Neighboring Probes Research Articles

Intelligent Electrochemical Point-of-Care Test Method with Interface Control Based on DNA Pyramids: Aflatoxin B1 Detection in Food and the Environment.

Sensitive, intelligent point-of-care test (iPOCT) methods for small molecules like aflatoxin B1 (AFB1) are urgently needed for food and the environment. The challenge remains of surface control in iPOCT. Herein, we developed an electrochemical sensor based on the DNA pyramid (DNP), combining a smartphone, app, and mobile electrochemical workstations to detect AFB1. The DNP's structure can reduce local overcrowding and entanglement between neighboring probes, control the density and orientation of recognition probes (antibodies), produce uniform and orientational surface assemblies, and improve antigen-antibody-specific recognition and binding efficiency. Simultaneously, the hollow structure of the DNP enhances the electron transfer capacity and increases the sensitivity of electrochemical detection. In this work, the biosensor based on DNP was first combined with electrochemical (Ec) iPOCT to simultaneously achieve ordered interface modulation of recognition probes and intelligent detection of AFB1. Under optimal conditions, we found a detection limit of 3 pg/mL and a linear range of 0.006-30 ng/mL (R2 = 0.995). Further, using peanut, soybean, corn, and lake water as complex matrices, it recorded recoveries of 82.15-100.53%, excellent selectivity, acceptable stability, and good reproducibility. Finally, this Ec iPOCT provides consistent results compared to the high-performance liquid chromatography-tandem mass spectrometry method.

Abstract 843: Epigenomic disruption of adipogenic regulators in dedifferentiated liposarcoma

Abstract Well-differentiated liposarcomas (WDLPS) and dedifferentiated liposarcomas (DDLPS) are the most common soft-tissue sarcomas in adults. They develop as a result of a disturbance in adipogenic differentiation however, the epigenomic changes underlying liposarcomagenesis are unknown. Here, we aimed to elucidate the role of epigenomic alterations in the initiation of DDLPS. Genome-wide DNA methylation profiles of 15 liposarcoma (6 WDLPS and 9 DDLPS) samples and 6 normal adipose tissue samples were obtained using the Infinium MethylationEPIC BeadChip. After grouping neighboring probes into genomic blocks (GBs, < 500 bp), principal component analysis of all the CpG GBs (n = 535,684) was conducted. Each of normal adipose tissue and WDLPS grouped closely while DDLPS distributed broadly, indicating heterogeneous methylation profiles among the DDLPS samples. Unsupervised hierarchical cluster analysis of enhancers (2,000 GBs with the high SD of 23,478 GBs) classified the samples into three tissue types, whereas that of promoters (1,000 GBs with the high SD of 8,668 GBs) could not classify the DDLPS samples distinctly. The Jonckheere-Terpstra trend test revealed 9,945 hypermethylated (p < 0.01, Δβ > 0.2) GBs in DDLPS compared to those in normal adipose tissues and WDLPS samples. Based upon a report on enhancers in adipocytes (Mikkelsen et al. Cell 143, 156-169. 2010), the hypermethylated GBs in DDLPS were enriched with typical- (2.2%: whole genome 1.2%) and super- (13.5%: whole genome 7.0%) enhancers. Genes involved in adipogenesis, including PPARG2, the master regulator of adipogenesis, and its target genes (FABP4 and PLIN1), were aberrantly methylated at the enhancers and repressed in DDLPS. Conversely, promoters were not enriched among hypermethylated GBs in DDLPS (1.2%: whole genome 4.1%). Taken together, these data indicate that aberrant methylation at the enhancers of adipogenic genes might be crucial for the generation of DDLPS. Evaluation of a demethylating agent in combination with a PPARγ agonist as a potential therapeutic is in progress. Citation Format: Hironori Takamatsu, Naoko Hattori, Naofumi Asano, Naoko Iida, Akihiko Yoshida, Eisuke Kobayashi, Robert Nakayama, Morio Matsumoto, Masaya Nakamura, Akira Kawai, Toshikazu Ushijima. Epigenomic disruption of adipogenic regulators in dedifferentiated liposarcoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 843.

