Cooperativity of paired oligonucleotide probes for microarray hybridization assays

Bates, Steven R.; Baldwin, Donald; Channing, Alexandra; Gifford, Lida K.; Hsu, A Y; Lu, Ponzy

doi:10.1016/j.ab.2005.03.030

Cited by 10 publications

(4 citation statements)

References 16 publications

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“…The length of the oligos varies from 25 to 70 bp. Generally, long oligos have higher sensitivity, but short oligos better recognize nonspecific hybridizations (Bates et al 2005). Several international projects have developed diagnostic microarrays for plant pathogens.…”

Section: Nucleic Acid-based Methodsmentioning

confidence: 99%

Advanced methods of plant disease detection. A review

Martinelli¹,

Scalenghe²,

Davino³

et al. 2014

Agron. Sustain. Dev.

709

341

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International audiencePlant diseases are responsible for major economic losses in the agricultural industry worldwide. Monitoring plant health and detecting pathogen early are essential to reduce disease spread and facilitate effective management practices. DNA-based and serological methods now provide essential tools for accurate plant disease diagnosis, in addition to the traditional visual scouting for symptoms. Although DNA-based and serological methods have revolutionized plant disease detection, they are not very reliable at asymptomatic stage, especially in case of pathogen with systemic diffusion. They need at least 1–2 days for sample harvest, processing, and analysis. Here, we describe modern methods based on nucleic acid and protein analysis. Then, we review innovative approaches currently under development. Our main findings are the following: (1) novel sensors based on the analysis of host responses, e.g., differential mobility spectrometer and lateral flow devices, deliver instantaneous results and can effectively detect early infections directly in the field; (2) biosensors based on phage display and biophotonics can also detect instantaneously infections although they can be integrated with other systems; and (3) remote sensing techniques coupled with spectroscopy-based methods allow high spatialization of results, these techniques may be very useful as a rapid preliminary identification of primary infections. We explain how these tools will help plant disease management and complement serological and DNA-based methods. While serological and PCR-based methods are the most available and effective to confirm disease diagnosis, volatile and biophotonic sensors provide instantaneous results and may be used to identify infections at asymptomatic stages. Remote sensing technologies will be extremely helpful to greatly spatialize diagnostic results. These innovative techniques represent unprecedented tools to render agriculture more sustainable and safe, avoiding expensive use of pesticides in crop protection

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Section: Nucleic Acid-based Methodsmentioning

confidence: 99%

Advanced methods of plant disease detection. A review

Martinelli¹,

Scalenghe²,

Davino³

et al. 2014

Agron. Sustain. Dev.

709

341

View full text Add to dashboard Cite

show abstract

“…To fully decipher the difference of immune response caused by different host genetics, Petry et al conducted PRRSV artificial infection experiments in the Nebraska Index line and a commercial Hampshire × Duroc cross line, and classified the pigs into high response (HR) and low response (LR) groups according to the viremia in the early stages of infection [ 10 ]. Using an oligonucleotide microarray, Bates et al compared the gene expression in both lung and bronchial lymph nodes of pigs from HR and LR groups, and identified four differentially expressed genes (DEGs), DEAD box RNA helicase 3 (DDX3), thymosin β-4 (Tβ4), acetylcholinesterase (ACHE) and X (inactive)-specific transcript (XIST) [ 11 ]. With the same panels of samples, Wysocki et al further analyzed the involved pathways when there was tolerance to PRRSV by the long Pigoligoarray, and inferred that PRRSV infection prevented protective immune responses in HR pigs [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Transcriptome Differences in Porcine Alveolar Macrophages from Tongcheng and Large White Pigs in Response to Highly Pathogenic Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) Infection

Liang

Zhen

et al. 2017

IJMS

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Porcine reproductive and respiratory syndrome virus (PRRSV) is a single-stranded positive-sense RNA virus that can cause devastating reproductive failure and respiratory tract lesions, which has led to serious damage to the swine industry worldwide. Our previous studies have indicated that Tongcheng (TC) pigs, a Chinese local breed, have stronger resistance or tolerance to PRRSV infection than Large White (LW) pigs. This study aims to investigate their host transcriptome differences in porcine alveolar macrophages (PAMs) at 7 days post challenge. Transcriptome profiling of PAMs from PRRSV infected and control pigs of these two breeds were performed using RNA-sequencing. For both breeds, there were 1257 common differentially expressed genes (DEGs) in response to PRRSV infection, involving hepatic fibrosis/hepatic stellate cell activation, phospholipase C, and granulocyte adhesion and diapedesis pathways. For TC pig, 549 specific DEGs were identified, including VAV2, BCL2 and BAX, which were enriched in activation of leukocyte extravasation and suppression of apoptosis. While, 898 specific DEGs were identified in LW pigs, including GNAQ, GNB5, GNG2, CALM4 and RHOQ, which were involved in suppression of Gαq and PI3K-AKT signaling. This study provides an insight into the transcriptomic comparison of resistant and susceptible pigs to PRRSV infection. TC pigs may promote the extravasation and migration of leukocytes to defend against PRRSV infections and suppress apoptosis of the infected macrophages to increase antigen presentation, thereby reducing the lung lesions.

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“…We adapted the principles of cooperativity abundantly described in cell targeting (12–16) to combat the specificity–sensitivity trade-off. Cooperativity has already been shown to enhance SNP detection and assay sensitivity (17,18). Here, we combine principles of cooperativity with a label-free hairpin in what we term tentacle probes (TPs).…”

Section: Introductionmentioning

confidence: 99%

Tentacle probes: eliminating false positives without sacrificing sensitivity

Satterfield

West

Caplan

2007

Nucleic Acids Research

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The majority of efforts to increase specificity or sensitivity in biosensors result in trade-offs with little to no gain in overall accuracy. This is because a biosensor cannot be more accurate than the affinity interaction it is based on. Accordingly, we have developed a new class of reagents based on mathematical principles of cooperativity to enhance the accuracy of the affinity interaction. Tentacle probes (TPs) have a hairpin structure similar to molecular beacons (MBs) for enhanced specificity, but are modified by the addition of a capture probe for increased kinetics and affinity. They produce kinetic rate constants up to 200-fold faster than MB with corresponding stem strengths. Concentration-independent specificity was observed with no false positives at up to 1 mM concentrations of variant analyte. In contrast, MBs were concentration dependent and experienced false positives above 3.88 μM of variant analyte. The fast kinetics of this label-free reagent may prove important for extraction efficiency, hence sensitivity and detection time, in microfluidic assays. The concentration-independent specificity of TPs may prove extremely useful in assays where starting concentrations and purities are unknown as would be the case in bioterror or clinical point of care diagnostics.

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Cooperativity of paired oligonucleotide probes for microarray hybridization assays

Cited by 10 publications

References 16 publications

Advanced methods of plant disease detection. A review

Advanced methods of plant disease detection. A review

Transcriptome Differences in Porcine Alveolar Macrophages from Tongcheng and Large White Pigs in Response to Highly Pathogenic Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) Infection

Tentacle probes: eliminating false positives without sacrificing sensitivity

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