QD-FRET-based biosensing of small molecule analytes using transcription factor-DNA binding

被引:0
|
作者
Thuy T Nguyen [1 ]
Chern, Margaret [2 ]
Baer, R. C. [3 ]
Fan, Andy [1 ]
Grazon, Chloe [1 ,4 ]
Galagan, James [1 ]
Dennis, Allison M. [1 ,2 ]
机构
[1] Boston Univ, Biomed Engn, Boston, MA 02215 USA
[2] Boston Univ, Mat Sci & Engn, Boston, MA 02215 USA
[3] Boston Univ, Microbiol, Boston, MA 02215 USA
[4] Univ Bordeaux, Bordeaux INP, LCPO, CNRS UMR 5629, F-33607 Pessac, France
关键词
Fluorescence resonance energy transfer (FRET); small molecule analyte; quantum dot (QD); protein; oligonucleotide; tdTomato; nanoparticle; assay; RESONANCE ENERGY-TRANSFER; QUANTUM DOTS; NANOCRYSTALS; PROTEINS; BEACONS; SURFACE;
D O I
10.1117/12.2516576
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
An alternative molecular recognition approach was developed for sensing small molecule analytes using the differential binding of an allosteric transcription factor (TF, specifically TetR) to its cognate DNA as the molecular recognition element coupled with fluorescent resonance energy transfer (FRET) to yield an internally calibrated optical signal transduction mechanism. Sensors were evaluated comprising Cy5-modified DNA (FRET acceptor) with either a tdTomato-TetR fusion protein (FP-TF) or quantum dot-TetR conjugate (QD-TF) as the FRET donor by measuring the ratio of acceptor and donor fluorescence intensities (F-A/F-D) with titrations of a derivative of the antibiotic tetracycline, anhydrous tetracycline (aTc). A proof-of-concept FRET-based biosensor was successfully demonstrated through the modulation of F-A/F-D signal intensities based on varying analyte concentrations. Sensor design parameters affecting overall signal-to-noise ratio and sensitivity of the sensors are also identified.
引用
收藏
页数:8
相关论文
共 50 条
  • [21] High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
    Jung, Christophe
    Schnepf, Max
    Bandilla, Peter
    Unnerstall, Ulrich
    Gaul, Ulrike
    JOVE-JOURNAL OF VISUALIZED EXPERIMENTS, 2019, (144):
  • [22] Thermodynamics-based modeling reveals regulatory effects of indirect transcription factor-DNA binding in Drosophila
    Bhogale, Shounak G.
    Sinha, Saurabh
    BIOPHYSICAL JOURNAL, 2022, 121 (03) : 132 - 132
  • [23] A knowledge-based orientation potential for transcription factor-DNA docking
    Takeda, Takako
    Corona, Rosario I.
    Guo, Jun-tao
    BIOINFORMATICS, 2013, 29 (03) : 322 - 330
  • [24] Cytosine arabinoside substitution decreases transcription factor-DNA binding element complex formation
    Zhang, XB
    Kiechle, FL
    ARCHIVES OF PATHOLOGY & LABORATORY MEDICINE, 2004, 128 (12) : 1364 - 1371
  • [25] Author Correction: True equilibrium measurement of transcription factor-DNA binding affinities using automated polarization microscopy
    Christophe Jung
    Peter Bandilla
    Marc von Reutern
    Max Schnepf
    Susanne Rieder
    Ulrich Unnerstall
    Ulrike Gaul
    Nature Communications, 10
  • [26] Genome-wide Inference of Transcription Factor-DNA Binding Specificity in Cell Regeneration Using a Combination Strategy
    Wang, Xiaofeng
    Zhang, Aiqun
    Ren, Weizheng
    Chen, Caiyu
    Dong, Jiahong
    CHEMICAL BIOLOGY & DRUG DESIGN, 2012, 80 (05) : 734 - 744
  • [27] Short, but matters: short tandem repeats confer variation in transcription factor-DNA binding
    Zhang, Jing
    Zhu, Bing
    SCIENCE BULLETIN, 2024, 69 (01) : 9 - 10
  • [28] BIOPHYSICAL AND STRUCTURAL ANALYSIS OF ANTENNAPEDIA AND ULTRABITHORAX HOMEODOMAIN TRANSCRIPTION FACTOR-DNA BINDING AFFINITIES
    Loss, Jeanmarie W.
    Orlomoski, Rachel J.
    Dresch, Jacqueline M.
    Drewell, Robert A.
    Spratt, Donald E.
    PROTEIN SCIENCE, 2019, 28 : 142 - 142
  • [29] A Structure-based Approach to Predicting in vitro Transcription Factor-DNA Interaction
    Gao, Zhen
    Ruan, Jianhua
    2013 IEEE INTERNATIONAL WORKSHOP ON GENOMIC SIGNAL PROCESSING AND STATISTICS (GENSIPS 2013), 2013, : 9 - 9
  • [30] An SVM-based method for assessment of transcription factor-DNA complex models
    Rosario I. Corona
    Sanjana Sudarshan
    Srinivas Aluru
    Jun-tao Guo
    BMC Bioinformatics, 19