Long intergenic noncoding RNA 299 methylation in peripheral blood is a biomarker for triple-negative breast cancer.

To identify DNA methylation biomarkers in peripheral blood samples from triple-negative breast cancer (TNBC) patients. We conducted an epigenome-wide association study (EWAS): the most promising markers were identified in 233 TNBC case-control pairs (discovery set) and subsequently validated in an independent validation set (57 TNBC patients and 124 controls). cg06588802 (LINC00299/ID2) showed a higher methylation in TNBC patients compared with controls (discovery set: 3% increase, p-value=0.0009; validation set: 2% increase, p-value=0.01). Consistent results at four neighboring methylation probes and the strong negative correlation (rho=-0.93) with LINC00299 expression add plausibility to this result. Hypermethylation of LINC00299 in peripheral blood may constitute a useful circulating biomarker for TNBC.

Bioinspired Trans‐Scale Functional Interface for Enhanced Enzymatic Dynamics and Ultrasensitive Detection of microRNA

AbstractProtein interactions with specific nucleic acid sequences are crucial in cell growth. Inspired by such binding events that often occur at nanoscale biointerface, here a trans‐scale functional interface capable of considerably enhancing in vitro DNA‐enzyme interaction is reported. Using a screen‐printed electrode with nanoroughened carbon surface, the high‐curvature gold nanostructures in a single electrodeposition step can be programmed. In this process, a synergistic effect is found between nanoroughened carbon and polyelectrolyte multilayer enabling the formation of high‐stability and high‐curvature nanostructures. More importantly, these fractal nanostructures effectively overcome neighboring probes aggregation at high density and allow the probes to be more freely accessed by target molecules. As compared to its planar counterparts, this nanostructuring interface demonstrates faster enzymatic dynamics that enables ultrasensitive detection of microRNA with a detection limit of 35 × 10−18 m. Such an efficient trans‐scale biosensing interface has also accurately differentiated the patients with rheumatic arthritis from the health ones, signifying its great potential in precision medicine.

A linear DNA probe as an alternative to a molecular beacon for improving the sensitivity of a homogenous fluorescence biosensing platform for DNA detection using target-primed rolling circle amplification

Linear single-labeled DNA probes are used in this RCA-based fluorescence strategy for DNA detection, which could effectively avoid the fluorescence quenching between neighboring signal probes using hairpin probe as signal probe.

Ultrasensitive IgG quantification using DNA nano-pyramids

In addition to being a repository of genetic information, DNA is a bio-polymer that can be formed into various nanostructures. This profound ability to engineer various moieties has expanded its role from data storage to a structural biomaterial for sensing applications. In this study, we anchored DNA nano-pyramids (DPs) to gold electrodes for the electrochemical sensing of immunoglobulin G (IgG), an important antibody produced in response to infection. The pyramidal DNA structure not only avoids entanglement with neighboring probes through the use of spatially separating pendant probes but also reduces the local overcrowding effect with the overall enhanced packing of targets. The results from electrochemical impedance spectroscopy measurements also show that DP layer has better conductivity, with the hollow structure further facilitating electron transfer and increasing the sensitivity of electrochemical detection. We are able to selectively detect IgG in the presence of other proteins in an analyte solution. The limit of detection was 2.8 pg ml(-1). Our ferrocene-labeled sandwich immunoassay works at 37 degrees C under a neutral pH environment. It also produces stable and reproducible signals even after storage for 1 week at 4 degrees C, further demonstrating the potential of this sensing system for clinical applications.

Cerebral functional imaging using near-infrared spectroscopy during repeated performances of motor rehabilitation tasks tested on healthy subjects.

