therapy of AOSD that can serve as a basis for a treatment guideline.</p

Multinational evidence-based recommendations on how to investigate and follow-up undifferentiated peripheral inflammatory arthritis: integrating systematic literature research and expert opinion of a broad international panel of rheumatologists in the 3E Initiative

Methods: 697 rheumatologists from 17 countries participated in the 3E (Evidence, Expertise, Exchange) Initiative of 2008–9 consisting of three separate rounds of discussions and modified Delphi votes. In the first round 10 clinical questions were selected. A bibliographic team systematically searched Medline, Embase, the Cochrane Library and ACR/EULAR 2007–2008 meeting abstracts. Relevant articles were reviewed for quality assessment, data extraction and synthesis. In the second round each country elaborated a set of national recommendations. Finally, multinational recommendations were formulated and agreement among the participants and the potential impact on their clinical practice was assessed. Results: A total of 39 756 references were identified, of which 250 were systematically reviewed. Ten multinational key recommendations about the investigation and follow-up of UPIA were formulated. One recommendation addressed differential diagnosis and investigations prior to establishing the operational diagnosis of UPIA, seven recommendations related to the diagnostic and prognostic value of clinical and laboratory assessments in established UPIA (history and physical examination, acute phase reactants, autoantibodies, radiographs, MRI and ultrasound, genetic markers and synovial biopsy), one recommendation highlighted predictors of persistence (chronicity) and the final recommendation addressed monitoring of clinical disease activity in UPIA. Conclusions: Ten recommendations on how to investigate and follow-up UPIA in the clinical setting were developed. They are evidence-based and supported by a large panel of rheumatologists, thus enhancing their validity and practical use