To investigate the relationship between the frontal and sensorimotor cortices and motor learning, hemodynamic responses were recorded from the frontal and sensorimotor cortices using functional near infrared spectroscopy (NIRS) while healthy subjects performed motor learning tasks used in rehabilitation medicine. Whole-head NIRS recordings indicated that response latencies in the anterior dorsomedial prefrontal cortex (aDMPFC) were shorter than in other frontal and parietal areas. Furthermore, the increment rate of the hemodynamic responses in the aDMPFC across the eight repeated trials significantly correlated with those in the other areas, as well as with the improvement rate of task performance across the 8 repeated trials. In the second experiment, to dissociate scalp- and brain-derived hemodynamic responses, hemodynamic responses were recorded from the head over the aDMPFC using a multi-distance probe arrangement. Six probes (a single source probe and 5 detectors) were linearly placed 6 mm apart from each of the neighboring probes. Using independent component analyses of hemodynamic signals from the 5 source-detector pairs, we dissociated scalp- and brain-derived components of the hemodynamic responses. Hemodynamic responses corrected for scalp-derived responses over the aDMPFC significantly increased across the 8 trials and correlated with task performance. In the third experiment, subjects were required to perform the same task with and without transcranial direct current stimulation (tDCS) of the aDMPFC before the task. The tDCS significantly improved task performance. These results indicate that the aDMPFC is crucial for improved performance in repetitive motor learning.

A novel method for identification and quantification of consistently differentially methylated regions.

Advances in biotechnology have resulted in large-scale studies of DNA methylation. A differentially methylated region (DMR) is a genomic region with multiple adjacent CpG sites that exhibit different methylation statuses among multiple samples. Many so-called “supervised” methods have been established to identify DMRs between two or more comparison groups. Methods for the identification of DMRs without reference to phenotypic information are, however, less well studied. An alternative “unsupervised” approach was proposed, in which DMRs in studied samples were identified with consideration of nature dependence structure of methylation measurements between neighboring probes from tiling arrays. Through simulation study, we investigated effects of dependencies between neighboring probes on determining DMRs where a lot of spurious signals would be produced if the methylation data were analyzed independently of the probe. In contrast, our newly proposed method could successfully correct for this effect with a well-controlled false positive rate and a comparable sensitivity. By applying to two real datasets, we demonstrated that our method could provide a global picture of methylation variation in studied samples. R source codes to implement the proposed method were freely available at http://www.csjfann.ibms.sinica.edu.tw/eag/programlist/ICDMR/ICDMR.html.

ExploreMaps: Efficient construction and ubiquitous exploration of panoramic view graphs of complex 3D environments

AbstractWe introduce a novel efficient technique for automatically transforming a generic renderable 3D scene into a simple graph representation named ExploreMaps, where nodes are nicely placed point of views, called probes, and arcs are smooth paths between neighboring probes. Each probe is associated with a panoramic image enriched with preferred viewing orientations, and each path with a panoramic video. Our GPU‐accelerated unattended construction pipeline distributes probes so as to guarantee coverage of the scene while accounting for perceptual criteria before finding smooth, good looking paths between neighboring probes. Images and videos are precomputed at construction time with off‐line photorealistic rendering engines, providing a convincing 3D visualization beyond the limits of current real‐time graphics techniques. At run‐time, the graph is exploited both for creating automatic scene indexes and movie previews of complex scenes and for supporting interactive exploration through a low‐DOF assisted navigation interface and the visual indexing of the scene provided by the selected viewpoints. Due to negligible CPU overhead and very limited use of GPU functionality, real‐time performance is achieved on emerging web‐based environments based on WebGL even on low‐powered mobile devices.

Reply to Suderman et al.: Importance of accounting for blood cell composition in epigenetic studies

In our paper (1), we primarily questioned whether the statistical approach used by Borghol et al. (2) was justified, because treating neighboring probes as independent can lead to inflated P value significance. We are glad to see that in their Letter, Borghol et al. do not dispute this (3). We are also pleased that Borghol et al. concur with our concerns regarding their description of procedures for determining the false-discovery rate (FDR) (1, 3). A thorough description of the FDR approach is of great importance in studies that measure many …

DNAzyme-based magneto-controlled electronic switch for picomolar detection of lead (II) coupling with DNA-based hybridization chain reaction

A novel magneto-controlled electrochemical DNA biosensor is designed for the ultrasensitive detection of lead coupling a lead-specific DNAzyme with DNA-based hybridization chain reaction (HCR). To construct such a magnetic lead sensor, DNAzyme-based molecular beacons, selective to cleavage in the presence of Pb2+, are initially immobilized onto magnetic beads, which were used as the recognition elements. Upon addition of target lead, catalytic cleavage of substrate DNA segments in the double-stranded DNAzymes results in the capture of the initiator strands via the conjugated catalytic strands on magnetic beads. The captured DNA initiator strands trigger the hybridization chain reaction between two alternating hairpin DNA structures labeled with ferrocene to form a nicked double-helix on the magnetic beads. Numerous ferrocene molecules are formed on the neighboring probes, each of which produces an electrochemical signal within the applied potential. Under optimal conditions, the electrochemical signal of the magnetic lead sensor increases with the increasing lead level in the sample, and exhibits a linear response over a Pb2+ concentration range of 0.1–75nM with a detection limit of 37pM. Quantitative measurement of Pb2+ in the complex sample demonstrates the selectivity of the sensor scheme and points favorably to the application of such technologies to the analysis of environmental samples. The unique combination of a DNAzyme with hybridization chain reaction makes it possible to change the DNAzyme to select for other compounds of interest. This work represents the initial steps toward the creation of a robust field sensor for lead in groundwater or drinking water.

Incorporating Genomic Annotation into a Hidden Markov Model for DNA Methylation Tiling Array Data

Abstract DNA methylation is an epigenetic modification that plays an important role in many biological processes. To better understand this epigenetic mechanism, genome-wide investigations of DNA methylation can be conducted via tiling array technology. In such DNA methylation profiling studies, statistical methods are employed to estimate the DNA methylation status of each probe represented on the tiling array. Although many of the current methods account for the inherent dependency between neighboring probes, they do not effectively utilize the wealth of information available in genomic annotation databases. Since previous studies indicate that DNA methylation patterns of some organisms differ by genomic element (e.g., gene, intergenic region), such genomic annotation information may be useful in predicting DNA methylation status. In this work, a novel statistical model is proposed within the hidden Markov model framework to incorporate genomic annotation information in the prediction of DNA methylation status. The advantages of incorporating genomic annotation via the proposed method are investigated through a simulation study and real data applications.

A continuous-index Bayesian hidden Markov model for prediction of nucleosome positioning in genomic DNA

Nucleosomes are units of chromatin structure, consisting of DNA sequence wrapped around proteins called "histones." Nucleosomes occur at variable intervals throughout genomic DNA and prevent transcription factor (TF) binding by blocking TF access to the DNA. A map of nucleosomal locations would enable researchers to detect TF binding sites with greater efficiency. Our objective is to construct an accurate genomic map of nucleosome-free regions (NFRs) based on data from high-throughput genomic tiling arrays in yeast. These high-volume data typically have a complex structure in the form of dependence on neighboring probes as well as underlying DNA sequence, variable-sized gaps, and missing data. We propose a novel continuous-index model appropriate for non-equispaced tiling array data that simultaneously incorporates DNA sequence features relevant to nucleosome formation. Simulation studies and an application to a yeast nucleosomal assay demonstrate the advantages of using the new modeling framework, as well as its robustness to distributional misspecifications. Our results reinforce the previous biological hypothesis that higher-order nucleotide combinations are important in distinguishing nucleosomal regions from NFRs.

Pyrene‐Excimer Probes Based on the Hybridization Chain Reaction for the Detection of Nucleic Acids in Complex Biological Fluids

Make it a double: Complementary probes with two pyrene labels were engineered for the amplification of a target DNA sequence. In the stem-closed conformation of the probes in the absence of the target, the two pyrene moieties were separated. The target propagated hybridization chain reactions to bring pyrene moieties on neighboring probes close together to form fluorescent pyrene excimers (see picture).

HAT: Hypergeometric Analysis of Tiling-arrays with application to promoter-GeneChip data

BackgroundTiling-arrays are applicable to multiple types of biological research questions. Due to its advantages (high sensitivity, resolution, unbiased), the technology is often employed in genome-wide investigations. A major challenge in the analysis of tiling-array data is to define regions-of-interest, i.e., contiguous probes with increased signal intensity (as a result of hybridization of labeled DNA) in a region. Currently, no standard criteria are available to define these regions-of-interest as there is no single probe intensity cut-off level, different regions-of-interest can contain various numbers of probes, and can vary in genomic width. Furthermore, the chromosomal distance between neighboring probes can vary across the genome among different arrays.ResultsWe have developed Hypergeometric Analysis of Tiling-arrays (HAT), and first evaluated its performance for tiling-array datasets from a Chromatin Immunoprecipitation study on chip (ChIP-on-chip) for the identification of genome-wide DNA binding profiles of transcription factor Cebpa (used for method comparison). Using this assay, we can refine the detection of regions-of-interest by illustrating that regions detected by HAT are more highly enriched for expected motifs in comparison with an alternative detection method (MAT). Subsequently, data from a retroviral insertional mutagenesis screen were used to examine the performance of HAT among different applications of tiling-array datasets. In both studies, detected regions-of-interest have been validated with (q)PCR.ConclusionsWe demonstrate that HAT has increased specificity for analysis of tiling-array data in comparison with the alternative method, and that it accurately detects regions-of-interest in two different applications of tiling-arrays. HAT has several advantages over previous methods: i) as there is no single cut-off level for probe-intensity, HAT can detect regions-of-interest at various thresholds, ii) it can detect regions-of-interest of any size, iii) it is independent of probe-resolution across the genome, and across tiling-array platforms and iv) it employs a single user defined parameter: the significance level. Regions-of-interest are detected by computing the hypergeometric-probability, while controlling the Family Wise Error. Furthermore, the method does not require experimental replicates, common regions-of-interest are indicated, a sequence-of-interest can be examined for every detected region-of-interest, and flanking genes can be reported.

Single-feature polymorphism discovery by computing probe affinity shape powers

BackgroundSingle-feature polymorphism (SFP) discovery is a rapid and cost-effective approach to identify DNA polymorphisms. However, high false positive rates and/or low sensitivity are prevalent in previously described SFP detection methods. This work presents a new computing method for SFP discovery.ResultsThe probe affinity differences and affinity shape powers formed by the neighboring probes in each probe set were computed into SFP weight scores. This method was validated by known sequence information and was comprehensively compared with previously-reported methods using the same datasets. A web application using this algorithm has been implemented for SFP detection. Using this method, we identified 364 SFPs in a barley near-isogenic line pair carrying either the wild type or the mutant uniculm2 (cul2) allele. Most of the SFP polymorphisms were identified on chromosome 6H in the vicinity of the Cul2 locus.ConclusionThis SFP discovery method exhibits better performance in specificity and sensitivity over previously-reported methods. It can be used for other organisms for which GeneChip technology is available. The web-based tool will facilitate SFP discovery. The 364 SFPs discovered in a barley near-isogenic line pair provide a set of genetic markers for fine mapping and future map-based cloning of the Cul2 locus.

Optical Measurement of Void Fraction and Bubble Size Distributions in a Microchannel

An optical measurement system was developed to investigate gas-liquid two-phase flow characteristics in a circular microchannel of 100 μ m diameter. By using multiple optical fibers and infrared photodiodes, void fraction, gas and liquid plug lengths, and their velocities were measured successfully. The probes responded to the passage of gas and liquid phases through the microchannel adequately so that the time-average void fraction could be obtained from the time fraction for each phase. Also, by cross-correlating the signals from two neighboring probes, the interface velocity representing gas plug velocity or ring-film propagation velocity depending on the flow pattern could be computed. Within the ranges of superficial gas and liquid velocities covered in the experiments (j L = 0.2∼0.4 m/s and j G = 0∼5 m/s), the gas plug length was found to increase with the increasing superficial gas velocity, but the liquid plug length was found to decrease sharply as the superficial gas velocity was increased; thus, the total length of the gas-liquid plug unit decreased with the superficial gas velocity.

A supervised hidden markov model framework for efficiently segmenting tiling array data in transcriptional and chIP-chip experiments: systematically incorporating validated biological knowledge

Large-scale tiling array experiments are becoming increasingly common in genomics. In particular, the ENCODE project requires the consistent segmentation of many different tiling array datasets into 'active regions' (e.g. finding transfrags from transcriptional data and putative binding sites from ChIP-chip experiments). Previously, such segmentation was done in an unsupervised fashion mainly based on characteristics of the signal distribution in the tiling array data itself. Here we propose a supervised framework for doing this. It has the advantage of explicitly incorporating validated biological knowledge into the model and allowing for formal training and testing. In particular, we use a hidden Markov model (HMM) framework, which is capable of explicitly modeling the dependency between neighboring probes and whose extended version (the generalized HMM) also allows explicit description of state duration density. We introduce a formal definition of the tiling-array analysis problem, and explain how we can use this to describe sampling small genomic regions for experimental validation to build up a gold-standard set for training and testing. We then describe various ideal and practical sampling strategies (e.g. maximizing signal entropy within a selected region versus using gene annotation or known promoters as positives for transcription or ChIP-chip data, respectively). For the practical sampling and training strategies, we show how the size and noise in the validated training data affects the performance of an HMM applied to the ENCODE transcriptional and ChIP-chip experiments. In particular, we show that the HMM framework is able to efficiently process tiling array data as well as or better than previous approaches. For the idealized sampling strategies, we show how we can assess their performance in a simulation framework and how a maximum entropy approach, which samples sub-regions with very different signal intensities, gives the maximally performing gold-standard. This latter result has strong implications for the optimum way medium-scale validation experiments should be carried out to verify the results of the genome-scale tiling array experiments.

Core transcriptional regulatory circuitry in human hepatocytes

We mapped the transcriptional regulatory circuitry for six master regulators in human hepatocytes using chromatin immunoprecipitation and high-resolution promoter microarrays. The results show that these regulators form a highly interconnected core circuitry, and reveal the local regulatory network motifs created by regulator–gene interactions. Autoregulation was a prominent theme among these regulators. We found that hepatocyte master regulators tend to bind promoter regions combinatorially and that the number of transcription factors bound to a promoter corresponds with observed gene expression. Our studies reveal portions of the core circuitry of human hepatocytes.

Electrostatically actuated dip pen nanolithography probe arrays

Dip pen nanolithography (DPN) is a method of creating nanoscale chemical patterns on surfaces using an atomic force microscope (AFM) probe. Until now, efforts to increase the process throughput have focused on passive multi-probe arrays and active arrays based on thermal bimetallic actuation. This paper describes the first use of electrostatic actuation to create an active DPN probe array. Electrostatic actuation offers the benefit of actuation without the probe heating required for thermal bimetallic actuation. Actuator cross talk between neighboring probes is also reduced, permitting more densely spaced probe arrays. The array presented here consists of 10 cantilever probes, where each is 120 μm long and 20 μm wide. Each cantilever probe is actuated by the electrostatic force between the probe and a built-in counter electrode with a 20–25 μm gap. The tip-to-tip probe spacing, also called the array pitch, is 30 μm. Patterns of 1-octadecanethiol were created on gold surfaces to demonstrate single-probe actuation, simultaneous multi-probe actuation, and overlap of patterns from adjacent probes. The minimum line width was 25 nm with an average line width of 30–40 nm.